From 626dbb52b3e59625f318d974c34ec4b6e6f3e16f Mon Sep 17 00:00:00 2001
From: emileten <e.tenezakis@gmail.com>
Date: Tue, 4 Jul 2023 19:26:56 +0900
Subject: [PATCH 1/8] add notebook

---
 docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb | 883 +++++++++++++++++++
 1 file changed, 883 insertions(+)
 create mode 100644 docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb

diff --git a/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb b/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
new file mode 100644
index 00000000..635af6de
--- /dev/null
+++ b/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
@@ -0,0 +1,883 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "657e3281",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Accessing and visualizing ESA CCI version 4 data from the MAAP STAC catalog"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eac6b82d",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### What are we doing in this notebook ?\n",
+    "\n",
+    "In this tutorial, we make use of the [stackstac package](https://github.com/gjoseph92/stackstac) that allows us to turn a stack collection imported from the catalog with the [`pystac_client`](https://pystac-client.readthedocs.io/en/stable/) to an xarray dataset, and we plot the time series of the mean aboveground biomass for a selected tile of the dataset across the available temporal range.\n",
+    "\n",
+    "### Requirements\n",
+    "\n",
+    "Create a conda environment with a configuration file to which you will paste the following"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "f72403f6-7776-4ed2-b5a2-5b2c5e96770e",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "name: esa-cci\n",
+    "channels:\n",
+    "    - conda-forge\n",
+    "    - defaults\n",
+    "dependencies:\n",
+    "    - python=3.10\n",
+    "    - ipykernel\n",
+    "    - numpy\n",
+    "    - matplotlib\n",
+    "    - pip\n",
+    "    - distributed\n",
+    "    - pip:\n",
+    "        - pystac_client\n",
+    "        - stackstac\n",
+    "        - dask[diagnostics]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d2179097-2ce3-4b4e-bf32-9ed50a8fa94e",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Make the `esa-cci` environment available as a notebook kernel and choose it to run this notebook. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "bcc3b0ba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stackstac import stack, mosaic\n",
+    "import pystac_client\n",
+    "import matplotlib.pyplot as plt \n",
+    "import numpy as np\n",
+    "import pystac"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26a45833",
+   "metadata": {},
+   "source": [
+    "We first create a client to access the STAC test catalog."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e6917d57",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "URL = \"https://stac.maap-project.org\"\n",
+    "catalog = pystac_client.Client.open(URL)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df045dc1",
+   "metadata": {},
+   "source": [
+    "We create a bounding box of interest to find the tile that covers a small region around manaus, brazil (amazon rainforest)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "73868a97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "bbox = [-55,-6,-54.8,-5.8]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b7e1326-debd-4bb5-8068-c6ba89c8e140",
+   "metadata": {},
+   "source": [
+    "We proceed to an item search in the catalog using the `pystac-client`, filtering items covering our area of interest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7bb997f0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stac_collection = catalog.search(\n",
+    "    collections=[\"ESACCI_Biomass_L4_AGB_V4_100m\"],\n",
+    "    bbox=bbox\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3281f4e8-da53-4fcc-bb3f-bebb43e5143d",
+   "metadata": {},
+   "source": [
+    "Let's take a quick look at the results of the search. We may be for example interested in knowing where the data is stored."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c73362b3-cfb5-48cd-a018-58ca5feb6ef4",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/esa-cci/lib/python3.10/site-packages/pystac_client/item_search.py:841: FutureWarning: get_all_items() is deprecated, use item_collection() instead.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'s3://nasa-maap-data-store/file-staging/nasa-map/ESACCI_Biomass_L4_AGB_V4_100m_2020/N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2020-fv4.0.tif'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stac_collection.get_all_items()[0].assets['estimates'].href"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c26df930-3bd2-4bc1-9941-bc7215f3fa22",
+   "metadata": {},
+   "source": [
+    "We turn the resulting set of items (we expect one item here in the result, given the size of our bounding box) into an xarray DataArray using `stackstac`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "46bf720c-18d7-4d80-960b-998abd4a5255",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/envs/esa-cci/lib/python3.10/site-packages/stackstac/prepare.py:364: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
+      "  times = pd.to_datetime(\n"
+     ]
+    }
+   ],
+   "source": [
+    "dask_array = stack(stac_collection.get_all_items())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86a57e55-3d3a-473f-85e5-cbf6b17d198a",
+   "metadata": {},
+   "source": [
+    "We can see that our array has the following dimensions : time (we have four data points, one for each year), latitude, longitude and band (we have two bands : the AGB estimates and the AGB standard deviation)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ed6f411a-4643-40a6-b263-f2169845af6a",
+   "metadata": {},
+   "outputs": [
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+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;stackstac-c81ceb3d41889578ee425038e6d84266&#x27; (time: 4,\n",
+       "                                                                band: 2,\n",
+       "                                                                y: 11250,\n",
+       "                                                                x: 11251)&gt;\n",
+       "dask.array&lt;fetch_raster_window, shape=(4, 2, 11250, 11251), dtype=float64, chunksize=(1, 1, 1024, 1024), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates: (12/13)\n",
+       "  * time            (time) datetime64[ns] 2017-01-01 2018-01-01 ... 2020-01-01\n",
+       "    id              (time) &lt;U52 &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-20...\n",
+       "  * band            (band) &lt;U18 &#x27;estimates&#x27; &#x27;standard_deviation&#x27;\n",
+       "  * x               (x) float64 -60.0 -60.0 -60.0 -60.0 ... -50.0 -50.0 -50.0\n",
+       "  * y               (y) float64 0.0 -0.0008889 -0.001778 ... -9.998 -9.999\n",
+       "    proj:shape      object {11250}\n",
+       "    ...              ...\n",
+       "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
+       "    proj:bbox       object {0.0, -60.0, -50.0000000001, -9.9999999999}\n",
+       "    proj:epsg       int64 4326\n",
+       "    description     (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
+       "    title           (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
+       "    epsg            int64 4326\n",
+       "Attributes:\n",
+       "    spec:        RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999...\n",
+       "    crs:         epsg:4326\n",
+       "    transform:   | 0.00, 0.00,-60.00|\\n| 0.00,-0.00, 0.00|\\n| 0.00, 0.00, 1.00|\n",
+       "    resolution:  0.00088888888888</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'stackstac-c81ceb3d41889578ee425038e6d84266'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>band</span>: 2</li><li><span class='xr-has-index'>y</span>: 11250</li><li><span class='xr-has-index'>x</span>: 11251</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4d88ddc7-93c6-44f7-8701-e2be74cac6aa' class='xr-array-in' type='checkbox' checked><label for='section-4d88ddc7-93c6-44f7-8701-e2be74cac6aa' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 1024, 1024), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 7.54 GiB </td>\n",
+       "                        <td> 8.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (4, 2, 11250, 11251) </td>\n",
+       "                        <td> (1, 1, 1024, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 968 chunks in 3 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "\n",
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+       "  <text x=\"92.474299\" y=\"147.463633\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,92.474299,147.463633)\">2</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-8f8b8c28-3492-4046-b3b9-8790da2874d1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8f8b8c28-3492-4046-b3b9-8790da2874d1' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-01 ... 2020-01-01</div><input id='attrs-5044bc75-f725-401f-9c38-c0a65fbd7849' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5044bc75-f725-401f-9c38-c0a65fbd7849' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0254e96f-a35e-405c-bac3-96e1f949d64c' class='xr-var-data-in' type='checkbox'><label for='data-0254e96f-a35e-405c-bac3-96e1f949d64c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U52</div><div class='xr-var-preview xr-preview'>&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-M...</div><input id='attrs-11624b59-5b5b-4e92-9773-bf5c4c55c386' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11624b59-5b5b-4e92-9773-bf5c4c55c386' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57e15acc-792d-4fea-a25c-fc35f5a950e2' class='xr-var-data-in' type='checkbox'><label for='data-57e15acc-792d-4fea-a25c-fc35f5a950e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2017-fv4.0&#x27;,\n",
+       "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2018-fv4.0&#x27;,\n",
+       "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2019-fv4.0&#x27;,\n",
+       "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2020-fv4.0&#x27;],\n",
+       "      dtype=&#x27;&lt;U52&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;estimates&#x27; &#x27;standard_deviation&#x27;</div><input id='attrs-9fafd05a-969c-440e-bf17-d1336ce4460d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9fafd05a-969c-440e-bf17-d1336ce4460d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2f112d9-a2ea-4e4f-b5fc-d13cd096fa2d' class='xr-var-data-in' type='checkbox'><label for='data-a2f112d9-a2ea-4e4f-b5fc-d13cd096fa2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -60.0 -60.0 ... -50.0 -50.0</div><input id='attrs-73ab7f06-40cb-4881-820c-e7daf110506d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73ab7f06-40cb-4881-820c-e7daf110506d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a55e13c-6750-4f80-a7b9-e82666ae89eb' class='xr-var-data-in' type='checkbox'><label for='data-2a55e13c-6750-4f80-a7b9-e82666ae89eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-60.000889, -60.      , -59.999111, ..., -50.002667, -50.001778,\n",
+       "       -50.000889])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 -0.0008889 ... -9.998 -9.999</div><input id='attrs-4f776e23-0768-44e2-8df0-3b3b5ece4930' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f776e23-0768-44e2-8df0-3b3b5ece4930' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c9fb254-b4d6-444a-b633-884e5cc4f37e' class='xr-var-data-in' type='checkbox'><label for='data-0c9fb254-b4d6-444a-b633-884e5cc4f37e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.000000e+00, -8.888889e-04, -1.777778e-03, ..., -9.997333e+00,\n",
+       "       -9.998222e+00, -9.999111e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:shape</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{11250}</div><input id='attrs-c690d8b2-dd4b-4f1b-901f-34d917d5edfd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c690d8b2-dd4b-4f1b-901f-34d917d5edfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80682345-6c89-4ba7-b6a9-0a2ffeaccfeb' class='xr-var-data-in' type='checkbox'><label for='data-80682345-6c89-4ba7-b6a9-0a2ffeaccfeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({11250}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:geometry</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates...</div><input id='attrs-63ef0e87-667e-4007-93fe-ea9efdf77328' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63ef0e87-667e-4007-93fe-ea9efdf77328' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30c19956-7fcf-42cf-8d99-c221e5391052' class='xr-var-data-in' type='checkbox'><label for='data-30c19956-7fcf-42cf-8d99-c221e5391052' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.9999999999], [-50.0000000001, -9.9999999999], [-50.0000000001, 0.0], [-60.0, 0.0], [-60.0, -9.9999999999]]]},\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:transform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, 1.0, -0.00088888888888, -6...</div><input id='attrs-a9422ef5-fa47-4729-a121-336f15521e62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9422ef5-fa47-4729-a121-336f15521e62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f22460e-c4b2-4c39-a19a-03299e7f2324' class='xr-var-data-in' type='checkbox'><label for='data-6f22460e-c4b2-4c39-a19a-03299e7f2324' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888888}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:bbox</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, -60.0, -50.0000000001, -9....</div><input id='attrs-de55fcff-d0f7-4277-9266-1d156fbb8302' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de55fcff-d0f7-4277-9266-1d156fbb8302' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5d27b74-ee89-4e1d-83b2-de55e3ed56bd' class='xr-var-data-in' type='checkbox'><label for='data-f5d27b74-ee89-4e1d-83b2-de55e3ed56bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, -60.0, -50.0000000001, -9.9999999999}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-470f9fc6-5ccc-4220-aaa7-eb5a987e5846' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-470f9fc6-5ccc-4220-aaa7-eb5a987e5846' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfecdd8c-2146-4b2e-9384-a768a5c9b2c2' class='xr-var-data-in' type='checkbox'><label for='data-bfecdd8c-2146-4b2e-9384-a768a5c9b2c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>description</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-36d8c53f-dc0f-4ff7-b6fe-7308027914dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36d8c53f-dc0f-4ff7-b6fe-7308027914dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ea9e496-027f-4601-bfa2-a59c354a9130' class='xr-var-data-in' type='checkbox'><label for='data-9ea9e496-027f-4601-bfa2-a59c354a9130' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
+       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-4df28b57-c206-4839-8abd-99598b72b34a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4df28b57-c206-4839-8abd-99598b72b34a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d04a35bd-8c89-42d2-a59d-b6d21064195a' class='xr-var-data-in' type='checkbox'><label for='data-d04a35bd-8c89-42d2-a59d-b6d21064195a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
+       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-44cc683a-ae95-44a2-8a47-a210a0b8cb5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44cc683a-ae95-44a2-8a47-a210a0b8cb5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b5c1ee5-9c69-4c6e-95f6-e6b5b3d0c1bb' class='xr-var-data-in' type='checkbox'><label for='data-7b5c1ee5-9c69-4c6e-95f6-e6b5b3d0c1bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3a5f0dd2-fe20-4f6b-9f21-29a64d9b3442' class='xr-section-summary-in' type='checkbox'  ><label for='section-3a5f0dd2-fe20-4f6b-9f21-29a64d9b3442' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9b0b965c-a0ac-41a1-9a17-df088c700752' class='xr-index-data-in' type='checkbox'/><label for='index-9b0b965c-a0ac-41a1-9a17-df088c700752' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a4aac565-cec1-42d4-a1d5-4f8e244f5995' class='xr-index-data-in' type='checkbox'/><label for='index-a4aac565-cec1-42d4-a1d5-4f8e244f5995' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-88d0e257-327b-48ce-bff8-9468bf437fbe' class='xr-index-data-in' type='checkbox'/><label for='index-88d0e257-327b-48ce-bff8-9468bf437fbe' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -60.00088888828888, -59.999999999399996, -59.999111110511116,\n",
+       "       -59.998222221622235, -59.997333332733355, -59.996444443844474,\n",
+       "       -59.995555554955594,  -59.99466666606671,  -59.99377777717784,\n",
+       "        -59.99288888828896,\n",
+       "       ...\n",
+       "       -50.008888888388796, -50.007999999499916, -50.007111110611035,\n",
+       "       -50.006222221722155, -50.005333332833274,   -50.0044444439444,\n",
+       "        -50.00355555505552,  -50.00266666616664,  -50.00177777727776,\n",
+       "        -50.00088888838888],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=11251))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e9ec08d6-59aa-4436-b761-3a1fc4afb8ff' class='xr-index-data-in' type='checkbox'/><label for='index-e9ec08d6-59aa-4436-b761-3a1fc4afb8ff' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0,  -0.00088888888888,  -0.00177777777776,\n",
+       "        -0.00266666666664,  -0.00355555555552,   -0.0044444444444,\n",
+       "        -0.00533333333328,  -0.00622222222216,  -0.00711111111104,\n",
+       "        -0.00799999999992,\n",
+       "       ...\n",
+       "         -9.9911111110112, -9.991999999900079,  -9.99288888878896,\n",
+       "        -9.99377777767784,  -9.99466666656672,   -9.9955555554556,\n",
+       "        -9.99644444434448,  -9.99733333323336,  -9.99822222212224,\n",
+       "        -9.99911111101112],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=11250))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c238ffe1-ab78-419b-93de-740735465d43' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c238ffe1-ab78-419b-93de-740735465d43' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999999, -49.9999999995, 0.0), resolutions_xy=(0.00088888888888, 0.00088888888888))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.00, 0.00,-60.00|\n",
+       "| 0.00,-0.00, 0.00|\n",
+       "| 0.00, 0.00, 1.00|</dd><dt><span>resolution :</span></dt><dd>0.00088888888888</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'stackstac-c81ceb3d41889578ee425038e6d84266' (time: 4,\n",
+       "                                                                band: 2,\n",
+       "                                                                y: 11250,\n",
+       "                                                                x: 11251)>\n",
+       "dask.array<fetch_raster_window, shape=(4, 2, 11250, 11251), dtype=float64, chunksize=(1, 1, 1024, 1024), chunktype=numpy.ndarray>\n",
+       "Coordinates: (12/13)\n",
+       "  * time            (time) datetime64[ns] 2017-01-01 2018-01-01 ... 2020-01-01\n",
+       "    id              (time) <U52 'N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-20...\n",
+       "  * band            (band) <U18 'estimates' 'standard_deviation'\n",
+       "  * x               (x) float64 -60.0 -60.0 -60.0 -60.0 ... -50.0 -50.0 -50.0\n",
+       "  * y               (y) float64 0.0 -0.0008889 -0.001778 ... -9.998 -9.999\n",
+       "    proj:shape      object {11250}\n",
+       "    ...              ...\n",
+       "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
+       "    proj:bbox       object {0.0, -60.0, -50.0000000001, -9.9999999999}\n",
+       "    proj:epsg       int64 4326\n",
+       "    description     (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
+       "    title           (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
+       "    epsg            int64 4326\n",
+       "Attributes:\n",
+       "    spec:        RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999...\n",
+       "    crs:         epsg:4326\n",
+       "    transform:   | 0.00, 0.00,-60.00|\\n| 0.00,-0.00, 0.00|\\n| 0.00, 0.00, 1.00|\n",
+       "    resolution:  0.00088888888888"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dask_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a4e747a9-c390-4d40-9eef-e2211c2a6cb4",
+   "metadata": {},
+   "source": [
+    "We want to take a look at the time series of the average AGB in our tile. Let's compute this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b92fc0c6-4e29-4c56-ac86-9babf61691e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean_time_series = dask_array.sel(band='estimates').mean(['x','y']).compute()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a21f0588-3eba-4be0-8bbd-e5891907212a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7f5d7a2eeb30>]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mean_time_series.plot()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "esa-cci",
+   "language": "python",
+   "name": "esa-cci"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 4b54e87940cbc5c45d757a301643df3c6d0c19ec Mon Sep 17 00:00:00 2001
From: omshinde23 <rajat.shinde@uah.edu>
Date: Thu, 21 Sep 2023 14:14:55 -0700
Subject: [PATCH 2/8] Update ESA CCI notebook

---
 docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb | 992 +++++++++++++++++--
 1 file changed, 912 insertions(+), 80 deletions(-)

diff --git a/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb b/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
index 635af6de..fa424630 100644
--- a/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
+++ b/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
@@ -17,9 +17,16 @@
     "tags": []
    },
    "source": [
-    "### What are we doing in this notebook ?\n",
+    "Authors: Emile Tenezakis (DevSeed), Rajat Shinde (UAH)\n",
     "\n",
-    "In this tutorial, we make use of the [stackstac package](https://github.com/gjoseph92/stackstac) that allows us to turn a stack collection imported from the catalog with the [`pystac_client`](https://pystac-client.readthedocs.io/en/stable/) to an xarray dataset, and we plot the time series of the mean aboveground biomass for a selected tile of the dataset across the available temporal range.\n",
+    "Date: September 12, 2023\n",
+    "\n",
+    "Description: In this tutorial, we explore accessing and visualizing ESA CCI Version 4 data from the MAAP STAC catalog. We make use of the [stackstac package](https://github.com/gjoseph92/stackstac) that allows us to turn a stack collection imported from the catalog with the [`pystac_client`](https://pystac-client.readthedocs.io/en/stable/) to an xarray dataset, and we plot the time series of the mean aboveground biomass for a selected tile of the dataset across the available temporal range.\n",
+    "\n",
+    "### Run This Notebook\n",
+    "To access and run this tutorial within MAAP's Algorithm Development Environment (ADE), please refer to the [\"Getting started with the MAAP\"](https://docs.maap-project.org/en/latest/getting_started/getting_started.html) section of our documentation.\n",
+    "\n",
+    "Disclaimer: it is highly recommended to run a tutorial within MAAP's ADE, which already includes packages specific to MAAP, such as maap-py. Running the tutorial outside of the MAAP ADE may lead to errors.\n",
     "\n",
     "### Requirements\n",
     "\n",
@@ -62,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
    "id": "bcc3b0ba",
    "metadata": {},
    "outputs": [],
@@ -71,7 +78,8 @@
     "import pystac_client\n",
     "import matplotlib.pyplot as plt \n",
     "import numpy as np\n",
-    "import pystac"
+    "import pystac\n",
+    "import xarray"
    ]
   },
   {
@@ -84,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 7,
    "id": "e6917d57",
    "metadata": {},
    "outputs": [],
@@ -103,11 +111,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 8,
    "id": "73868a97",
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Filtering for the items in the collection\n",
     "bbox = [-55,-6,-54.8,-5.8]"
    ]
   },
@@ -121,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 9,
    "id": "7bb997f0",
    "metadata": {},
    "outputs": [],
@@ -142,27 +151,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 10,
    "id": "c73362b3-cfb5-48cd-a018-58ca5feb6ef4",
    "metadata": {
     "tags": []
    },
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/opt/conda/envs/esa-cci/lib/python3.10/site-packages/pystac_client/item_search.py:841: FutureWarning: get_all_items() is deprecated, use item_collection() instead.\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "'s3://nasa-maap-data-store/file-staging/nasa-map/ESACCI_Biomass_L4_AGB_V4_100m_2020/N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2020-fv4.0.tif'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -176,12 +177,12 @@
    "id": "c26df930-3bd2-4bc1-9941-bc7215f3fa22",
    "metadata": {},
    "source": [
-    "We turn the resulting set of items (we expect one item here in the result, given the size of our bounding box) into an xarray DataArray using `stackstac`"
+    "We turn the resulting set of items (we expect one item here in the result, given the size of our bounding box) into an xarray DataArray using `stackstac`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "id": "46bf720c-18d7-4d80-960b-998abd4a5255",
    "metadata": {},
    "outputs": [
@@ -189,13 +190,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/opt/conda/envs/esa-cci/lib/python3.10/site-packages/stackstac/prepare.py:364: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
+      "/opt/conda/lib/python3.10/site-packages/stackstac/prepare.py:408: UserWarning: The argument 'infer_datetime_format' is deprecated and will be removed in a future version. A strict version of it is now the default, see https://pandas.pydata.org/pdeps/0004-consistent-to-datetime-parsing.html. You can safely remove this argument.\n",
       "  times = pd.to_datetime(\n"
      ]
     }
    ],
    "source": [
-    "dask_array = stack(stac_collection.get_all_items())"
+    "#Change variable name\n",
+    "arr = stack(stac_collection.get_all_items())"
    ]
   },
   {
@@ -208,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "id": "ed6f411a-4643-40a6-b263-f2169845af6a",
    "metadata": {},
    "outputs": [
@@ -578,7 +580,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;stackstac-c81ceb3d41889578ee425038e6d84266&#x27; (time: 4,\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;stackstac-0d88a6aa99e2eb93c2b68b6e8780c93c&#x27; (time: 4,\n",
        "                                                                band: 2,\n",
        "                                                                y: 11250,\n",
        "                                                                x: 11251)&gt;\n",
@@ -589,19 +591,19 @@
        "  * band            (band) &lt;U18 &#x27;estimates&#x27; &#x27;standard_deviation&#x27;\n",
        "  * x               (x) float64 -60.0 -60.0 -60.0 -60.0 ... -50.0 -50.0 -50.0\n",
        "  * y               (y) float64 0.0 -0.0008889 -0.001778 ... -9.998 -9.999\n",
-       "    proj:shape      object {11250}\n",
-       "    ...              ...\n",
        "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
+       "    ...              ...\n",
+       "    proj:shape      object {11250}\n",
        "    proj:bbox       object {0.0, -60.0, -50.0000000001, -9.9999999999}\n",
        "    proj:epsg       int64 4326\n",
-       "    description     (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
        "    title           (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
+       "    description     (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
        "    epsg            int64 4326\n",
        "Attributes:\n",
        "    spec:        RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999...\n",
        "    crs:         epsg:4326\n",
        "    transform:   | 0.00, 0.00,-60.00|\\n| 0.00,-0.00, 0.00|\\n| 0.00, 0.00, 1.00|\n",
-       "    resolution:  0.00088888888888</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'stackstac-c81ceb3d41889578ee425038e6d84266'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>band</span>: 2</li><li><span class='xr-has-index'>y</span>: 11250</li><li><span class='xr-has-index'>x</span>: 11251</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4d88ddc7-93c6-44f7-8701-e2be74cac6aa' class='xr-array-in' type='checkbox' checked><label for='section-4d88ddc7-93c6-44f7-8701-e2be74cac6aa' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 1024, 1024), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    resolution:  0.00088888888888</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'stackstac-0d88a6aa99e2eb93c2b68b6e8780c93c'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>band</span>: 2</li><li><span class='xr-has-index'>y</span>: 11250</li><li><span class='xr-has-index'>x</span>: 11251</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5afbc5ab-29db-44c1-9aad-0abd93db3099' class='xr-array-in' type='checkbox' checked><label for='section-5afbc5ab-29db-44c1-9aad-0abd93db3099' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 1024, 1024), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -740,18 +742,18 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-8f8b8c28-3492-4046-b3b9-8790da2874d1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8f8b8c28-3492-4046-b3b9-8790da2874d1' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-01 ... 2020-01-01</div><input id='attrs-5044bc75-f725-401f-9c38-c0a65fbd7849' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5044bc75-f725-401f-9c38-c0a65fbd7849' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0254e96f-a35e-405c-bac3-96e1f949d64c' class='xr-var-data-in' type='checkbox'><label for='data-0254e96f-a35e-405c-bac3-96e1f949d64c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-68941179-f4f7-464d-8150-894a3648980d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-68941179-f4f7-464d-8150-894a3648980d' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-01 ... 2020-01-01</div><input id='attrs-70a5a18b-3a79-4316-abd9-6e9591aca463' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70a5a18b-3a79-4316-abd9-6e9591aca463' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6d35674-1cf1-407d-aff7-2e73b93b40c4' class='xr-var-data-in' type='checkbox'><label for='data-e6d35674-1cf1-407d-aff7-2e73b93b40c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U52</div><div class='xr-var-preview xr-preview'>&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-M...</div><input id='attrs-11624b59-5b5b-4e92-9773-bf5c4c55c386' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-11624b59-5b5b-4e92-9773-bf5c4c55c386' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57e15acc-792d-4fea-a25c-fc35f5a950e2' class='xr-var-data-in' type='checkbox'><label for='data-57e15acc-792d-4fea-a25c-fc35f5a950e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2017-fv4.0&#x27;,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U52</div><div class='xr-var-preview xr-preview'>&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-M...</div><input id='attrs-e70233fc-9336-4f3b-a0a6-2833ce771329' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e70233fc-9336-4f3b-a0a6-2833ce771329' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c8dc5ab-36d0-4b9f-8cf0-331e87d71cd0' class='xr-var-data-in' type='checkbox'><label for='data-3c8dc5ab-36d0-4b9f-8cf0-331e87d71cd0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2017-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2018-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2019-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2020-fv4.0&#x27;],\n",
-       "      dtype=&#x27;&lt;U52&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;estimates&#x27; &#x27;standard_deviation&#x27;</div><input id='attrs-9fafd05a-969c-440e-bf17-d1336ce4460d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9fafd05a-969c-440e-bf17-d1336ce4460d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2f112d9-a2ea-4e4f-b5fc-d13cd096fa2d' class='xr-var-data-in' type='checkbox'><label for='data-a2f112d9-a2ea-4e4f-b5fc-d13cd096fa2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -60.0 -60.0 ... -50.0 -50.0</div><input id='attrs-73ab7f06-40cb-4881-820c-e7daf110506d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73ab7f06-40cb-4881-820c-e7daf110506d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a55e13c-6750-4f80-a7b9-e82666ae89eb' class='xr-var-data-in' type='checkbox'><label for='data-2a55e13c-6750-4f80-a7b9-e82666ae89eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-60.000889, -60.      , -59.999111, ..., -50.002667, -50.001778,\n",
-       "       -50.000889])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 -0.0008889 ... -9.998 -9.999</div><input id='attrs-4f776e23-0768-44e2-8df0-3b3b5ece4930' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f776e23-0768-44e2-8df0-3b3b5ece4930' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c9fb254-b4d6-444a-b633-884e5cc4f37e' class='xr-var-data-in' type='checkbox'><label for='data-0c9fb254-b4d6-444a-b633-884e5cc4f37e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.000000e+00, -8.888889e-04, -1.777778e-03, ..., -9.997333e+00,\n",
-       "       -9.998222e+00, -9.999111e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:shape</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{11250}</div><input id='attrs-c690d8b2-dd4b-4f1b-901f-34d917d5edfd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c690d8b2-dd4b-4f1b-901f-34d917d5edfd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80682345-6c89-4ba7-b6a9-0a2ffeaccfeb' class='xr-var-data-in' type='checkbox'><label for='data-80682345-6c89-4ba7-b6a9-0a2ffeaccfeb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({11250}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:geometry</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates...</div><input id='attrs-63ef0e87-667e-4007-93fe-ea9efdf77328' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63ef0e87-667e-4007-93fe-ea9efdf77328' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30c19956-7fcf-42cf-8d99-c221e5391052' class='xr-var-data-in' type='checkbox'><label for='data-30c19956-7fcf-42cf-8d99-c221e5391052' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.9999999999], [-50.0000000001, -9.9999999999], [-50.0000000001, 0.0], [-60.0, 0.0], [-60.0, -9.9999999999]]]},\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:transform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, 1.0, -0.00088888888888, -6...</div><input id='attrs-a9422ef5-fa47-4729-a121-336f15521e62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a9422ef5-fa47-4729-a121-336f15521e62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f22460e-c4b2-4c39-a19a-03299e7f2324' class='xr-var-data-in' type='checkbox'><label for='data-6f22460e-c4b2-4c39-a19a-03299e7f2324' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888888}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:bbox</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, -60.0, -50.0000000001, -9....</div><input id='attrs-de55fcff-d0f7-4277-9266-1d156fbb8302' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-de55fcff-d0f7-4277-9266-1d156fbb8302' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5d27b74-ee89-4e1d-83b2-de55e3ed56bd' class='xr-var-data-in' type='checkbox'><label for='data-f5d27b74-ee89-4e1d-83b2-de55e3ed56bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, -60.0, -50.0000000001, -9.9999999999}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-470f9fc6-5ccc-4220-aaa7-eb5a987e5846' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-470f9fc6-5ccc-4220-aaa7-eb5a987e5846' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfecdd8c-2146-4b2e-9384-a768a5c9b2c2' class='xr-var-data-in' type='checkbox'><label for='data-bfecdd8c-2146-4b2e-9384-a768a5c9b2c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>description</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-36d8c53f-dc0f-4ff7-b6fe-7308027914dd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36d8c53f-dc0f-4ff7-b6fe-7308027914dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ea9e496-027f-4601-bfa2-a59c354a9130' class='xr-var-data-in' type='checkbox'><label for='data-9ea9e496-027f-4601-bfa2-a59c354a9130' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
-       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-4df28b57-c206-4839-8abd-99598b72b34a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4df28b57-c206-4839-8abd-99598b72b34a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d04a35bd-8c89-42d2-a59d-b6d21064195a' class='xr-var-data-in' type='checkbox'><label for='data-d04a35bd-8c89-42d2-a59d-b6d21064195a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
-       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-44cc683a-ae95-44a2-8a47-a210a0b8cb5a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44cc683a-ae95-44a2-8a47-a210a0b8cb5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b5c1ee5-9c69-4c6e-95f6-e6b5b3d0c1bb' class='xr-var-data-in' type='checkbox'><label for='data-7b5c1ee5-9c69-4c6e-95f6-e6b5b3d0c1bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3a5f0dd2-fe20-4f6b-9f21-29a64d9b3442' class='xr-section-summary-in' type='checkbox'  ><label for='section-3a5f0dd2-fe20-4f6b-9f21-29a64d9b3442' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9b0b965c-a0ac-41a1-9a17-df088c700752' class='xr-index-data-in' type='checkbox'/><label for='index-9b0b965c-a0ac-41a1-9a17-df088c700752' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a4aac565-cec1-42d4-a1d5-4f8e244f5995' class='xr-index-data-in' type='checkbox'/><label for='index-a4aac565-cec1-42d4-a1d5-4f8e244f5995' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-88d0e257-327b-48ce-bff8-9468bf437fbe' class='xr-index-data-in' type='checkbox'/><label for='index-88d0e257-327b-48ce-bff8-9468bf437fbe' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -60.00088888828888, -59.999999999399996, -59.999111110511116,\n",
+       "      dtype=&#x27;&lt;U52&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;estimates&#x27; &#x27;standard_deviation&#x27;</div><input id='attrs-e25dae1c-b198-436e-92b8-05cf8f094838' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e25dae1c-b198-436e-92b8-05cf8f094838' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d51d5f56-893d-40ff-a44d-1646576fbfdc' class='xr-var-data-in' type='checkbox'><label for='data-d51d5f56-893d-40ff-a44d-1646576fbfdc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -60.0 -60.0 ... -50.0 -50.0</div><input id='attrs-b8206be8-f64c-41da-a743-4804603575ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8206be8-f64c-41da-a743-4804603575ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9168accf-ccd9-49ff-b661-807282e62f3a' class='xr-var-data-in' type='checkbox'><label for='data-9168accf-ccd9-49ff-b661-807282e62f3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-60.000889, -60.      , -59.999111, ..., -50.002667, -50.001778,\n",
+       "       -50.000889])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 -0.0008889 ... -9.998 -9.999</div><input id='attrs-1959eba2-668c-4859-89f3-6a2f6ec0588e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1959eba2-668c-4859-89f3-6a2f6ec0588e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ac215a0-ec1a-4bd0-8ff3-f8799a33958a' class='xr-var-data-in' type='checkbox'><label for='data-1ac215a0-ec1a-4bd0-8ff3-f8799a33958a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.000000e+00, -8.888889e-04, -1.777778e-03, ..., -9.997333e+00,\n",
+       "       -9.998222e+00, -9.999111e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:transform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, 1.0, -0.00088888888888, -6...</div><input id='attrs-d7f8c229-d444-44f3-b4f9-a0a274d2aa85' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7f8c229-d444-44f3-b4f9-a0a274d2aa85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f16c7d44-6496-4996-a958-abaa902f3020' class='xr-var-data-in' type='checkbox'><label for='data-f16c7d44-6496-4996-a958-abaa902f3020' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888888}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:geometry</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates...</div><input id='attrs-a7ef44c6-2aec-4018-bda3-70bce146ca53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7ef44c6-2aec-4018-bda3-70bce146ca53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcc6e0c2-fe12-4100-925e-8b9a57b819a8' class='xr-var-data-in' type='checkbox'><label for='data-bcc6e0c2-fe12-4100-925e-8b9a57b819a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.9999999999], [-50.0000000001, -9.9999999999], [-50.0000000001, 0.0], [-60.0, 0.0], [-60.0, -9.9999999999]]]},\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:shape</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{11250}</div><input id='attrs-612d53a5-94ae-4e20-836d-acbe26df8777' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-612d53a5-94ae-4e20-836d-acbe26df8777' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0de61d4-5057-4d47-a393-e4d00fc7a292' class='xr-var-data-in' type='checkbox'><label for='data-e0de61d4-5057-4d47-a393-e4d00fc7a292' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({11250}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:bbox</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, -60.0, -50.0000000001, -9....</div><input id='attrs-52cf1f2c-ee08-4d5f-8c5b-e6229bfba1a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-52cf1f2c-ee08-4d5f-8c5b-e6229bfba1a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-647086be-7e5d-408a-87b9-bbbdf8e3884a' class='xr-var-data-in' type='checkbox'><label for='data-647086be-7e5d-408a-87b9-bbbdf8e3884a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, -60.0, -50.0000000001, -9.9999999999}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-06fad089-d3c0-480f-bc71-00ffcca55ed2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06fad089-d3c0-480f-bc71-00ffcca55ed2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcdb82db-1cbd-4415-a7db-3c5ab8da9113' class='xr-var-data-in' type='checkbox'><label for='data-bcdb82db-1cbd-4415-a7db-3c5ab8da9113' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-03ff452c-51bc-499d-a221-90ff4b075bd1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-03ff452c-51bc-499d-a221-90ff4b075bd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8657e9c9-ae92-4ea9-848f-666713d717fa' class='xr-var-data-in' type='checkbox'><label for='data-8657e9c9-ae92-4ea9-848f-666713d717fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
+       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>description</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-5f5cb2a1-9268-42d4-a510-f2ea15e8355f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f5cb2a1-9268-42d4-a510-f2ea15e8355f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8261839-8254-4ea5-988f-390d7dc5f164' class='xr-var-data-in' type='checkbox'><label for='data-d8261839-8254-4ea5-988f-390d7dc5f164' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
+       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-67b7f004-0a3f-490f-8042-9648d367086f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67b7f004-0a3f-490f-8042-9648d367086f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-266a3a95-aefe-4feb-845f-9be66bc209c7' class='xr-var-data-in' type='checkbox'><label for='data-266a3a95-aefe-4feb-845f-9be66bc209c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-68ca9d35-27ec-42bd-8690-dc697da71452' class='xr-section-summary-in' type='checkbox'  ><label for='section-68ca9d35-27ec-42bd-8690-dc697da71452' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3d3767ec-d5f9-4f21-ada8-4788aa77add0' class='xr-index-data-in' type='checkbox'/><label for='index-3d3767ec-d5f9-4f21-ada8-4788aa77add0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4de854d4-78c4-48e4-ace7-f13c695d2cf4' class='xr-index-data-in' type='checkbox'/><label for='index-4de854d4-78c4-48e4-ace7-f13c695d2cf4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-25ad9d76-4bb5-4378-bc25-14ae564c1151' class='xr-index-data-in' type='checkbox'/><label for='index-25ad9d76-4bb5-4378-bc25-14ae564c1151' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -60.00088888828888, -59.999999999399996, -59.999111110511116,\n",
        "       -59.998222221622235, -59.997333332733355, -59.996444443844474,\n",
        "       -59.995555554955594,  -59.99466666606671,  -59.99377777717784,\n",
        "        -59.99288888828896,\n",
@@ -760,7 +762,7 @@
        "       -50.006222221722155, -50.005333332833274,   -50.0044444439444,\n",
        "        -50.00355555505552,  -50.00266666616664,  -50.00177777727776,\n",
        "        -50.00088888838888],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=11251))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e9ec08d6-59aa-4436-b761-3a1fc4afb8ff' class='xr-index-data-in' type='checkbox'/><label for='index-e9ec08d6-59aa-4436-b761-3a1fc4afb8ff' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0,  -0.00088888888888,  -0.00177777777776,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=11251))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3a769702-4546-434a-b256-c69767e91060' class='xr-index-data-in' type='checkbox'/><label for='index-3a769702-4546-434a-b256-c69767e91060' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0,  -0.00088888888888,  -0.00177777777776,\n",
        "        -0.00266666666664,  -0.00355555555552,   -0.0044444444444,\n",
        "        -0.00533333333328,  -0.00622222222216,  -0.00711111111104,\n",
        "        -0.00799999999992,\n",
@@ -769,12 +771,12 @@
        "        -9.99377777767784,  -9.99466666656672,   -9.9955555554556,\n",
        "        -9.99644444434448,  -9.99733333323336,  -9.99822222212224,\n",
        "        -9.99911111101112],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=11250))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c238ffe1-ab78-419b-93de-740735465d43' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c238ffe1-ab78-419b-93de-740735465d43' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999999, -49.9999999995, 0.0), resolutions_xy=(0.00088888888888, 0.00088888888888))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.00, 0.00,-60.00|\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=11250))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1115830d-193c-4bfa-8d28-0893bd9f561c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1115830d-193c-4bfa-8d28-0893bd9f561c' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999999, -49.9999999995, 0.0), resolutions_xy=(0.00088888888888, 0.00088888888888))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.00, 0.00,-60.00|\n",
        "| 0.00,-0.00, 0.00|\n",
        "| 0.00, 0.00, 1.00|</dd><dt><span>resolution :</span></dt><dd>0.00088888888888</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'stackstac-c81ceb3d41889578ee425038e6d84266' (time: 4,\n",
+       "<xarray.DataArray 'stackstac-0d88a6aa99e2eb93c2b68b6e8780c93c' (time: 4,\n",
        "                                                                band: 2,\n",
        "                                                                y: 11250,\n",
        "                                                                x: 11251)>\n",
@@ -785,13 +787,13 @@
        "  * band            (band) <U18 'estimates' 'standard_deviation'\n",
        "  * x               (x) float64 -60.0 -60.0 -60.0 -60.0 ... -50.0 -50.0 -50.0\n",
        "  * y               (y) float64 0.0 -0.0008889 -0.001778 ... -9.998 -9.999\n",
-       "    proj:shape      object {11250}\n",
-       "    ...              ...\n",
        "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
+       "    ...              ...\n",
+       "    proj:shape      object {11250}\n",
        "    proj:bbox       object {0.0, -60.0, -50.0000000001, -9.9999999999}\n",
        "    proj:epsg       int64 4326\n",
-       "    description     (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
        "    title           (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
+       "    description     (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
        "    epsg            int64 4326\n",
        "Attributes:\n",
        "    spec:        RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999...\n",
@@ -800,13 +802,13 @@
        "    resolution:  0.00088888888888"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "dask_array"
+    "arr"
    ]
   },
   {
@@ -814,56 +816,886 @@
    "id": "a4e747a9-c390-4d40-9eef-e2211c2a6cb4",
    "metadata": {},
    "source": [
-    "We want to take a look at the time series of the average AGB in our tile. Let's compute this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "b92fc0c6-4e29-4c56-ac86-9babf61691e1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean_time_series = dask_array.sel(band='estimates').mean(['x','y']).compute()"
+    "Now, we will perform a spatial filtering operation and select a subset of the created stack as it is compute intensive to run experiments on the entire stack due to its size. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "a21f0588-3eba-4be0-8bbd-e5891907212a",
-   "metadata": {},
+   "execution_count": 16,
+   "id": "e9d173bd-fbfc-4f5c-b0bb-d948b49259ce",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f5d7a2eeb30>]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mean_time_series.plot()"
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+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;stackstac-0d88a6aa99e2eb93c2b68b6e8780c93c&#x27; (time: 4,\n",
+       "                                                                band: 2,\n",
+       "                                                                y: 2250, x: 5625)&gt;\n",
+       "dask.array&lt;getitem, shape=(4, 2, 2250, 5625), dtype=float64, chunksize=(1, 1, 1024, 1024), chunktype=numpy.ndarray&gt;\n",
+       "Coordinates: (12/13)\n",
+       "  * time            (time) datetime64[ns] 2017-01-01 2018-01-01 ... 2020-01-01\n",
+       "    id              (time) &lt;U52 &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-20...\n",
+       "  * band            (band) &lt;U18 &#x27;estimates&#x27; &#x27;standard_deviation&#x27;\n",
+       "  * x               (x) float64 -55.0 -55.0 -55.0 -55.0 ... -50.0 -50.0 -50.0\n",
+       "  * y               (y) float64 -3.001 -3.002 -3.003 ... -4.998 -4.999 -5.0\n",
+       "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
+       "    ...              ...\n",
+       "    proj:shape      object {11250}\n",
+       "    proj:bbox       object {0.0, -60.0, -50.0000000001, -9.9999999999}\n",
+       "    proj:epsg       int64 4326\n",
+       "    title           (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
+       "    description     (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
+       "    epsg            int64 4326\n",
+       "Attributes:\n",
+       "    spec:        RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999...\n",
+       "    crs:         epsg:4326\n",
+       "    transform:   | 0.00, 0.00,-60.00|\\n| 0.00,-0.00, 0.00|\\n| 0.00, 0.00, 1.00|\n",
+       "    resolution:  0.00088888888888</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'stackstac-0d88a6aa99e2eb93c2b68b6e8780c93c'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>band</span>: 2</li><li><span class='xr-has-index'>y</span>: 2250</li><li><span class='xr-has-index'>x</span>: 5625</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5dcb3ec3-3dee-4721-9486-389c93ff0732' class='xr-array-in' type='checkbox' checked><label for='section-5dcb3ec3-3dee-4721-9486-389c93ff0732' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 720, 518), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 772.48 MiB </td>\n",
+       "                        <td> 8.00 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (4, 2, 2250, 5625) </td>\n",
+       "                        <td> (1, 1, 1024, 1024) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 144 chunks in 4 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"374\" height=\"112\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-d83d7e01-ace9-41c3-8ba9-f641b6ccb86a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d83d7e01-ace9-41c3-8ba9-f641b6ccb86a' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-01 ... 2020-01-01</div><input id='attrs-ca39f9dc-c13e-4e26-8247-6c88b2408e96' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca39f9dc-c13e-4e26-8247-6c88b2408e96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8988ae1a-ba3a-4f47-b0cc-d0c4a6ff630f' class='xr-var-data-in' type='checkbox'><label for='data-8988ae1a-ba3a-4f47-b0cc-d0c4a6ff630f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
+       "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U52</div><div class='xr-var-preview xr-preview'>&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-M...</div><input id='attrs-fdd4a64b-0af1-4c8b-a03b-b57462fa9ede' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdd4a64b-0af1-4c8b-a03b-b57462fa9ede' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d09a8525-5a10-468d-ab91-d4703c7fd80e' class='xr-var-data-in' type='checkbox'><label for='data-d09a8525-5a10-468d-ab91-d4703c7fd80e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2017-fv4.0&#x27;,\n",
+       "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2018-fv4.0&#x27;,\n",
+       "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2019-fv4.0&#x27;,\n",
+       "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2020-fv4.0&#x27;],\n",
+       "      dtype=&#x27;&lt;U52&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;estimates&#x27; &#x27;standard_deviation&#x27;</div><input id='attrs-cdfcfdd9-c91c-4481-90aa-85eb3c49b076' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cdfcfdd9-c91c-4481-90aa-85eb3c49b076' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01423b6a-0d13-4b18-8de8-77a6ac13c374' class='xr-var-data-in' type='checkbox'><label for='data-01423b6a-0d13-4b18-8de8-77a6ac13c374' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-55.0 -55.0 -55.0 ... -50.0 -50.0</div><input id='attrs-ee85812c-8dc3-4bf8-a75e-7167f6b02ce3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee85812c-8dc3-4bf8-a75e-7167f6b02ce3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90ba73ab-b5df-4898-8453-c3a865a39df2' class='xr-var-data-in' type='checkbox'><label for='data-90ba73ab-b5df-4898-8453-c3a865a39df2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-55.      , -54.999111, -54.998222, ..., -50.002667, -50.001778,\n",
+       "       -50.000889])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.001 -3.002 ... -4.999 -5.0</div><input id='attrs-6b8c838c-8073-4c7b-a00c-86cab51f145c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b8c838c-8073-4c7b-a00c-86cab51f145c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ae0f6ae-9c37-4c55-b5f2-ad3b7c59068f' class='xr-var-data-in' type='checkbox'><label for='data-6ae0f6ae-9c37-4c55-b5f2-ad3b7c59068f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-3.000889, -3.001778, -3.002667, ..., -4.998222, -4.999111, -5.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:transform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, 1.0, -0.00088888888888, -6...</div><input id='attrs-78129de4-ec15-47eb-a51d-a6e6ced5d85c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78129de4-ec15-47eb-a51d-a6e6ced5d85c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ea19ff5-1b38-4705-bb1d-0f58b56dc4a5' class='xr-var-data-in' type='checkbox'><label for='data-2ea19ff5-1b38-4705-bb1d-0f58b56dc4a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888888}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:geometry</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates...</div><input id='attrs-f4d0e9e7-5327-48a5-913b-5f72321bd757' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4d0e9e7-5327-48a5-913b-5f72321bd757' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77238c93-efef-407f-bb12-60736f2d8a1b' class='xr-var-data-in' type='checkbox'><label for='data-77238c93-efef-407f-bb12-60736f2d8a1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.9999999999], [-50.0000000001, -9.9999999999], [-50.0000000001, 0.0], [-60.0, 0.0], [-60.0, -9.9999999999]]]},\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:shape</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{11250}</div><input id='attrs-80ff2960-920d-4c6d-8995-b43191024b5e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-80ff2960-920d-4c6d-8995-b43191024b5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-109e60ca-0ab5-499a-b4f2-02d326306fd9' class='xr-var-data-in' type='checkbox'><label for='data-109e60ca-0ab5-499a-b4f2-02d326306fd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({11250}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:bbox</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, -60.0, -50.0000000001, -9....</div><input id='attrs-07d24a84-8bb5-451c-b91c-67bcfa0ebeed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07d24a84-8bb5-451c-b91c-67bcfa0ebeed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-544bdd02-f9bb-46d9-b7ca-50b3f5c80f01' class='xr-var-data-in' type='checkbox'><label for='data-544bdd02-f9bb-46d9-b7ca-50b3f5c80f01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, -60.0, -50.0000000001, -9.9999999999}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-d7d2dfaf-b53b-4720-a16a-3209c112f41a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7d2dfaf-b53b-4720-a16a-3209c112f41a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae52eae8-a186-40b3-89af-f4d159ba9dee' class='xr-var-data-in' type='checkbox'><label for='data-ae52eae8-a186-40b3-89af-f4d159ba9dee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-93315d07-a5b5-4e18-afcb-44f7a15fd347' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93315d07-a5b5-4e18-afcb-44f7a15fd347' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26c2feb9-154e-4f2f-88e5-5b7a6cda5c9b' class='xr-var-data-in' type='checkbox'><label for='data-26c2feb9-154e-4f2f-88e5-5b7a6cda5c9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
+       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>description</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-2d929175-3be8-4d7d-ba3c-046d820caa39' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d929175-3be8-4d7d-ba3c-046d820caa39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64f48bea-270a-4262-bf3e-dbcbc46e91e3' class='xr-var-data-in' type='checkbox'><label for='data-64f48bea-270a-4262-bf3e-dbcbc46e91e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
+       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-8a17978e-d6da-44ed-8777-5aeaeedffe70' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8a17978e-d6da-44ed-8777-5aeaeedffe70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aecc405e-ef57-4d63-9d1d-aea9aa3f9d7a' class='xr-var-data-in' type='checkbox'><label for='data-aecc405e-ef57-4d63-9d1d-aea9aa3f9d7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9d766367-7e7a-4ef0-81e3-1069fa02f101' class='xr-section-summary-in' type='checkbox'  ><label for='section-9d766367-7e7a-4ef0-81e3-1069fa02f101' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8e3687e2-0be8-4531-864c-4b3dbbfb8969' class='xr-index-data-in' type='checkbox'/><label for='index-8e3687e2-0be8-4531-864c-4b3dbbfb8969' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4adcc4c4-668a-4a93-88e4-69b03ef6fa7a' class='xr-index-data-in' type='checkbox'/><label for='index-4adcc4c4-668a-4a93-88e4-69b03ef6fa7a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-00728d1b-9712-48c7-a95e-8e9e396a391f' class='xr-index-data-in' type='checkbox'/><label for='index-00728d1b-9712-48c7-a95e-8e9e396a391f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    -54.99999999945,  -54.99911111056112, -54.998222221672236,\n",
+       "       -54.997333332783356, -54.996444443894475, -54.995555555005595,\n",
+       "       -54.994666666116714, -54.993777777227834,  -54.99288888833896,\n",
+       "        -54.99199999945008,\n",
+       "       ...\n",
+       "       -50.008888888388796, -50.007999999499916, -50.007111110611035,\n",
+       "       -50.006222221722155, -50.005333332833274,   -50.0044444439444,\n",
+       "        -50.00355555505552,  -50.00266666616664,  -50.00177777727776,\n",
+       "        -50.00088888838888],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=5625))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8a185561-90df-4272-9c79-5c0a72246b0c' class='xr-index-data-in' type='checkbox'/><label for='index-8a185561-90df-4272-9c79-5c0a72246b0c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  -3.00088888885888,   -3.00177777774776,   -3.00266666663664,\n",
+       "         -3.00355555552552,    -3.0044444444144,   -3.00533333330328,\n",
+       "         -3.00622222219216,   -3.00711111108104,   -3.00799999996992,\n",
+       "          -3.0088888888588,\n",
+       "       ...\n",
+       "         -4.99199999995008, -4.9928888888389595,   -4.99377777772784,\n",
+       "         -4.99466666661672,    -4.9955555555056,   -4.99644444439448,\n",
+       "         -4.99733333328336,   -4.99822222217224,   -4.99911111106112,\n",
+       "            -4.99999999995],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=2250))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cec76e9c-290a-490c-9119-11b79d575c40' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cec76e9c-290a-490c-9119-11b79d575c40' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999999, -49.9999999995, 0.0), resolutions_xy=(0.00088888888888, 0.00088888888888))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.00, 0.00,-60.00|\n",
+       "| 0.00,-0.00, 0.00|\n",
+       "| 0.00, 0.00, 1.00|</dd><dt><span>resolution :</span></dt><dd>0.00088888888888</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray 'stackstac-0d88a6aa99e2eb93c2b68b6e8780c93c' (time: 4,\n",
+       "                                                                band: 2,\n",
+       "                                                                y: 2250, x: 5625)>\n",
+       "dask.array<getitem, shape=(4, 2, 2250, 5625), dtype=float64, chunksize=(1, 1, 1024, 1024), chunktype=numpy.ndarray>\n",
+       "Coordinates: (12/13)\n",
+       "  * time            (time) datetime64[ns] 2017-01-01 2018-01-01 ... 2020-01-01\n",
+       "    id              (time) <U52 'N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-20...\n",
+       "  * band            (band) <U18 'estimates' 'standard_deviation'\n",
+       "  * x               (x) float64 -55.0 -55.0 -55.0 -55.0 ... -50.0 -50.0 -50.0\n",
+       "  * y               (y) float64 -3.001 -3.002 -3.003 ... -4.998 -4.999 -5.0\n",
+       "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
+       "    ...              ...\n",
+       "    proj:shape      object {11250}\n",
+       "    proj:bbox       object {0.0, -60.0, -50.0000000001, -9.9999999999}\n",
+       "    proj:epsg       int64 4326\n",
+       "    title           (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
+       "    description     (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
+       "    epsg            int64 4326\n",
+       "Attributes:\n",
+       "    spec:        RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999...\n",
+       "    crs:         epsg:4326\n",
+       "    transform:   | 0.00, 0.00,-60.00|\\n| 0.00,-0.00, 0.00|\\n| 0.00, 0.00, 1.00|\n",
+       "    resolution:  0.00088888888888"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min_x = -55\n",
+    "min_y = -3.0\n",
+    "max_x = -50.0 \n",
+    "max_y = -5.0\n",
+    "\n",
+    "arr_sub = arr.sel(x=slice(min_x, max_x), y=slice(min_y, max_y))\n",
+    "arr_sub"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1124341-5f4d-4fe7-80f9-381ed11e0ede",
+   "metadata": {},
+   "source": [
+    "### Masking values based on a condition\n",
+    "\n",
+    "Below, we will explore how to mask few values based on a condition and plot the time series for the filtered array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "1b9c7539-793e-41b6-86fe-2d9e9abb6d79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr_est = arr_sub.sel(band='estimates')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8eecc263-f593-4aad-b724-b93099b56df0",
+   "metadata": {},
+   "source": [
+    "Let's try to print the values of the `arr_est`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "c60c3de4-8cd3-46cf-8b2a-99e103250575",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[274., 215., 295., ..., 221., 181., 143.],\n",
+       "        [248., 285., 284., ..., 165., 190., 165.],\n",
+       "        [244., 224., 274., ..., 188., 182., 162.],\n",
+       "        ...,\n",
+       "        [351., 382., 376., ...,  22.,  41.,  41.],\n",
+       "        [340., 343., 358., ...,   0.,   0.,  50.],\n",
+       "        [289., 320., 334., ...,   0.,   0.,  51.]],\n",
+       "\n",
+       "       [[219., 184., 278., ..., 219., 237., 214.],\n",
+       "        [213., 227., 275., ..., 188., 236., 239.],\n",
+       "        [241., 233., 313., ..., 205., 210., 197.],\n",
+       "        ...,\n",
+       "        [379., 379., 379., ...,  41.,  68.,  34.],\n",
+       "        [333., 368., 371., ...,   0.,   0.,  39.],\n",
+       "        [329., 365., 379., ...,   0.,   0.,  38.]],\n",
+       "\n",
+       "       [[325., 182., 273., ..., 257., 262., 261.],\n",
+       "        [234., 302., 204., ..., 228., 252., 202.],\n",
+       "        [316., 301., 292., ..., 166., 256., 144.],\n",
+       "        ...,\n",
+       "        [385., 302., 388., ...,   7.,   5.,   4.],\n",
+       "        [323., 327., 300., ...,   0.,   0.,   8.],\n",
+       "        [406., 406., 309., ...,   0.,   0.,  10.]],\n",
+       "\n",
+       "       [[287., 128., 292., ..., 192., 167., 206.],\n",
+       "        [268., 264., 192., ..., 262., 236., 233.],\n",
+       "        [314., 244., 227., ..., 171., 237., 210.],\n",
+       "        ...,\n",
+       "        [379., 232., 343., ...,   5.,   1.,   3.],\n",
+       "        [339., 300., 301., ...,   0.,   0.,   3.],\n",
+       "        [376., 411., 277., ...,   0.,   0.,   2.]]])"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr_est.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0558d3c7-6b39-4e6b-ab8f-c399cee52a29",
+   "metadata": {},
+   "source": [
+    "Now, we are going to filter the values which are less than 10. In the resulting xarray, all the values less than 10 are going to be `nan`. We can also replace `nan` by any other value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "91ab0e4d-47c5-4ffb-a602-719c2bb29f76",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[274., 215., 295., ..., 221., 181., 143.],\n",
+       "        [248., 285., 284., ..., 165., 190., 165.],\n",
+       "        [244., 224., 274., ..., 188., 182., 162.],\n",
+       "        ...,\n",
+       "        [351., 382., 376., ...,  22.,  41.,  41.],\n",
+       "        [340., 343., 358., ...,  nan,  nan,  50.],\n",
+       "        [289., 320., 334., ...,  nan,  nan,  51.]],\n",
+       "\n",
+       "       [[219., 184., 278., ..., 219., 237., 214.],\n",
+       "        [213., 227., 275., ..., 188., 236., 239.],\n",
+       "        [241., 233., 313., ..., 205., 210., 197.],\n",
+       "        ...,\n",
+       "        [379., 379., 379., ...,  41.,  68.,  34.],\n",
+       "        [333., 368., 371., ...,  nan,  nan,  39.],\n",
+       "        [329., 365., 379., ...,  nan,  nan,  38.]],\n",
+       "\n",
+       "       [[325., 182., 273., ..., 257., 262., 261.],\n",
+       "        [234., 302., 204., ..., 228., 252., 202.],\n",
+       "        [316., 301., 292., ..., 166., 256., 144.],\n",
+       "        ...,\n",
+       "        [385., 302., 388., ...,  nan,  nan,  nan],\n",
+       "        [323., 327., 300., ...,  nan,  nan,  nan],\n",
+       "        [406., 406., 309., ...,  nan,  nan,  10.]],\n",
+       "\n",
+       "       [[287., 128., 292., ..., 192., 167., 206.],\n",
+       "        [268., 264., 192., ..., 262., 236., 233.],\n",
+       "        [314., 244., 227., ..., 171., 237., 210.],\n",
+       "        ...,\n",
+       "        [379., 232., 343., ...,  nan,  nan,  nan],\n",
+       "        [339., 300., 301., ...,  nan,  nan,  nan],\n",
+       "        [376., 411., 277., ...,  nan,  nan,  nan]]])"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr_masked_lt = arr_est.where(arr_est >= 10)\n",
+    "arr_masked_lt.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f511c71-5098-4aa2-9d0c-da089c967b7e",
+   "metadata": {},
+   "source": [
+    "Now, let's try to perform filtering based on the opposite condition so as to mask all the values greater than 10."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "d7101951-869a-40e8-b5f0-79a9b625ba5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ...,  0.,  0., nan],\n",
+       "        [nan, nan, nan, ...,  0.,  0., nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ...,  0.,  0., nan],\n",
+       "        [nan, nan, nan, ...,  0.,  0., nan]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ...,  7.,  5.,  4.],\n",
+       "        [nan, nan, nan, ...,  0.,  0.,  8.],\n",
+       "        [nan, nan, nan, ...,  0.,  0., 10.]],\n",
+       "\n",
+       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        ...,\n",
+       "        [nan, nan, nan, ...,  5.,  1.,  3.],\n",
+       "        [nan, nan, nan, ...,  0.,  0.,  3.],\n",
+       "        [nan, nan, nan, ...,  0.,  0.,  2.]]])"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr_masked_gt = arr_est.where(arr_est <= 10)\n",
+    "arr_masked_gt.values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfeeec10-e023-4234-b528-aa7dc87c7e05",
+   "metadata": {},
+   "source": [
+    "### Plotting mean time-series\n",
+    "\n",
+    "Below we will look into plotting the mean time-series for the `estimate` band. We will be using the `compute()` module of the `dask` which performs an in-memory computation in a parallel approach."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "1d960b78-e15b-4e2a-970f-a07239a49272",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fc22818e410>]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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jPs4nW0vF/5MiTWjdmjVrNLo1fv75ZzIzM9WFTe5/xE1NTTX2+/bbb4t8ztatW/Pzzz+zbds2jb+ACzvGT253Z2FUq1btqeu7du3K7Nmzsbe3L3ThZ2pqSqtWrShTpgw7d+7kr7/+olmzZuqfUWFasV6USqVCUZQ8/y4rVqzI01VYUK7mzZtTpkwZLly4wKhRowp97kqVKjFq1Ch2797NoUOHingFsGnTJubPn69uWUpKSuLXX3+lZcuWz2xNadu2LfPmzWP16tWMGzdOvfyXX34hOTm5SMNSFOXf71X8fjzJ0tKSTp06kZ6eTs+ePTl//nyBRZqlpSU+Pj5s2rSJL774AnNzcyCna3D16tW4urpStWrVl5KrKAr7O1uY30MXFxdcXFyoXr06FStWZNu2bfkWab169WL58uX88ssvGg8mrVq1ChcXF3x8fJ56nl69ejFixAjCwsLU22ZmZrJ69Wp8fHx0Usy86DXlp3bt2i8zYoklRZrQuk2bNmFkZET79u05f/48U6dOpW7duvTt2xfIudejbNmyDB8+nOnTp2NsbMyaNWs4c+ZMkc85aNAgFixYwKBBg5g1axZVqlTh999/Z+fOnYXa38TEhEaNGhX5/E8aO3Ysv/zyC35+fowbN446deqQnZ3NjRs32LVrFx999BE+Pj5MmzaNW7du0bZtW1xdXXn48CFfffWVxv1Hnp6emJubs2bNGmrUqIGVlZX6y+Rls7Gxwc/Pj/nz5+Pg4IC7uzuhoaGsXLmSMmXKaGyb+5f1smXLsLa2xszMDA8PD+zt7fnf//7H4MGDiY+Pp0+fPjg6OnLv3j3OnDnDvXv3WLp0KQkJCbRu3ZoBAwZQvXp1rK2tOX78ODt27KB3797q8+zbt4/WrVsXuqvE0NCQ9u3bM378eLKzs5k7dy6JiYnMmDHjmfu2b9+eDh06MHnyZBITE2nevDnh4eFMnz6d+vXrM3DgwKfu/84777Bq1SoiIyPVRc/Tfk4FeRW/H++99x7m5uY0b96c8uXLExMTQ1BQELa2ts9sIQwKCqJ9+/a0bt2aCRMmYGJiwpIlSzh37hzr1q3T6RhoL+t39km5vQAFtVx26tSJ9u3b88EHH5CYmIiXlxfr1q1jx44drF69WuOPgfw+E0OHDuXrr7/m9ddfZ86cOTg6OrJkyRIuX76cZ/iZwMBAZsyY8czWe8h5Cjd3qKOsrCyuX7/Oxo0bAWjVqpW6dXjmzJnMnDmT3bt3q/878zzX9P333/P2228XqnWyIF5eXgBEREQ89Wf1ww8/MHToUL777jsGDRpUpHPpO3lwQGjdpk2buHTpEr1792batGl069aNXbt2qe81sbe3Z/v27VhYWPDWW28xdOhQrKys8n2cv7AsLCzYs2cP7dq14+OPP6ZPnz7cunVLJ9OaWFpacuDAAYYMGcKyZcvo0qULffv2ZdGiRbi6uqrHn/Lx8SEmJobJkycTEBDA+++/j7m5OXv27KFWrVrq6/ruu++Ii4sjICCAxo0bs2zZsleWfe3atbRu3ZpJkybRu3dvTpw4QUhIiMa9XJDTMrFw4ULOnDmDv78/jRs35tdffwXgrbfeYu/evTx69Ihhw4bRrl07PvzwQ06dOqVujTIzM8PHx4cff/yRN998k06dOrFixQomT57M8uXL1ed59OgRUPB9Tv82atQo2rdvz5gxYxgwYACZmZls376d5s2bP3Pf3GmQxo8fT3BwMJ07d1YPx7Fnz548LVv/Lk6ysrLIysrSGO7haT+ngryK34+WLVty7tw5PvzwQ9q3b8+4ceOoWrUqBw4cyHMP3r+1atWKPXv2YGlpyZAhQ+jXrx8JCQls27btuYa4KS5y/12VpwzbsWnTJgYOHMi0adPo2LEjYWFhrFu3Ls9DU/l9JkxNTdm9ezetW7dm9OjRdOvWjejoaP744488Mzw8evRIfU/ks0yfPp3XX3+d119/ncePH7Nv3z71+yeH9cjOzs6T6Xmu6Xl/J/OT+zDBk/L7WeVmLckPHaiUp33ShBBCj02aNIl169Zx5cqVPDfHPyn3nsL58+czYcKEV56rfv36eHp6qlsqRMliZGTEkCFDWLFihU5zNGnSBDc3NzZs2KDTHE/q27cvUVFRhe5qFk8n3Z1CiGJr7969TJ069akFmjb9/fffHDhwgLNnz/LWW2/pOo54Rdzd3dm/fz+RkZFUqlSp0A8ovEyJiYmcOXOGVatWaf3cBVEUhX379rF69WpdRykxpEgTQhRb+vbXelBQEL/++iuDBg1Szw4gSp4ZM2YwfPhwvLy8sLW1feEna4vCxsbmmbNcaJtKpSI2NlbXMUoU6e4UQgghnlN6ejpRUVE8fvyYunXr6jqOKKGkSBNCCCGE0EPydKcQQgghhB6SIk0IIYQQQg/JgwPFWHZ2Nnfu3MHa2lqnA0YKIYQQovAURSEpKQkXFxcMDApuL5MirRi7c+cOFStW1HUMIYQQQhTBzZs3cXV1LXB9qS7SgoKC1KPfm5ub4+vry9y5czXmXlQUhRkzZrBs2TIePHiAj48PX3/9tXrE96dNvP3zzz/z+uuvF3j+JUuWMH/+fKKjo6lVqxYLFy6kZcuWhc5vbW0N5Pwj29jYFHo/IYQQQuhOYmIiFStWVH+PF6RUP93ZsWNH+vXrR+PGjcnMzOTTTz/l7NmzXLhwAUtLSwDmzp3LrFmz+P7776latSqff/45+/fv5/Lly1hbW5OVlaWeDy3XsmXLmDdvHjExMVhZWeV77vXr1zNw4ECWLFlC8+bN+fbbb1mxYgUXLlygUqVKhcqfmJiIra0tCQkJUqQJIYQQxURhv79LdZH2b/fu3cPR0ZHQ0FD8/PxQFAUXFxfGjh3L5MmTAUhLS8PJyYm5c+cybNiwfI9Tv359GjRowMqVKws8l4+PDw0aNGDp0qXqZTVq1KBnz54EBQUVKq8UaUIIIUTxU9jvb3m68wkJCQkA2NnZARAVFUVMTAwBAQHqbUxNTWnVqhWHDx/O9xgnT57k9OnTvPPOOwWeJz09nZMnT2ocFyAgIKDA40JOgZiYmKjxEkIIIUTJJEXaPxRFYfz48bRo0QJvb28AYmJiAHByctLY1snJSb3u31auXEmNGjXw9fUt8Fz3798nKyvruY4LOffQ2draql/y0IAQQghRckmR9o9Ro0YRHh7OunXr8qz79/AWiqLkO+RFamoqa9eufWorWlGOm2vKlCkkJCSoXzdv3izUeYQQQghR/JTqpztzjR49mm3btrF//36NR2GdnZ2BnBa18uXLq5fHxsbmaQUD2LhxIykpKQwaNOip53NwcMDQ0DBPq1lBx81lamqKqalpoa5JCCGEEMVbqW5JUxSFUaNGsWnTJvbs2ZNnKA0PDw+cnZ0JCQlRL0tPTyc0NDTf7syVK1fSvXt3ypUr99TzmpiY0LBhQ43jAoSEhDy1m1QIIYQQpUepbkkbOXIka9euZevWrVhbW6tbtmxtbTE3N0elUjF27Fhmz55NlSpVqFKlCrNnz8bCwoIBAwZoHCsiIoL9+/fz+++/53uutm3b0qtXL0aNGgXA+PHjGThwII0aNaJZs2YsW7aMGzduMHz48Fd70UIIIYQoFkp1kZY7/IW/v7/G8uDgYIYMGQLApEmTSE1NZcSIEerBbHft2pVnALrvvvuOChUq5HliM1dkZCT3799Xv3/jjTeIi4tj5syZREdH4+3tze+//46bm9vLu0AhhBBCFFsyTloxJuOkCSGEEMWPjJMmhBBCCFGMSZEmhBBCCKGHpEgTQgghhNBDUqSJPP44G82Ja/G6jiGEEEKUaqX66U6R1+OMLKZuPcf9R+k08bBjhL8nraqWe+pMCEIIIYR4+aQlTWhITc+iXQ0njA1VHIuKZ0jwcbr+7yDbw6PJypYHgYUQQghtkSE4irFXOQRHdEIqKw5EsTbsBqkZWQBUdrBkeCtPetavgImR1PdCCCFEURT2+1uKtGJMG+OkxSen8/3ha6w6fI2E1AwAytua8V7LyvRrUhELE+kxF0IIIZ6HFGmlgDYHs32UlsnasOusOBBFbFIaAHaWJrzt686gZu7YWhi/0vMLIYQQJYUUaaWALmYceJyRxS+nbvFt6FVuxKcAYGVqxJtNK/FOCw8crc20kkMIIYQorqRIKwV0OS1UZlY2289Gs2RvJJfvJgFgYmRA30auDPPzpKKdhVbzCCGEEMWFFGmlgD7M3ZmdrbDnUixf74vgrxsPATA0UNG9rgsf+HtS1cn66QcQQgghShkp0koBfSjScimKwtGr8SzZF8GBK/fVy9vXdGKEvyf1K5XVYTohhBBCf0iRVgroU5H2pPBbD1m6L5Id52PI/XT5etozsrUXvp72MjCuEEKIUk2KtFJAX4u0XBGxj/gmNJItf90m85+BcOu62jKitRftazhhYCDFmhBCiNJHirRSQN+LtFy3HqSw4kAU647dIC0zG4AqjlZ84O9Jt7ouGBvKwLhCCCFKDynSSoHiUqTluv8ojeBDUfxw+DpJaZkAVChjzvBWlXm9UUXMjA11nFAIIYR49aRIKwWKW5GWK/FxBquPXmflgSjiktMBcLAy5Z0WHrzVtBLWZjIwrhBCiJJLirRSoLgWabkeZ2Tx84mbfBt6ldsPUwGwNjNicDN33m7ujr2VqY4TCiGEEC+fFGmlQHEv0nJlZGWz7fQdluyLIPJeMgBmxgb0a1yJ9/wqU6GMuY4TCiGEEC+PFGmlQEkp0nJlZyvsunCXJfsiCL+VAICRgYpe9Ssw3N8Tz3JWOk4ohBBCvDgp0kqBklak5VIUhUMRcXy9N4IjV+MAUKmgk7czI/y98K5gq+OEQgghRNFJkVYKlNQi7Umnbjxgyd5I/rx4V73Mr2o5Rvh74uNhJwPjCiGEKHakSCsFSkORlutyTBJL90Xwa3g0Wf8MjNvQrSwj/D1pU91RijUhhBDFhhRppUBpKtJy3YhL4dv9kWw4eYv0fwbGre5szQf+nnSpXR4jGRhXCCGEnpMirRQojUVartjEx6w8GMXqo9dJTs8CwM3egmF+nrzWsAKmRjIwrhBCCP0kRVopUJqLtFwJKRmsOnKN4ENRPEjJAMDJxpR3W1RmgE8lLE2NdJxQCCGE0CRFWikgRdr/S0nPZN2xmyzff5WYxMcAlLEwZnAzd4b4ulPW0kTHCYUQQogcUqSVAlKk5ZWWmcWWv27zTehVou7nDIxrYWLIgCaVeLdlZZxtzXScUAghRGknRVopIEVawbKyFf44F82SvZFciE4EwMTQgNcaVmCYnyfuDpY6TiiEEKK0kiKtFJAi7dkURWHf3/dYujeSY9fiATBQQZc6Lozw96RGefm5CSGE0C4p0koBKdKez/Fr8SzZG8Hey/fUy9pUd2Rka08autnpMJkQQojSRIq0UkCKtKI5fyeBpfsi2X42mtxPfxMPO0a29sKvioMMjCuEEOKVkiKtFJAi7cVE3U/m29BIfjl1i4ysnF+DWi42jGztRYdazhgaSLEmhBDi5ZMirRSQIu3liE5IZcWBKNaG3SA1I2dg3MoOlgz396RnvQqYGMksBkIIIV4eKdJKASnSXq745HS+P3yN7w9Fkfg4EwAXWzPe86tMv8aVMDeRWQyEEEK8OCnSSgEp0l6NR2mZrA27zvIDUdxLSgPAztKEoc3dGdjMHVtzYx0nFEIIUZwV9vu7VPfjBAUF0bhxY6ytrXF0dKRnz55cvnxZYxtFUQgMDMTFxQVzc3P8/f05f/58nmMdOXKENm3aYGlpSZkyZfD39yc1NbXAcwcGBqJSqTRezs7OL/0axfOzMjXifT9PDkxqzaxe3lS0Myc+OZ0vdv1N8zl7mPPHJWKTHus6phBCiBKuVBdpoaGhjBw5kqNHjxISEkJmZiYBAQEkJyert5k3bx5ffvklixcv5vjx4zg7O9O+fXuSkpLU2xw5coSOHTsSEBDAsWPHOH78OKNGjcLA4Ok/3lq1ahEdHa1+nT179pVdq3h+ZsaGvOnjxt6P/PmqXz2qOVnzKC2Tb0IjaTF3L1O3nONmfIquYwohhCihpLvzCffu3cPR0ZHQ0FD8/PxQFAUXFxfGjh3L5MmTAUhLS8PJyYm5c+cybNgwAJo2bUr79u357LPPCn2uwMBAtmzZwunTp4ucV7o7tSs7W2HPpVi+3hfBXzceAmBooKJHXRc+8PekipO1bgMKIYQoFqS7swgSEhIAsLPLGdg0KiqKmJgYAgIC1NuYmprSqlUrDh8+DEBsbCxhYWE4Ojri6+uLk5MTrVq14uDBg88835UrV3BxccHDw4N+/fpx9erVV3BV4mUxMFDRrqYTmz7wZd17TWlZxYGsbIVNf92m/YL9vP/DCU7ffKjrmEIIIUoIKdL+oSgK48ePp0WLFnh7ewMQExMDgJOTk8a2Tk5O6nW5hVVgYCDvvfceO3bsoEGDBrRt25YrV64UeD4fHx9++OEHdu7cyfLly4mJicHX15e4uLgC90lLSyMxMVHjJbRPpVLRzNOeH9/xYduo5nSs5YxKBbsu3KXn14d4c8VRDkXcRxqphRBCvAgp0v4xatQowsPDWbduXZ51/x6BXlEU9bLs7GwAhg0bxttvv039+vVZsGAB1apV47vvvivwfJ06deK1116jdu3atGvXju3btwOwatWqAvcJCgrC1tZW/apYseJzX6d4ueq4luGbgQ0JGefHaw1cMTJQcSgijjdXhNFzyWF2no8hO1uKNSGEEM9PijRg9OjRbNu2jb179+Lq6qpenvu0ZW6rWa7Y2Fh161r58uUBqFmzpsY2NWrU4MaNG4XOYGlpSe3atZ/a+jZlyhQSEhLUr5s3bxb6+OLV8nK05r9967Jvoj+Dm7lhamTAmZsPGfbjSTos3M+mU7fIyMrWdUwhhBDFSKku0hRFYdSoUWzatIk9e/bg4eGhsd7DwwNnZ2dCQkLUy9LT0wkNDcXX1xcAd3d3XFxc8gzd8ffff+Pm5lboLGlpaVy8eFFd9OXH1NQUGxsbjZfQL65lLZjRw5tDH7dhhL8n1qZGXIl9xPifz9D6i338eOQaj/+Z1UAIIYR4Gq0XadHR0UybNo02bdpQo0YNvL296datGytXriQrS7tfXiNHjmT16tWsXbsWa2trYmJiiImJUY9vplKpGDt2LLNnz2bz5s2cO3eOIUOGYGFhwYABA9TbTJw4kUWLFrFx40YiIiKYOnUqly5d4p133lGfq23btixevFj9fsKECYSGhhIVFUVYWBh9+vQhMTGRwYMHa/VnIF4NBytTJnWszqEpbZjYoRr2libcepDK1K3naTF3L0v3RZL0OEPXMYUQQugxI22e7MSJE7Rr1w4PDw/Mzc35+++/efPNN0lPT2fChAmsXLmSnTt3Ym2tnaEMli5dCoC/v7/G8uDgYIYMGQLApEmTSE1NZcSIETx48AAfHx927dqlkXHs2LE8fvyYcePGER8fT926dQkJCcHT01O9TWRkJPfv31e/v3XrFv379+f+/fuUK1eOpk2bcvTo0edqfRP6z8bMmJGtvRja3IOfT9xk2f6r3H6Yytwdl1iyL4LBzdx5u7k79lamuo4qhBBCz2h1nLQWLVrQvn17pk+fDsDq1atZvHgxR48e5cGDB7Rp0wY/Pz+++uorbUUq1mSctOInIyubrafvsHRfBJH3cgZNNjM2oF/jSrzvVxmXMuY6TiiEEOJV08u5Oy0sLDh37hyVK1cGcp6MNDMz4+bNmzg5ORESEsKQIUO4ffu2tiIVa1KkFV/Z2Qq7LsSwZF8k4bdyxuczNlTRs14Fhvt74lnOSscJhRBCvCp6OZito6Mj0dHR6vd3794lMzNTHbBKlSrEx8drM5IQOmFgoKKjd3m2jmzOj+80oVllezKyFDacvEW7L0MZseYk524n6DqmEEIIHdLqPWk9e/Zk+PDhzJ8/H1NTUz777DNatWqFuXlOF8/ly5epUKGCNiMJoVMqlYqWVcrRsko5Tt14wJK9kfx58S6/n43h97Mx+FUtx0h/T5p42OUZr08IIUTJptXuzkePHvHOO++wadMmsrKyaNasGatXr1YPfbFr1y4SEhJ4/fXXtRWpWJPuzpLpUkwi3+yLZNuZO+SOg9vQrSwjW3vSupqjFGtCCFHM6eU9abkeP35MZmYmVlZy382LkCKtZLsRl8K3+yPZcOIW6f8MhFvd2ZoRrb3oUrs8hgZSrAkhRHGk10VaQkICWVlZ6onMc8XHx2NkZCQFRyFJkVY6xCY+ZuXBKFYfvU5yes5Ygm72Fgxv5UnvBhUwNTLUcUIhhBDPQy8fHMjVr18/fvrppzzLf/75Z/r166eDRELoL0cbM6Z0rsHhj9syvn1VyloYcz0uhSmbzuI3by8rDlwlOS1T1zGFEEK8ZDppSbOzs+PQoUPUqFFDY/mlS5do3rw5cXFx2o5ULElLWumUkp7JumM3Wb7/KjGJjwEoY2HMEF93hvi6U8bCRMcJhRBCPI1et6SlpaWRmZn3L/+MjAz1lExCiPxZmBjxTgsPQif5M/e12rjbW/AwJYOFf17Bd84eZm2/wN1/ijchhBDFl06KtMaNG7Ns2bI8y7/55hsaNmyog0RCFD+mRoa80bgSuz/yZ/GA+tQob0NKehbLD0TRcu5epmw6y/W4ZF3HFEIIUUQ66e48dOgQ7dq1o3HjxrRt2xaA3bt3c/z4cXbt2kXLli21HalYku5O8SRFUdj39z2W7I3g+LUHABiooGsdFz7w96RGefmMCCGEPtDrpzsBTp8+zbx58zhz5gzm5ubUqVOHKVOmUKVKFV3EKZakSBMFORYVz5J9Eey7fE+9rG11R0a09qShm91T9hRCCPGq6X2RJl6cFGniWc7dTmBpaCS/n40m9zfdx8OOEa298KviIAPjCiGEDuh1kfbo0SNOnjxJTEwMKpUKZ2dnGjRoIIPbPicp0kRhXb33iG9Dr7Lpr1tkZOX8yntXsGGEvxcdajnLwLhCCKFFelmkZWZm8tFHH7F8+XIeP36MiYkJiqKQkZGBmZkZ77//PvPnz8fY2FhbkYo1KdLE84pOSGX5/ijWHbtBakbOwLiVy1kyvJUnPetVwMRIJ88SCSFEqaKXQ3B89NFH/PLLLwQHBxMfH8/jx49JS0sjPj6e4OBgNm3axMSJE7UZSYhSpbytOdO61eTQx20Y08YLGzMjrt5LZtLGcPzn7yX4UBSp/8xqIIQQQre02pJWrlw51q9fT5s2bfJdv3v3bvr168e9e/fyXS80SUuaeFFJjzNYG3aDFQejuJeUBoCdpQlDm7szsJk7tubSqi2EEC+bXrakpaam4uDgUOB6e3t7GcxWCC2yNjNmWCtPDkxqzec9valoZ058cjpf7Pqb5nP2MOePS+riTQghhHZptSWtW7dupKamsmbNGpycnDTW3b17l4EDB2JmZsa2bdu0FalYk5Y08bJlZmXzW3g0S/dFcvluEgCmRgb0bVSR9/0qU9HOQscJhRCi+NPLBwdu3rxJ586duXTpEt7e3jg5OaFSqYiJieHcuXPUrFmT7du34+rqqq1IxZoUaeJVyc5W2H0plq/3RnD65kMADA1U9KibMzBuFSdr3QYUQohiTC+LNIDs7Gx27tzJ0aNHiYmJAcDZ2ZlmzZoREBCAgYE8XVZYUqSJV01RFI5cjWPpvkgOXLmvXh5Q04kRrb2oV7GM7sIJIUQxpbdFmnh5pEgT2nTm5kOW7otkx/kY9bLmXvaM9Peimae9DIwrhBCFpLdFWmxsLOfPn6dhw4bY2Nhw9+5dVq1aRXZ2Nl26dKF27drajFOsSZEmdCEiNoml+66y5fRtsrJz/vNRt2IZRvp70q6GEwYyMK4QQjyVXhZp+/bto2vXrqSkpODs7MyOHTvo0qUL5ubmGBgYcO3aNbZt20ZAQIC2IhVrUqQJXbr1IIXl+6/y0/GbpGVmA1DVyYoP/D3pVscFI0O5dUEIIfKjl0VaixYtqFevHnPmzOGbb77hq6++okePHixevBiAiRMncvjwYQ4dOqStSMWaFGlCH9xLSiP4UBQ/HrlOUlomAK5lzRnWypPXG7piZmyo44RCCKFf9LJIs7W15dSpU3h6epKZmYm5uTnHjx+nXr16AFy5coXGjRvz8OFDbUUq1qRIE/ok8XEGPx65zncHo4hLTgfAwcqUd1t68KZPJazNZGBcIYQAPR3M1sTEhMePHwOQnp5Odna2+j3kDHYr83YKUTzZmBkzsrUXBye3YUb3WlQoY879R2nM+eMSzefs4b+7LhP3SAbGFUKIwtJqS1rPnj3Jysri448/5ocffuDUqVM4OTmxfv16VCoVgwcP5tGjR/zxxx/ailSsSUua0GcZWdlsPX2HpfsiiLyXDICFiSErBjfC17PgmUeEEKKk08uWtPnz53Pp0iVatmzJoUOH2Lp1K4aGhpQpUwZbW1tCQ0OZNWuWNiMJIV4RY0MD+jR0JWRcK755qwG1XGxISc9iws9nSHqcoet4Qgih93QyTlpcXBz29vbq97t37yY1NZVmzZppLBdPJy1pojhJSc+kw8L93IxP5U2fSszqJcPtCCFKJ718cEC8XFKkieLmcOR9BiwPA2Dtuz74ekm3pxCi9NHL7s5ffvmFlJQUbZ5SCKFHfD0deNOnEgCTN4WT/M+QHUIIIfLSapH2+uuv4+zszPvvv09YWJg2Ty2E0BNTOtegQhlzbsanMn/nZV3HEUIIvaX1IcEnTpzIiRMnaNasGd7e3ixcuJC4uDhtxxBC6IiVqRFBvXPuR/v+8DWORcXrOJEQQugnrRdpw4YN49SpUxw/fhw/Pz9mzJhBhQoV6Nu3LyEhIdqOI4TQAb+q5XijUUUAJv8STmp6lo4TCSGE/tHZ5HoNGzZkyZIlREdHs3z5cu7du0fHjh1xd3fXVSQhhBZ90qUGTjamRN1P5ssQ6fYUQoh/02qRplKp8iwzMzNj4MCB7N27l8uXL/Pmm29qM5IQQkdszY3V3Z4rD0Zx6sYDHScSQgj9otUi7VmjfXh5eclgtkKUIm2qO9G7fgWyFZi0MZzHGdLtKYQQubRapEVFReHgoD/jIgUFBdG4cWOsra1xdHSkZ8+eXL6s2e2iKAqBgYG4uLhgbm6Ov78/58+fz3OsI0eO0KZNGywtLSlTpgz+/v6kpqY+9fxLlizBw8MDMzMzGjZsyIEDB17q9QlRHEzrVpNy1qZExD5i0e4ruo4jhBB6Q6tFmpubGwYGOrsNLo/Q0FBGjhzJ0aNHCQkJITMzk4CAAJKTk9XbzJs3jy+//JLFixdz/PhxnJ2dad++PUlJSeptjhw5QseOHQkICODYsWMcP36cUaNGPfVa169fz9ixY/n000/566+/aNmyJZ06deLGjRuv9JqF0DdlLEz4vKc3AN/uv8rZWwk6TiSEEPpB6zMO/Prrr5w4cYKOHTvSrFkz9uzZwxdffEF2dja9e/fm/fff12YcDffu3cPR0ZHQ0FD8/PxQFAUXFxfGjh3L5MmTAUhLS8PJyYm5c+cybNgwAJo2bUr79u357LPPCn0uHx8fGjRowNKlS9XLatSoQc+ePQkKCirUMWTGAVGSjFp7it/Co6nubM22US0wMdKfP+iEEOJl0ssZB7755ht69+7N9u3b6dixI2vWrKFnz55UqFABd3d3xo4dy1dffaXNSBoSEnL+grezswNyumdjYmIICAhQb2NqakqrVq04fPgwALGxsYSFheHo6Iivry9OTk60atWKgwcPFnie9PR0Tp48qXFcgICAAPVx85OWlkZiYqLGS4iSYkb3WthbmnApJonFeyN0HUcIIXROq0XaokWLWLJkCSdOnGDLli28++67zJkzh+XLl/PNN9+wZMkSvv32W21GUlMUhfHjx9OiRQu8vXO6XmJiYgBwcnLS2NbJyUm97urVqwAEBgby3nvvsWPHDho0aEDbtm25ciX/+2vu379PVlbWU4+bn6CgIGxtbdWvihUrFu1ihdBD9lamzOhRC4AleyO4cEf+CBFClG5aLdKuXbtGhw4dAGjdujVZWVn4+fmp1/v7+3P9+nVtRlIbNWoU4eHhrFu3Ls+6fw8doiiKell2djaQM0jv22+/Tf369VmwYAHVqlXju+++e+o5n3bc/EyZMoWEhAT16+bNm4W6NiGKiy61y9OxljOZ2QoTN54hIytb15GEEEJntFqk2dvbq4uwO3fukJmZqXGj/PXr19Vdjdo0evRotm3bxt69e3F1dVUvd3Z2BsjTuhUbG6tuBStfvjwANWvW1NimRo0aBT4E4ODggKGh4VOPmx9TU1NsbGw0XkKUJCqVipk9a1HGwpjzdxL5NjRS15GEEEJntFqk9ejRg3feeYdZs2bRq1cvBg0axEcffcSOHTvYuXMno0ePznOf1qukKAqjRo1i06ZN7NmzBw8PD431Hh4eODs7a0xXlZ6eTmhoKL6+vgC4u7vj4uKSZ+iOv//+Gzc3t3zPa2JiQsOGDfNMgxUSEqI+rhCllaO1GdO75fzRs2h3BH/fTXrGHkIIUUIpWvTo0SPl3XffVby9vZXhw4cr6enpyvz58xUTExNFpVIp/v7+yt27d7WW54MPPlBsbW2Vffv2KdHR0epXSkqKeps5c+Yotra2yqZNm5SzZ88q/fv3V8qXL68kJiaqt1mwYIFiY2OjbNiwQbly5Yryn//8RzEzM1MiIiLU27Rp00b53//+p37/008/KcbGxsrKlSuVCxcuKGPHjlUsLS2Va9euFTp/QkKCAigJCQkv+JMQQr9kZ2crQ4OPKW6Tf1O6/++AkpGZpetIQgjx0hT2+1urRVpBUlNTNYoebQHyfQUHB6u3yc7OVqZPn644Ozsrpqamip+fn3L27Nk8xwoKClJcXV0VCwsLpVmzZsqBAwc01ru5uSnTp0/XWPb1118rbm5uiomJidKgQQMlNDT0ufJLkSZKsuiHqYr39B2K2+TflG/2RTx7ByGEKCYK+/2t9XHSnpSenk5UVBSenp4YGRnpKkaxJeOkiZLu5xM3mbQxHBMjA/74sCWe5ax0HUkIIV6YXo6TlislJYV33nkHCwsLatWqpb7BfsyYMcyZM0cXkYQQeuj1hq74VS1HemY2kzaGk5Wts78phRBC63RSpE2ZMoUzZ86wb98+zMzM1MvbtWvH+vXrdRFJCKGHVCoVQb1rY2VqxMnrD/j+8DVdRxJCCK3RSZG2ZcsWFi9eTIsWLTTGBatZsyaRkfLIvRDi/1UoY86UztUBmL/zEtfjkp+xhxBClAw6KdJy58j8t+Tk5KcO5iqEKJ0GNKmEr6c9jzNyuj2zpdtTCFEK6KRIa9y4Mdu3b1e/zy3Mli9fTrNmzXQRSQihx1QqFXNfq4O5sSFhUfGsCdPNzCRCCKFNOnmkMigoiI4dO3LhwgUyMzP56quvOH/+PEeOHCE0NFQXkYQQeq6inQWTO1Yj8NcLBP1xCf9qjlS0s9B1LCGEeGV00pLm6+vLoUOHSElJwdPTk127duHk5MSRI0do2LChLiIJIYqBQc3caeJuR0p6FlM2nUWHIwgJIcQrp9Nx0sSLkXHSRGkUdT+Zjgv3k5aZzZzetenXpJKuIwkhxHPR63HScmVmZhISEsLKlSvZvXs3WVlZuowjhCgGPBwsmdihGgCztl8kOiFVx4mEEOLV0GqRNmbMGPUDA7du3aJ27dp06tSJTz/9lA4dOlC/fn1u376tzUhCiGLo7eYe1K9UhqS0TOn2FEKUWFot0jZu3EjlypUB+Oijj3B1dSUmJoaYmBhiY2Nxc3Nj7Nix2owkhCiGDA1UzO9TBxMjA/Zdvscvp+SPOyFEyaPVIu3BgwfqGQYOHz7MrFmzcHBwAMDOzo6goCD27dunzUhCiGLKy9Gase2qADDz1/PcTXys40RCCPFyabVIq1q1KseOHQPA2tqaxMREjfVJSUlkZ2drM5IQohh7v2Vl6rjakvg4k083n5NuTyFEiaLVIm3cuHFMmDCBffv2MWXKFMaMGcPu3bu5c+cOe/fuZdiwYfTu3VubkYQQxZiRoQHz+9TF2FDFnxfvsu3MHV1HEkKIl0arg9kOGTKE+Ph4unTpgqIoZGVlERAQoF7fvXt3FixYoM1IQohirpqzNaPbVOHLkL8J3HYeX08Hylmb6jqWEEK8MJ2Mk/bw4UNCQkK4evUq2dnZlC9fnubNm1OlShVtRynWZJw0IXJkZGXTffEhLkYn0rm2M0velEGxhRD6q7Df3zKYbTEmRZoQ/+/c7QR6fn2IzGyFJW82oHPt8rqOJIQQ+Srs97dO5u6EnCc9V65cycWLF1GpVFSvXp2hQ4diZ2enq0hCiGLMu4ItH/h78r89EUzbeo6mle2xszTRdSwhhCgyncw4EBoairu7O4sWLeLBgwfEx8fzv//9Dw8PD5lgXQhRZKPaeFHVyYr7j9KZ8et5XccRQogXopPuTm9vb3x9fVm6dCmGhoYAZGVlMWLECA4dOsS5c+e0HalYku5OIfI6c/MhvZYcIluB5YMa0b6mk64jCSGEBr2euzMyMpKPPvpIXaABGBoaMn78eCIjI3URSQhRQtStWIb3/HJmNvl081kSUjJ0nEgIIYpGJ0VagwYNuHjxYp7lFy9epF69etoPJIQoUca1q0rlcpbEJqXx2fYLuo4jhBBForUHB8LDw9X/f8yYMXz44YdERETQtGlTAI4ePcrXX3/NnDlztBVJCFFCmRkbMr9PHfp8c4SNJ2/RpU55Wldz1HUsIYR4Llq7J83AwACVSvXMaVtUKhVZWVnaiFTsyT1pQjzdZ79dYOXBKMrbmrFznB82Zsa6jiSEEPo3BEdUVJS2TiWEEABMCKjGnxfvcj0uhaDfLxLUu46uIwkhRKFprUhzc3PT1qmEEAIAcxND5r5Wh37LjrLu2E261HahRRUHXccSQohC0fqDA2fOnCE4OFjdsnb+/HlGjBjB8OHD2blzp7bjCCFKuKaV7RnULOePxMm/hJOclqnjREIIUThaLdJ++eUXGjZsyMSJE6lbty67d++mRYsWXLlyhWvXrtGlSxfWrl2rzUhCiFJgcsfquJY15/bDVObuuKTrOEIIUShaLdJmz57NjBkzuH//PsuWLaNPnz6MHz+ekJAQduzYwdy5c5k/f742IwkhSgFLUyPmvpZzP9oPR65z9GqcjhMJIcSzabVIu3z5Mm+++SYAb7zxBsnJyfTs2VO9vlevXkRERGgzkhCilGju5UD/JhWBnG7P1HR5ilwIod+0WqRZW1sTF5fzF+zDhw/JzMxUvweIi4vDyspKm5GEEKXIlM41KG9rxvW4FObvvKzrOEII8VRaLdLatWvHyJEjWbNmDYMHD6ZDhw5MmTKFS5cucfnyZSZOnEiLFi20GUkIUYrYmBkT1Ls2AMGHozh5PV7HiYQQomBaLdK++OILrK2tGT58OJmZmaxfv55GjRpRs2ZNatasyZ07d2TGASHEK+VfzZE+DV1RFJi4MZzHGdLtKYTQT1qbceBpIiMjSU1NpXr16hgZaW3otmJPZhwQomgSUjJovyCU2KQ0hrWqzJRONXQdSQhRihT2+1snE6z/m6enJ97e3lKgCSG0wtbCmFm9cro9l++/yumbD3UbSAgh8qHVIu3WrVvcv39f/f7AgQO8+eabtGzZkrfeeosjR45oM44QohRrX9OJHvVcyFZg4oYzpGVKt6cQQr9otUjr27cvx48fB2Dr1q34+/vz6NEjmjdvTkpKCq1ateK3337TZiQhRCkW2K0WDlYmXIl9xOI9MvyPEEK/aLVIO3fuHDVq5Nz7ERQUxOzZs9m6dStz5sxh06ZNfPnll0ybNk1reYKCgmjcuDHW1tY4OjrSs2dPLl/WfCxfURQCAwNxcXHB3Nwcf39/zp8/r7GNv78/KpVK49WvX7+nnjswMDDPPs7Ozi/9GoUQBStracJnPbwBWLIvknO3E3ScSAgh/p9WizQDAwMSExMBiIqKolOnThrrO3XqlKdIepVCQ0MZOXIkR48eJSQkhMzMTAICAkhOTlZvM2/ePL788ksWL17M8ePHcXZ2pn379iQlJWkc67333iM6Olr9+vbbb595/lq1amnsc/bs2Zd+jUKIp+tUuzxdapcnK1th4sZw0jOzdR1JCCEA0Oqd+q1atWLdunXUqVOH+vXrs2/fPurUqaNev3fvXipUqKC1PDt27NB4HxwcjKOjIydPnsTPzw9FUVi4cCGffvopvXv3BmDVqlU4OTmxdu1ahg0bpt7XwsLiuVvCjIyMpPVMCD0wo0ctDkfe52J0Ikv3RfJhuyq6jiSEENptSZszZw7Lly9n8ODBtGjRgk8//ZSBAwcye/ZsBg8ezKhRo/jkk0+0GUlDQkJOV4ednR2Q09oXExNDQECAehtTU1NatWrF4cOHNfZds2YNDg4O1KpViwkTJuRpacvPlStXcHFxwcPDg379+nH16tWXeDVCiMJysDIlsHstABbvvcKlmEQdJxJCCC0XaTVq1CAsLIz09HTmzZtHcnIya9asITAwkIiICH766SeGDBmizUhqiqIwfvx4WrRogbd3zj0qMTExADg5OWls6+TkpF4H8Oabb7Ju3Tr27dvH1KlT+eWXX9QtbwXx8fHhhx9+YOfOnSxfvpyYmBh8fX01psn6t7S0NBITEzVeQoiXo3tdF9rXdCIjS2HihnAys6TbUwihW1ofmMzT05N169ahKAqxsbFkZ2fj4OCAsbGxtqNoGDVqFOHh4Rw8eDDPOpVKpfFeURSNZe+99576/3t7e1OlShUaNWrEqVOnaNCgQb7ne/J+vNq1a9OsWTM8PT1ZtWoV48ePz3efoKAgZsyY8VzXJYQoHJVKxaye3oRdjePs7QSWHbjKCH8vXccSQpRiL9ySduvWLbKzn/8vTpVKhZOTE87OzjofxHb06NFs27aNvXv34urqql6ee7/Yk61mALGxsXla157UoEEDjI2NuXLlSqEzWFpaUrt27afuM2XKFBISEtSvmzdvFvr4Qohnc7QxY1q3nG7PhSFXiIh99m0LQgjxqrxwkVazZk2uXbv23PutXLkSb29vzMzMMDMzw9vbmxUrVrxonOeiKAqjRo1i06ZN7NmzBw8PD431Hh4eODs7ExISol6Wnp5OaGgovr6+BR73/PnzZGRkUL58+UJnSUtL4+LFi0/dx9TUFBsbG42XEOLleq1BBfyrlSM9K5uJG8PJytb5zHlCiFLqhYu0okz9OXXqVD788EO6devGhg0b2LBhA926dWPcuHH85z//edFIhTZy5EhWr17N2rVrsba2JiYmhpiYGFJTU4Gc1r6xY8cye/ZsNm/ezLlz5xgyZAgWFhYMGDAAyJl3dObMmZw4cYJr167x+++/8/rrr1O/fn2aN2+uPlfbtm1ZvHix+v2ECRMIDQ0lKiqKsLAw+vTpQ2JiIoMHD9ba9Qsh8lKpVAT1ro21qRF/3XhI8KEoXUcSQpRWyguysrJSIiMjn2sfe3t7Ze3atXmWr127VrG3t3/RSIUG5PsKDg5Wb5Odna1Mnz5dcXZ2VkxNTRU/Pz/l7Nmz6vU3btxQ/Pz8FDs7O8XExETx9PRUxowZo8TFxWmcy83NTZk+fbr6/RtvvKGUL19eMTY2VlxcXJTevXsr58+ff678CQkJCqAkJCQU6fqFEAVbF3ZdcZv8m1L109+Vq/ce6TqOEKIEKez3t0pRitAU9oSgoCA++OADypQpU+h9ypYty7Fjx6hSRXMsor///psmTZrw8OHDF4lUaiQmJmJra0tCQoJ0fQrxkimKwsCVxzgYcZ8m7nb89H5TDAxUz95RCCGeobDf3y/c3TllypTnKtAA3nrrLZYuXZpn+bJly3jzzTdfNJIQQryw3G5PCxNDjl2L54cj13QdSQhRyhTpsco+ffrQqFEjPv74Y43l8+fP59ixY2zYsCHPPk8OK6FSqVixYgW7du2iadOmABw9epSbN28yaNCgokQSQoiXrqKdBVM6VWfq1vPM3XGZNtWdqGRvoetYQohSokjdneXKlWPPnj3Url1bY/nZs2dp164dd+/ezbNP69atCxdIpWLPnj3PG6lUku5OIV697GyFASuOcvRqPM0q27P2PZ88YycKIcTzKOz3d5Fa0h49eoSJiUme5cbGxgWOgr93796inEoIIXTKwEDF3Nfq0HHhAY5cjWPtsRu86eOm61hCiFKgSPekeXt7s379+jzLf/rpJ2rWrPnCoYQQQp+42VsysUM1AIJ+v8Tth6k6TiSEKA2K1JI2depUXnvtNSIjI2nTpg0Au3fvZt26dfnej/ak6Ohodu/ejZ2dHe3atdNokUtOTua///0v06ZNK0osIYR4ZQb7urP9bDQnrz/g41/C+WFoE+n2FEK8UkUegmP79u3Mnj2b06dPY25uTp06dZg+fTqtWrUqcJ/jx48TEBBAdnY2GRkZuLq6snnzZmrVypmG5e7du7i4uJCVlVW0qyll5J40IbQr8t4jOn91gLTMbOa9Voe+jSvqOpIQohh65UNwdOnShUOHDpGcnMz9+/fZs2fPUws0gE8++YTevXvz4MED7t69S/v27WnVqhV//fVXUWMIIYTWeJazYnz7qgB8tv0CMQmPdZxICFGSvfA4ac/j5MmTTJ48GQMDA6ytrfn666+ZNGkSbdu25fjx49qMIoQQRfJuy8rUrViGpMeZfLr5bJGmxhNCiMIoUpGWlZXFF198QZMmTXB2dsbOzk7j9TSPH2v+5Tlp0iQ++eQTAgICOHz4cFHiCCGE1hgaqJjfpw4mhgbsvhTLltO3dR1JCFFCFalImzFjBl9++SV9+/YlISGB8ePH07t3bwwMDAgMDCxwP29v73wLsQkTJvDJJ5/Qv3//osQRQgitqupkzZi2XgAEbrtAbJJ0ewohXr4iFWlr1qxh+fLlTJgwASMjI/r378+KFSuYNm0aR48eLXC/QYMGcejQoXzXTZw4kZkzZ1KpUqWiRBJCCK0a1sqTWi42JKRmMHXLOen2FEK8dEV6utPS0pKLFy9SqVIlypcvz/bt22nQoAFXr16lfv36JCQkvIqs4l/k6U4hdOvCnUS6Lz5IZrbC4gH16VrHRdeRhBDFwCt9utPV1ZXo6GgAvLy82LVrF5AzxIapqekz94+KiuLKlSt5ll+5coVr164VJZIQQmhdTRcbRrbO6factvU8cY/SdJxICFGSFKlI69WrF7t37wbgww8/ZOrUqVSpUoVBgwYxdOjQZ+4/ZMiQfO9NCwsLY8iQIUWJJIQQOjGytRfVna2JT05n+rbzuo4jhChBijyY7ZPCwsI4dOgQXl5edO/e/Znb29jYcOrUKby8vDSWR0RE0KhRIx4+fPiikUoF6e4UQj+cvZVAzyWHyMpW+OathnT0dtZ1JCGEHnul3Z379+8nMzNT/d7Hx4fx48fTuXNn9u/f/8z9VSoVSUlJeZYnJCTIbANCiGKntqstw/wqA/CfLed4mJKu40RCiJKgSEVa69atiY+Pz7M8ISGB1q1bP3P/li1bEhQUpFGQZWVlERQURIsWLYoSSQghdGpM2yp4OVpx/1EaM3+9oOs4QogSoEgTrCuKku/EwnFxcVhaWj5z/3nz5uHn50e1atVo2bIlAAcOHCAxMZE9e/YUJZIQQuiUmbEh8/vU4bWlh9n012261i1Pm+pOuo4lhCjGnqtI6927N5DTXTlkyBCNJzmzsrIIDw/H19f3mcepWbMm4eHhLF68mDNnzmBubs6gQYMYNWrUM2csEEIIfVW/UlneaeHB8gNRTNl0ll3j7LA1N9Z1LCFEMfVcRZqtrS2Q05JmbW2Nubm5ep2JiQlNmzblvffeK9SxXFxcmD179vOcXggh9N5HAdX482IsUfeTmbX9AvP61NV1JCFEMfVcRVpwcDAA7u7uTJgwoVBdm/nJysrC0NBQ/T4sLIy0tDSaNWuGsbH81SmEKL7MjA2Z16cOfb89ws8nbtG1jgt+VcvpOpYQohgq0oMDkyZN0rgn7fr16yxcuFA9qG1BoqOjadGiBaamprRq1YoHDx7QtWtXmjVrhr+/P97e3upBcoUQorhq7G7H4GbuAEzZdJZHaZlP30EIIfJRpCKtR48e/PDDDwA8fPiQJk2a8N///pcePXqwdOnSAvebPHkyiqKwefNmypcvT9euXUlMTOTmzZtcv34dJycnZs2a9czzBwcHs2HDhjzLN2zYwKpVq4pySUII8VJN6liNinbm3H6YStDvF3UdRwhRDBWpSDt16pT6qcyNGzfi7OzM9evX+eGHH1i0aFGB+/3555/897//pVu3bixZsoQjR44wffp0KlSoQMWKFZkxYwZ//PHHM88/Z84cHBwc8ix3dHSU+9yEEHrBwsSIua/VAWBN2A0OR9zXcSIhRHFTpCItJSUFa2trAHbt2kXv3r0xMDCgadOmXL9+vcD9Hjx4QIUKFQCws7PDwsICNzc39XpPT89CdXdev34dDw+PPMvd3Ny4cePG816OEEK8Er6eDrzpUwmAyZvCSZZuTyHEcyhSkebl5cWWLVu4efMmO3fuJCAgAIDY2NinTm/g6OioUYT9e8iNBw8eFOphBEdHR8LDw/MsP3PmDPb29s9zKUII8UpN6VyDCmXMuRmfyvydl3UdRwhRjBSpSJs2bRoTJkzA3d0dHx8fmjVrBuS0qtWvX7/A/erVq8eRI0fU7+fMmaNRpB08eJA6deo88/z9+vVjzJgx7N27l6ysLLKystizZw8ffvgh/fr1K8olCSHEK2FlakRQ79oArDpyjePX8s7WIoQQ+SnyBOsxMTFER0dTt25dDAxyar1jx45hY2ND9erVixTm+PHjmJub4+3t/dTt0tPTGThwIBs2bMDIKGcUkaysLAYPHsw333yDiYlJkc5f3MgE60IUH5M3hrP+xE08HCz5fUxLzE0Mn72TEKJEKuz3d5GKtN27d9O2bdt81y1evJhRo0Y97yGL5MqVK5w+fRpzc3Nq166tcX9baSBFmhDFR0JqBgELQrmbmMZ7LT34tEtNXUcSQujIKy3SypQpQ0hICI0bN9ZYvnDhQqZNm0ZiYuJT99+zZw8HDx4kOjoaQ0NDPDw86N69O1WqVHnmuS9evMjRo0fx9fWlWrVqXLp0ia+++oq0tDTeeust2rRp87yXU2xJkSZE8bLn0l2Gfn8CAxVs/MCXBpXK6jqSEEIHCvv9XaR70hYsWEDnzp25cOGCetkXX3zB9OnT2b59e4H7xcbG4uPjQ7t27Zg5cybLli3j6NGjfPHFF9SoUYNJkyY99bw7duygXr16TJgwgXr16rFjxw78/PyIiIjgxo0bdOjQQSZoF0LorTbVnehdvwLZCkzaGM7jjCxdRxJC6LEiFWlvv/02kydPJiAggGvXrjF37lw+++wz/vjjD/X4afkZM2YMLi4uxMfHk5SUxAcffECtWrWIjo5m165dfPfdd3z11VcF7j9z5kwmTpxIXFwcwcHBDBgwgPfee4+QkBD+/PNPJk2axJw5c4pySUIIoRXTutWknLUpEbGPWLT7iq7jCCH0WJEfHACYMmUKy5cvJysrix07duDj4/PU7W1tbTl8+DC1atUCIDk5mbJly3L//n1sbGxYvXo1n3/+OZcuXSpw/5MnT+Ll5UV2djampqaEhYXRoEEDAM6dO0e7du2IiYkp6iUVK9LdKUTxtPN8DMN+PImhgYotI5pT29VW15GEEFpU2O/vQk+wnt9MAuXLl8fCwgI/Pz/CwsIICwsDclrM8mNqaqox56eBgQFZWVlkZuYM8Ojr68u1a9cKlcfAwAAzMzPKlCmjXmZtbU1CQkIhr0gIIXSjQy1nutYpz2/h0UzceIZto1pgYlSkjg0hRAlW6CJtwYIF+S43NDTk0KFDHDp0CACVSlVgkdaiRQumTZvGqlWrMDEx4ZNPPqFy5crqsdLu3btH2bIF30jr7u5OREQEXl5eABw5coRKlSqp19+8eZPy5csX9pKEEEJnZnSvxZHIOC7FJPH13gjGta+q60hCCD1T6CItKirqhU/2xRdfEBAQQJkyZVCpVFhaWmpMlH7x4kWGDBlS4P4ffPABWVn/f6Ptv8dT++OPP0rV051CiOLL3sqUGT1qMWrtX3y9N4IOtZyp6SK3LQgh/t8L3ZNWFCkpKRw6dIi0tDSaNm2a70TponDknjQhijdFUfhg9Sl2nI+hlosNW0Y2x9hQuj2FKOle6RAcffr0yfcpyvnz5/P6668/dV8LCwvat29P165ddV6gBQUF0bhxY6ytrXF0dKRnz55cvqw5t56iKAQGBuLi4oK5uTn+/v6cP39eYxt/f39UKpXGqzDTUy1ZsgQPDw/MzMxo2LAhBw4ceKnXJ4TQbyqVipk9a1HGwpjzdxL5NjRS15GEEHqkSEVaaGgoXbp0ybO8Y8eO7N+/v1DH2L17N127dsXT0xMvLy+6du3Kn3/+WZQ4RRYaGsrIkSM5evQoISEhZGZmEhAQQHJysnqbefPm8eWXX7J48WKOHz+Os7Mz7du3JykpSeNY7733HtHR0erXt99++9Rzr1+/nrFjx/Lpp5/y119/0bJlSzp16sSNGzdeybUKIfSTo7UZ07vlzD6waHcEf99NesYeQojSokhF2qNHj/KdH9PY2PiZsw1AztRRHTt2xNramg8//JAxY8ZgY2ND586dWbx4cVEiFcmOHTsYMmQItWrVom7dugQHB3Pjxg1OnjwJ5LSiLVy4kE8//ZTevXvj7e3NqlWrSElJYe3atRrHsrCwwNnZWf2ytX36I/Vffvkl77zzDu+++y41atRg4cKFVKxYkaVLl76y6xVC6Kee9SrQtroj6VnZTNxwhsysbF1HEkLogSIVad7e3qxfvz7P8p9++omaNZ89H11QUBALFixg3bp1jBkzhjFjxrB27VoWLFjA7NmzixLppcgdviP3adOoqChiYmIICAhQb2NqakqrVq04fPiwxr5r1qzBwcGBWrVqMWHChDwtbU9KT0/n5MmTGscFCAgIyHPcJ6WlpZGYmKjxEkIUfyqVilm9amNtZsSZWwmsPPjiD2oJIYq/Qj/d+aSpU6fy2muvERkZqX6acvfu3axbt07jac2CJCYm0rFjxzzLAwICmDx5clEivTBFURg/fjwtWrRQPzWaOyiuk5OTxrZOTk5cv35d/f7NN9/Ew8MDZ2dnzp07x5QpUzhz5gwhISH5nuv+/ftkZWXle9ynDcQbFBTEjBkzinR9Qgj95mxrxtSuNZm0MZz/hvxNu5pOeJaz0nUsIYQOFaklrXv37mzZsoWIiAhGjBjBRx99xK1bt/jzzz/p2bNnofbfvHlznuVbt26lW7duRYn0wkaNGkV4eDjr1q3Ls+7JAXghp6B7ctl7771Hu3bt8Pb2pl+/fmzcuJE///yTU6dOPfWczzruv02ZMoWEhAT16+bNm4W5NCFEMfF6Q1f8qpYjPTObSRvDycrW6sP3Qgg9U6SWNIAuXbrk+/BAQZ6csaBGjRrMmjWLffv20axZMwCOHj3KoUOH+Oijj4oaqchGjx7Ntm3b2L9/P66ururlzs7OQE6L2pOD5MbGxuZpBXtSgwYNMDY25sqVK+opq57k4OCAoaFhnlazZx3X1NQUU1PTQl+XEKJ4UalUBPWuTYcF+zl5/QHfH77GOy08dB1LCKEjRS7Snte/ZywoW7YsFy5c4MKFC+plZcqU4bvvvuM///mPVjIpisLo0aPZvHkz+/btw8ND8z+GuV2YISEh1K9fH8i5nyw0NJS5c+cWeNzz58+TkZFR4OwHJiYmNGzYkJCQEHr16qVeHhISQo8ePV7ClQkhiqsKZcyZ0rk6n24+x/ydl2hXwxE3e0tdxxJC6ECRirSsrCwWLFjAzz//zI0bN0hPT9dYHx8fn2eflzFjwcs2cuRI1q5dy9atW7G2tla3bNna2mJubo5KpWLs2LHMnj2bKlWqUKVKFWbPno2FhQUDBgwAIDIykjVr1tC5c2ccHBy4cOECH330EfXr16d58+bqc7Vt25ZevXoxatQoAMaPH8/AgQNp1KgRzZo1Y9myZdy4cYPhw4dr/wchhNArA5pUYnt4NIcj45i0MZx17zXFwKDgWyGEECVTke5JmzFjBl9++SV9+/YlISGB8ePH07t3bwwMDAgMDCz0cdLT07l8+bJ6gnVtW7p0KQkJCfj7+1O+fHn168knVydNmsTYsWMZMWIEjRo14vbt2+zatQtra2sgp1Vs9+7ddOjQgWrVqjFmzBgCAgL4888/MTQ0VB8nMjKS+/fvq9+/8cYbLFy4kJkzZ1KvXj3279/P77//jpubm/Z+AEIIvaRSqZj7Wh3MjQ0Ji4pnTdj1Z+8khChxijQtlKenJ4sWLaJLly5YW1tz+vRp9bKjR4/mGUPs31JSUhg9ejSrVq0C4O+//6Zy5cqMGTMGFxcXPv7446JdTSkj00IJUbJ9fyiKwF8vYGFiyM6xflS0s9B1JCHES/BKp4WKiYmhdu3aAFhZWanHF+vatSvbt29/5v65Q1Ts27cPMzMz9fJ27drlO/6aEEKURoOaudPE3Y6U9CymbDqLlqdaFkLoWJGKNFdXV6KjowHw8vJi165dABw/frxQTx9u2bKFxYsX06JFC40hJ2rWrElkpMxdJ4QQAAYGKub2qYOpkQEHI+6z/rgMuyNEaVKkIq1Xr17s3r0bgA8//JCpU6dSpUoVBg0axNChQ5+5/71793B0dMyzPDk5+anjhAkhRGnj4WDJxA7VAJi1/SLRCak6TiSE0JYiPd05Z84c9f/v06cPFStW5NChQ3h5edG9e/dn7t+4cWO2b9/O6NGjgf8f1HX58uXqcdOEEELkeLu5B9vPRvPXjYdM2XSW4CGN5Q9aIUqBIhVp+/fvx9fXFyOjnN19fHzw8fEhMzOT/fv34+fn99T9g4KC6NixIxcuXCAzM5OvvvqK8+fPc+TIEUJDQ4sSSQghSixDAxXz+9Sh86KD7Lt8j19O3aZPQ9dn7yiEKNaK1N3ZunXrfMdCS0hIoHXr1s/c39fXl0OHDpGSkoKnpye7du3CycmJI0eO0LBhw6JEEkKIEs3L0Zqx7aoAMPPX88QmPtZxIiHEq1aklrSC5piMi4vD0rJwI2PXrl1bPQSHEEKIZ3u/ZWV2nIsh/FYCn245x7KBDaXbU4gS7LmKtN69ewM595ANGTJE40nOrKwswsPD8fX1fbkJhRBCAGBkaMD8PnXp+r8DhFy4y7Yzd+hRr4KuYwkhXpHn6u60tbXF1tYWRVGwtrZWv7e1tcXZ2Zn333+f1atXP/UYS5YsoV27dvTt25c9e/ZorLt//z6VK1d+/qsQQohSopqzNaPb5HR7Bm47z72kNB0nEkK8Ks/VkhYcHAxAuXLlCAwMxMIiZ/Tra9eusWXLFmrUqIGDg0OB+y9atIgpU6bw9ttvk5CQQOfOnZk+fTpTpkwBclrjrl+X6U+EEOJpPvD35I9zMVyMTmT6tnMseVPu5RWiJCrSgwN//fUXP/zwAwAPHz6kadOm/Pe//6Vnz54sXbq0wP2+/fZbli9fzuLFi/nxxx/Zu3cvCxcuZNq0aUVLL4QQpZCxoQHz+9TByEDF72dj+P1stK4jCSFegSIXaS1btgRg48aNODk5cf36dX744QcWLVpU4H5RUVEa96w1a9aMPXv2sGzZMnVrmhBCiGfzrmDLB/6eAEzbeo745HQdJxJCvGxFKtJSUlKwtrYGYNeuXfTu3RsDAwOaNm361O5KBwcHbt7UnNakVq1a7Nmzh+DgYCZOnFiUOEIIUSqNauNFVScr7j9KZ8av53UdRwjxkhWpSPPy8mLLli3cvHmTnTt3EhAQAEBsbOxTZ3Nv0aIFv/zyS57lNWvWZPfu3ezYsaMocYQQolQyNTJkfp+6GKhg6+k7hFy4q+tIQoiXqEhF2rRp05gwYQLu7u74+Piop3LatWsX9evXL3C/jz/+mLp16+a7rlatWuzdu1fuTxNCiOdQt2IZ3vPLeSr+081nSUjJ0HEiIcTLolIURSnKjjExMURHR1O3bl0MDHJqvWPHjmFjY0P16tVfakiRv8TERGxtbUlISHhqC6YQomR7nJFF50UHuHovmT4NXfni9fz/GBZC6IfCfn8XqSUNwNnZmfr166sLNIAmTZoUukA7cOAAb731Fs2aNeP27dsA/Pjjjxw8eLCokYQQolQyMzZkfp86qFSw8eQt9l6O1XUkIcRLUOQi7UX88ssvdOjQAXNzc/766y/S0nIGY0xKSmL27Nm6iCSEEMVaQzc7hjb3AOCTTWdJfCzdnkIUdzop0j7//HO++eYbli9fjrGxsXq5r68vp06d0kUkIYQo9iYEVMPN3oLohMcE/X5R13GEEC9IJ0Xa5cuX8fPzy7PcxsaGhw8faj+QEEKUAOYmhsx9rQ4A647d5OCV+zpOJIR4ETop0sqXL09ERESe5QcPHpS5O4UQ4gU0rWzPoGZuAHy8KZzktEwdJxJCFJVOirRhw4bx4YcfEhYWhkql4s6dO6xZs4YJEyYwYsQIXUQSQogSY3LH6riWNefWg1Tm7rik6zhCiCJ6rgnWX5ZJkyaRkJBA69atefz4MX5+fpiamjJhwgRGjRqli0hCCFFiWJoaMfe1Ory5Iowfjlync+3yNK1sr+tYQojnVORx0l6GlJQULly4QHZ2NjVr1sTKykpXUYolGSdNCPE0UzaFs+7YTdzsLdjxoR/mJoa6jiSEQAvjpL0MFhYWNGrUiCZNmkiBJoQQL9mUzjUob2vG9bgUvth1WddxhBDPSetF2pkzZxg0aBCVK1fG3NwcKysrateuzdSpU0lMTNR2HCGEKLFszIwJ6l0bgO8ORXHyeryOEwkhnodWi7SdO3fSrFkzkpKSaNq0KQYGBrz99tt06dKFn376iQYNGhATE6PNSEIIUaL5V3OkT0NXFAUmbgzncUaWriMJIQpJq/ek1a9fn2HDhjF8+HAAQkJCGDNmDBcvXiQjI4NOnTpRsWJFgoODtRWpWJN70oQQhZGQkkH7BaHEJqUxrFVlpnSqoetIQpRqenlP2qVLl+jYsaP6fbt27YiMjCQ6OhpjY2OmT5/O9u3btRlJCCFKPFsLY2b1yun2XL7/KqdvPtRtICFEoWi1SKtQoQKXL///zauRkZFkZ2djb5/zaLirqyuPHj3SZiQhhCgV2td0okc9F7IVmLTxDGmZ0u0phL7TapE2aNAg3n33Xb755huCg4Pp1asX3bt3x8TEBIDTp0/j4eGhzUhCCFFqBHarhYOVCX/ffcTiPXlnfRFC6BetDmb7ySefkJyczGeffUZaWhodOnTgq6++Uq+vUKECS5cu1WYkIYQoNcpamvBZD28+WHOKJfsi6VDLGe8KtrqOJYQogE4HsxUvRh4cEEIUxcg1p9h+Npoa5W3YOrI5JkY6HTJTiFJHLx8ceFJWVhZ3797l/v37uooghBCl0owetShrYczF6ESW7ovUdRwhRAG0XqRt374dPz8/LC0tcXFxwcnJiTJlyjBw4EBu3Lih7ThCCFHqOFiZEti9FgCL917hUowMJC6EPtJqkfbjjz/Sv39/GjZsyLhx4yhXrhyTJk1izpw53Lx5k4YNG3LlyhVtRhJCiFKpe10X2td0IiNLYeKGcDKzsnUdSQjxL1q9J61GjRoEBgbyxhtvAHDixAl69erFjRs3UKlU9OvXj/T0dDZt2qStSMWa3JMmhHgRsYmPab9gPwmpGUzqWI0R/l66jiREqaCX96Rdv34dHx8f9ftGjRoRExNDdHQ0AOPHj2fv3r1ayxMUFETjxo2xtrbG0dGRnj17aozjBqAoCoGBgbi4uGBubo6/vz/nz5/P93iKotCpUydUKhVbtmx56rkDAwNRqVQaL2dn55d1aUII8UyONmZM61oTgIUhV4iITdJxIiHEk7RapLm7u3PixAn1+1OnTmFgYICTkxMAdnZ2ZGRkaC1PaGgoI0eO5OjRo4SEhJCZmUlAQADJycnqbebNm8eXX37J4sWLOX78OM7OzrRv356kpLz/MVu4cCEqlarQ569VqxbR0dHq19mzZ1/KdQkhRGH1blAB/2rlSM/KZuLGcLKy5YF/IfSFVsdJGzlyJO+++y7Hjx/HzMyMFStWMHDgQAwNDQEICwujatWqWsuzY8cOjffBwcE4Ojpy8uRJ/Pz8UBSFhQsX8umnn9K7d28AVq1ahZOTE2vXrmXYsGHqfc+cOcOXX37J8ePHKV++fKHOb2RkJK1nQgidUqlUBPWuTcCX+/nrxkOCD0XxbsvKuo4lhEDLLWkjR44kKCiIgwcPsn37doYMGcLixYvV65s0acLatWu1GUlDQkICkNOiBxAVFUVMTAwBAQHqbUxNTWnVqhWHDx9WL0tJSaF///4sXrz4uYquK1eu4OLigoeHB/369ePq1atP3T4tLY3ExESNlxBCvKjytuZ82iVn0vX5Oy8TdT/5GXsIIbRB60NwfPDBBxw6dIgTJ04wa9YszMzM1OuqVKlC9erVtR0JyLmfbPz48bRo0QJvb28AYmJiANTdsbmcnJzU6wDGjRuHr68vPXr0KPT5fHx8+OGHH9i5cyfLly8nJiYGX19f4uLiCtwnKCgIW1tb9atixYrPc4lCCFGgNxpXpIWXA2mZ2UzeGE62dHsKoXMyzPQ/Ro0aRXh4OOvWrcuz7t/3mSmKol62bds29uzZw8KFC5/rfJ06deK1116jdu3atGvXju3btwM53akFmTJlCgkJCerXzZs3n+ucQghRkNxuTwsTQ45di+eHI9d0HUmIUk8nRVpWVhZffPEFTZo0wdnZGTs7O42Xto0ePZpt27axd+9eXF1d1ctzuy6fbDUDiI2NVbeu7dmzh8jISMqUKYORkRFGRjm3+b322mv4+/sXOoOlpSW1a9d+6jhxpqam2NjYaLyEEOJlqWhnwZROOb0Zc3dc5mZ8io4TCVG66aRImzFjBl9++SV9+/YlISGB8ePH07t3bwwMDAgMDNRaDkVRGDVqFJs2bWLPnj14eHhorPfw8MDZ2ZmQkBD1svT0dEJDQ/H19QXg448/Jjw8nNOnT6tfAAsWLCA4OLjQWdLS0rh48WKhHzoQQohX4U0fN5pWtiM1I4vJv4Qj0zsLoTs6KdLWrFnD8uXLmTBhAkZGRvTv358VK1Ywbdo0jh49qrUcI0eOZPXq1axduxZra2tiYmKIiYkhNTUVyGn+Hzt2LLNnz2bz5s2cO3eOIUOGYGFhwYABA4Cc1jZvb2+NF0ClSpU0ir62bdtqPCQxYcIEQkNDiYqKIiwsjD59+pCYmMjgwYO1dv1CCPFvBgYq5r5WB3NjQw5HxrH2mEzXJ4Su6KRIi4mJoXbt2gBYWVmpn6rs2rWr+t4sbVi6dCkJCQn4+/tTvnx59Wv9+vXqbSZNmsTYsWMZMWIEjRo14vbt2+zatQtra+vnOldkZKTGZPK3bt2if//+VKtWjd69e2NiYsLRo0dxc3N7adcnhBBF4WZvycQO1QAI+v0Stx+m6jiREKWTVsdJy+Xq6kp0dDSVKlXCy8uLXbt20aBBA44fP46pqanWchSmGV+lUhEYGPhc3bD5HffatWsa73/66adCH08IIbRtsK87289Gc/L6Az7+JZwfhjZ5rsG6hRAvTictab169WL37t0AfPjhh0ydOpUqVaowaNAghg4dqotIQgghnmBooGJenzqYGhlw4Mp9Npy8petIQpQ6Wp1gvSBHjx7l8OHDeHl50b17d13HKTZkgnUhxKv2bWgkQX9cwtrMiJBxrXC2NXv2TkKIpyrs97deFGmiaKRIE0K8alnZCr2XHubMzYe0re7IisGNpNtTiBdU2O9vndyTluvChQvcuHGD9PR0jeXSmiaEEPrB0EDF/D516LroILsvxbLl9G161Xd99o5CiBemkyLt6tWr9OrVi7Nnz6JSqdQ32uf+dZaVlaWLWEIIIfJR1cmaMW29+GLX3wRuu0BzLwccraXbU4hXTScPDnz44Yd4eHhw9+5dLCwsOH/+PPv376dRo0bs27dPF5GEEEI8xbBWntRysSEhNYOpW87JILdCaIFOirQjR44wc+ZMypUrh4GBAQYGBrRo0YKgoCDGjBmji0hCCCGewtjQgPl96mJkoGLn+btsPxut60hClHg6m7vTysoKAAcHB+7cuQOAm5sbly9f1kUkIYQQz1DTxYaRrb0AmLb1PHGP0nScSIiSTSdFmre3N+Hh4QD4+Pgwb948Dh06xMyZM6lcubIuIgkhhCiEka29qO5sTXxyOtO3ndd1HCFKNJ0Uaf/5z3/Izs4G4PPPP+f69eu0bNmS33//nUWLFukikhBCiEIwMcrp9jQ0UPFbeDQ7zsXoOpIQJZbejJMWHx9P2bJlZfyd5yDjpAkhdGXejkss2ReJg5Upf473o4yFia4jCVFsFPb7Wyctaf+WmZmJnZ2dFGhCCFFMjGlbBS9HK+4/SmPmrxd0HUeIEkmrRdqOHTs4e/YsANnZ2Xz++edUqFABU1NTXF1dmTNnjjzWLYQQxYCZsSHz+9TBQAWb/rrNnkt3dR1JiBJHq0XaRx99RFJSEgBz585l4cKFTJgwge3btzNx4kQWLlzIvHnztBlJCCFEEdWvVJZ3W+Y87DVl01kSUjN0nEiIkkWr96SZm5vz999/U7FiRWrXrs3UqVPp27evev327dsZO3YsV65c0VakYk3uSRNC6NrjjCw6fXWAqPvJvNGoInP71NF1JCH0nl7ek1a2bFlu374NwL1796hSpYrG+qpVq6rXCyGE0H9mxobM61MHlQrWn7jJ/r/v6TqSECWGVou0Xr16MWvWLLKysujRowdLlizRuAdt8eLF1KtXT5uRhBBCvKDG7nYMbuYO5HR7PkrL1G0gIUoIrRZps2fPJiYmhurVq5Oamsrq1avx8PAgICCAypUr8/3337NgwQJtRhJCCPESTOpYjYp25tx+mErQ7xd1HUeIEkGrRZqtrS2HDx/mo48+Ii4uDnd3d0xNTUlPT6d///6cP38eHx8fbUYSQgjxEliYGDH3tZz70daE3eBwxH0dJxKi+NObwWzF85MHB4QQ+ubTzWdZE3aDinbm7Bzrh4WJka4jCaF39PLBASGEECXblM41qFDGnJvxqczbcVnXcYQo1nT2J87GjRv5+eefuXHjBunp6RrrTp06paNUQgghXoSVqRFBvWsz6LtjrDpyjS51ytPY3U7XsYQolnTSkrZo0SLefvttHB0d+euvv2jSpAn29vZcvXqVTp066SKSEEKIl8SvajneaFQRRYFJG8NJTc/SdSQhiiWdFGlLlixh2bJlLF68GBMTEyZNmkRISAhjxowhISFBF5GEEEK8RJ90qYGTjSlR95P5MkS6PYUoCp0UaTdu3MDX1xfImYUgd6qogQMHsm7dOl1EEkII8RLZmhsT1Ls2ACsPRnHqxgMdJxKi+NFJkebs7ExcXBwAbm5uHD16FICoqCiZYF0IIUqINtWd6F2/Atn/dHs+zpBuTyGeh06KtDZt2vDrr78C8M477zBu3Djat2/PG2+8Qa9evXQRSQghxCswrVtNylmbEhH7iEW7ZV5mIZ6HTsZJy87OJjs7GyOjnIdLf/75Zw4ePIiXlxfDhw/HxMRE25GKJRknTQhRHOw8H8OwH09iaKBiy4jm1Ha11XUkIXSqsN/fMphtMSZFmhCiuBi19hS/hUdT3dmabaNaYGIkw3SK0ktvB7ONjY1l7969JCYmAnD37l3mzZvHnDlzOHfunLbjCCGE0IIZ3Wthb2nCpZgkvt4boes4QhQLWi3S9u3bR+XKlWnbti3Vq1cnPDycRo0asWLFCr7//nsaNWrErl27tBlJCCGEFthbmTKjRy0Avt4bwYU7iTpOJIT+02qR9p///IchQ4aQmJjI+PHj6dKlCz169ODvv//m0qVLjB49mhkzZmgzkhBCCC3pUrs8HWs5k5mtMHHjGTKysnUdSQi9ptV70mxtbTl16hSenp5kZmZibm7O8ePHqVevHgBXrlyhcePGPHz4UFuRijW5J00IUdzEJj0mYMF+HqZkMCGgKqPaVNF1JCG0Ti/vSTMxMeHx48cApKenk52drX4PkJqairGxsTYjCSGE0CJHazOmd6sJwKLdEfx9N0nHiYTQX1ot0po3b87HH3/MoUOHGDduHA0aNODzzz8nOTmZlJQUPvvsMxo1aqTNSEIIIbSsZ70KtK3uSHpWNhM3hpMp3Z5C5EurRdr8+fO5dOkSLVu25NChQ2zduhVDQ0PKlCmDra0toaGhzJo1S5uRhBBCaJlKpWJWr9pYmxlx5uZDVh6M0nUkIfSSTsZJi4uLw97eXv1+9+7dpKam0qxZM43l4unknjQhRHH284mbTNoYjomRAX982BLPcla6jiSEVshgtqWAFGlCiOJMURQGBx9n/9/3aOhWlp+HNcPQQKXrWEK8cnr54ADAmTNnCA4OJioqp3n7/PnzjBgxguHDh7Nz506tZgkKCqJx48ZYW1vj6OhIz549uXz5ssY2iqIQGBiIi4sL5ubm+Pv7c/78+XyPpygKnTp1QqVSsWXLlmeef8mSJXh4eGBmZkbDhg05cODAy7gsIYQoFlQqFUG9a2NlasTJ6w9YdfiariMJoVe0WqT98ssvNGzYkIkTJ1K3bl12795NixYtuHLlCteuXaNLly6sXbtWa3lCQ0MZOXIkR48eJSQkhMzMTAICAkhOTlZvM2/ePL788ksWL17M8ePHcXZ2pn379iQl5X0iaeHChahUhfsrcP369YwdO5ZPP/2Uv/76i5YtW9KpUydu3Ljx0q5PCCH0XYUy5kzpXB2AeTsvcT0u+Rl7CFGKKFrUoEED5fPPP1cURVHWrVunlClTRpk5c6Z6/RdffKHUq1dPm5E0xMbGKoASGhqqKIqiZGdnK87OzsqcOXPU2zx+/FixtbVVvvnmG419T58+rbi6uirR0dEKoGzevPmp52rSpIkyfPhwjWXVq1dXPv7440LnTUhIUAAlISGh0PsIIYS+yc7OVvovO6K4Tf5N6fvNYSUrK1vXkYR4pQr7/a3VlrTLly/z5ptvAvDGG2+QnJxMz5491et79epFRITu5nRLSEgAwM7ODoCoqChiYmIICAhQb2NqakqrVq04fPiwellKSgr9+/dn8eLFODs7P/M86enpnDx5UuO4AAEBARrH/be0tDQSExM1XkIIUdypVCrmvlYHc2NDwqLiWRN2XdeRhNALWi3SrK2tiYuLA+Dhw4dkZmaq30POU59WVrp5ukdRFMaPH0+LFi3w9vYGICYmBgAnJyeNbZ2cnNTrAMaNG4evry89evQo1Lnu379PVlbWM4/7b0FBQdja2qpfFStWLNT5hBBC31W0s2Byx2oABP1xiZvxKTpOJITuabVIa9euHSNHjmTNmjUMHjyYDh06MGXKFC5dusTly5eZOHEiLVq00GYktVGjRhEeHs66devyrPv3fWaKoqiXbdu2jT179rBw4cLnPufTjpufKVOmkJCQoH7dvHnzuc8phBD6alAzd5q425GSnsWUTWdRZPABUcpptUj74osvsLa2Zvjw4WRmZrJ+/XoaNWpEzZo1qVmzJnfu3GHOnDnajATA6NGj2bZtG3v37sXV1VW9PLfr8t+tW7GxsepWsD179hAZGUmZMmUwMjLCyMgIgNdeew1/f/98z+fg4IChoeFTj5sfU1NTbGxsNF5CCFFSGBiomNunDqZGBhyMuM/64/KHqCjdtFqkOTk5ERISQlJSEr///js2Njb873//IyIigjNnznDhwgU8PT21lkdRFEaNGsWmTZvYs2cPHh4eGus9PDxwdnYmJCREvSw9PZ3Q0FB8fX0B+PjjjwkPD+f06dPqF8CCBQsIDg7O97wmJiY0bNhQ47gAISEh6uMKIURp5OFgycQOOd2es7ZfJDohVceJhNAdI10HAKhcubJOzjty5EjWrl3L1q1bsba2Vrds2draYm5ujkqlYuzYscyePZsqVapQpUoVZs+ejYWFBQMGDAByWtvye1igUqVKGkVf27Zt6dWrF6NGjQJg/PjxDBw4kEaNGtGsWTOWLVvGjRs3GD58uBauXAgh9NfbzT3Yfjaav248ZMqmswQPaVzo4Y2EKEm0VqQtWrSo0NuOGTPmFSb5f0uXLgXI0y0ZHBzMkCFDAJg0aRKpqamMGDGCBw8e4OPjw65du7C2tn6uc0VGRnL//n31+zfeeIO4uDhmzpxJdHQ03t7e/P7777i5ub3QNQkhRHFnaKBifp86dF50kH2X77Hp1G1ea+j67B2FKGG0Ni3Uv7sS7927R0pKCmXKlAFynva0sLDA0dGRq1evaiNSsSfTQgkhSrIl+yKYt+MyNmZG/Dm+FY42ZrqOJMRLoXfTQkVFRalfs2bNol69ely8eJH4+Hji4+O5ePEiDRo04LPPPtNWJCGEEHrs/ZaVqeNqS+LjTD7dck6e9hSljk4mWPf09GTjxo3Ur19fY/nJkyfp06ePel5P8XTSkiaEKOkuxyTR9X8HyMhS+KpfPXrUq6DrSEK8ML1rSXtSdHQ0GRkZeZZnZWVx9+5dHSQSQgihj6o5WzO6TRUAAred515Smo4TCaE9OinS2rZty3vvvceJEyfUzdcnTpxg2LBhtGvXTheRhBBC6KkP/D2pUd6GBykZBG47r+s4QmiNToq07777jgoVKtCkSRPMzMwwNTWlSZMmlC9fnhUrVugikhBCCD1lbGjA/D51MDJQsf1sNH+cjdZ1JCG0Qif3pOW6cuUKFy9eRFEUatSoQdWqVXUVpViSe9KEEKXJf3dd5n97InCwMmHXuFbYWZroOpIQRVLY72+tD2Z769Ytli5dyuHDh4mJiUGlUuHk5ETz5s0ZPny4xrRMQgghRK5RbbzYeT6Gv+8+Ysav5/mqX/1n7yREMabV7s6DBw9So0YNNm/eTN26dRk0aBBvvfUWdevWZfPmzdSsWZNDhw5pM5IQQohiwtTIkPl96mKggq2n7xByQR40EyWbVrs7GzduTIsWLViwYEG+68eNG8fBgwc5fvy4tiIVa9LdKYQojYL+uMi3oVdxtDYlZFwrbC2MdR1JiOeil0NwnDt37qlzUw4bNoxz585pMZEQQojiZly7qlQuZ0lsUhqfbb+g6zhCvDJaLdLKly/P4cOHC1x/5MgRypcvr8VEQgghihszY0Pm96mDSgUbT95i7+VYXUcS4pXQ6oMDEyZMYPjw4Zw8eZL27dvj5OSESqUiJiaGkJAQVqxYwcKFC7UZSQghRDHU0M2Ooc09WHkwik82nWXnOD9szKTbU5QsWh+CY/369SxYsICTJ0+SlZUFgKGhIQ0bNmT8+PH07dtXm3GKNbknTQhRmqWmZ9Hxq/1cj0vBtaw5Lbwc8KlsRxMPeyqUMdd1PCEKVNjvb52Nk5aRkcH9+/cBcHBwwNhY/gJ6XlKkCSFKuxPX4hkSfJxHaZkay13LmtPEw46mHvb4VLajkp0FKpVKRymF0KT3RZp4cVKkCSEEJKRmcDwqnrCoOI5FxXPuTiJZ2ZpfbU42pvh42OcUbpXt8CxnJUWb0Bkp0koBKdKEECKvR2mZnLz+gLCrOUXbmVsPycjS/KqztzShiYcdTTzs8PGwp7qzNQYGUrQJ7ZAirRSQIk0IIZ4tNT2Lv24+IOxqPMei4jl14wFpmdka29iYGWkUbbVcbDAy1Mn01qIUkCKtFJAiTQghnl9aZhZnbyUQFhVPWFQ8J6/Fk5yepbGNpYkhDd3t8PHIedV2tcXUyFBHiUVJI0VaKSBFmhBCvLjMrGzO30lU39N2LCqexMeaDyKYGhnQoFLZnJa2ynbUr1gWcxMp2kTRSJFWCkiRJoQQL19WtsLlmCSNoi0uOV1jG2NDFXVdy/xTtNnT0K0sVqZaHXpUFGNSpJUCUqQJIcSrpygKkfcecfSfe9rCouK4m5imsY2hgQpvFxv1PW2N3e1kTlFRICnSSgEp0oQQQvsUReFGfAphV+M5+k9r260HqRrbqFRQ3dlGfU9bYw87HKxMdZRY6Bsp0koBKdKEEEI/3H6YyrF/Crawq/FcvZ+cZxsvR6t/WtpyWtucbc10kFToAynSSgEp0oQQQj/FJj1WF2zHouK5fDcpzzZu9hY0cc+5p83Hww7XsuYywG4pIUVaKSBFmhBCFA/xyekcv/ZP0XYtjgt3EvnXpAi42JqpH0Ro4mFHZQdLKdpKKCnSSgEp0oQQonhKfJzByWsP1Pe0nb2VQOa/qjYHK9OcrtHKOd2jVRytZFaEEkKKtFJAijQhhCgZUtIzOXX9IWFRcYRFxXP65kPS/zUrQlkLYxq7//+sCDVdbDCUoq1YkiKtFJAiTQghSqbHGVmcufmQsH/GaTt5/QGpGZqzIlibGtHQvSw+Hvb4VLajdgVbjGUqq2JBirRSQIo0IYQoHdIzszl3J+GfBxHiOHHtAUlpmrMimBsb0tCtrPoJ0roVy2BmLLMi6CMp0koBKdKEEKJ0yspWuBidyNGr/8yKcC2ehykZGtuYGBlQr2IZ9ZAfDdzKYGEisyLoAynSSgEp0oQQQgBkZytciX2kvqct7Go89x9pzopgZKCitqstTTzsaOphT0P3stiYyawIuiBFWikgRZoQQoj8KIpC1P1k9T1tYVfjuJPwWGMbAxXUdLGhiXvOPW1N3O0oa2mio8SlixRppYAUaUIIIQpDURRuPUj9p2jLaW27HpeSZ7tqTtb/jNWW8xSpo7XMivAqSJFWCkiRJoQQoqhiEh4TljuVVVQ8EbGP8mxT2cHyiaLNngplzHWQtOSRIq0UkCJNCCHEy3L/URrH/ynYwqLiuRSTyL8rBNey5up72pp42OFmbyGzIhSBFGmlgBRpQgghXpWElIycqaz+aW07dyeRrH/NiuBkY0oTD/t/niC1w8vRSoq2QpAirRSQIk0IIYS2PErL5OT1B4T9M+zHmVsPycjSLCHsLU1o7P7/97TVcLaRqazyUdjv71I9NHFQUBCNGzfG2toaR0dHevbsyeXLlzW2URSFwMBAXFxcMDc3x9/fn/Pnz2tsM2zYMDw9PTE3N6dcuXL06NGDS5cuPfXcgYGBqFQqjZezs/NLv0YhhBDiZbAyNaJV1XJM6lidjR/4Ej69A2vf8+HDtlVoVtkeUyMD4pLT2XE+hhm/XqDLooPUm7mLd74/zrL9kZy++ZDMrOxnn0iolepR7UJDQxk5ciSNGzcmMzOTTz/9lICAAC5cuIClpSUA8+bN48svv+T777+natWqfP7557Rv357Lly9jbW0NQMOGDXnzzTepVKkS8fHxBAYGEhAQQFRUFIaGBY/2XKtWLf7880/1+6dtK4QQQugTcxNDfD0d8PV0ACAtM4uztxLU97SdvBZP4uNMdl+KZfelWAAsTQxp6G6n7h6t7WqLqZF89xVEujufcO/ePRwdHQkNDcXPzw9FUXBxcWHs2LFMnjwZgLS0NJycnJg7dy7Dhg3L9zjh4eHUrVuXiIgIPD09890mMDCQLVu2cPr06SLnle5OIYQQ+iozK5vzdxLV97Qdi8op2p5kamRAg0pl1U+Q1q9YFnOTkl+0Ffb7u1S3pP1bQkICAHZ2dgBERUURExNDQECAehtTU1NatWrF4cOH8y3SkpOTCQ4OxsPDg4oVKz71fFeuXMHFxQVTU1N8fHyYPXs2lStXLnD7tLQ00tL+fwTpxMTE57o+IYQQQluMDA2oW7EMdSuW4X0/T7KyFS7HJGkUbXHJ6Ry5GseRq3GwG4wNVdR1LfNP0WZPQ7eyWJmW3lKl9F75vyiKwvjx42nRogXe3t4AxMTEAODk5KSxrZOTE9evX9dYtmTJEiZNmkRycjLVq1cnJCQEE5OCR2728fHhhx9+oGrVqty9e5fPP/8cX19fzp8/j729fb77BAUFMWPGjBe5TCGEEEInDA1U1HSxoaaLDW8390BRFCLvPeLo1X9mRYiK425iGieuP+DE9Qcs2ReJoYEKbxebfyaNt6exux22FqVnKivp7vzHyJEj2b59OwcPHsTV1RWAw4cP07x5c+7cuUP58uXV27733nvcvHmTHTt2qJclJCQQGxtLdHQ0X3zxBbdv3+bQoUOYmRVutObk5GQ8PT2ZNGkS48ePz3eb/FrSKlasKN2dQgghij1FUbgRn0LY1dyx2uK49SBVYxuVCqo726jvaWvsYYeDlamOEheddHc+h9GjR7Nt2zb279+vLtAA9dOWMTExGkVabGxsntY1W1tbbG1tqVKlCk2bNqVs2bJs3ryZ/v37FyqDpaUltWvX5sqVKwVuY2pqiqlp8fswCiGEEM+iUqlws7fEzd6Svo1zbhe6/TCVY7mzIlyN5+r9ZC5GJ3IxOpHvD18DwMvR6p+WtpzWNmfbkjOVVaku0hRFYfTo0WzevJl9+/bh4eGhsd7DwwNnZ2dCQkKoX78+AOnp6YSGhjJ37txnHvvJVq9nSUtL4+LFi7Rs2fL5L0QIIYQogSqUMadXfVd61c9pQIlNeqwu2I5FxXP5bhIRsY+IiH3E2rAbALjZW9DEPeeeNh8PO1zLmhfbAXZLdZE2cuRI1q5dy9atW7G2tlbfg2Zra4u5ec4/6tixY5k9ezZVqlShSpUqzJ49GwsLCwYMGADA1atXWb9+PQEBAZQrV47bt28zd+5czM3N6dy5s/pcbdu2pVevXowaNQqACRMm0K1bNypVqkRsbCyff/45iYmJDB48WPs/CCGEEKIYcLQ2o2sdF7rWcQEgPjk9Z1aEq/EcuxbHhTuJXI9L4XpcChtO3gLAxdZM/SBCEw87KjtYFpuirVQXaUuXLgXA399fY3lwcDBDhgwBYNKkSaSmpjJixAgePHiAj48Pu3btUo+RZmZmxoEDB1i4cCEPHjzAyckJPz8/Dh8+jKOjo/qYkZGR3L9/X/3+1q1b9O/fn/v371OuXDmaNm3K0aNHcXNze7UXLYQQQpQQdpYmdKjlTIdaObcnJT7O4OS1Bxz9p4v07K0E7iQ8ZsvpO2w5fQcAByvTnK7Ryjndo1UcrfR2VgR5cKAYk3HShBBCiIKlpGdy6vpDwqLiCIuK5/TNh6Rnas56UNbCmMbuduonSGu62GD4ios2mbuzFJAiTQghhCi8xxlZnLn5kLB/xmk7ef0BqRlZGttYmxrR0L0sPh453aN1XG0xNny5s2hKkVYKSJEmhBBCFF16Zjbn7iT88yBCHCeuPSApTXNWhNCJ/rjZW77U88oQHEIIIYQQT2Hyz7RUDSqV5QP/nFkRLkYncvRqzj1tN+JTqGRnobN80pJWjElLmhBCCFH8FPb7++V2sgohhBBCiJdCijQhhBBCCD0kRZoQQgghhB6SIk0IIYQQQg9JkSaEEEIIoYekSBNCCCGE0ENSpAkhhBBC6CEp0oQQQggh9JAUaUIIIYQQekiKNCGEEEIIPSRFmhBCCCGEHpIiTQghhBBCD0mRJoQQQgihh6RIE0IIIYTQQ0a6DiCKTlEUABITE3WcRAghhBCFlfu9nfs9XhAp0oqxpKQkACpWrKjjJEIIIYR4XklJSdja2ha4XqU8q4wTeis7O5s7d+5gbW2NSqV6acdNTEykYsWK3Lx5Exsbm5d2XG2T69Avch36paRcB5SMaykJ15CrpFzLq7wORVFISkrCxcUFA4OC7zyTlrRizMDAAFdX11d2fBsbm2L9C5ZLrkO/yHXol5JyHVAyrqUkXEOuknItr+o6ntaClkseHBBCCCGE0ENSpAkhhBBC6CEp0kQepqamTJ8+HVNTU11HeSFyHfpFrkO/lJTrgJJxLSXhGnKVlGvRh+uQBweEEEIIIfSQtKQJIYQQQughKdKEEEIIIfSQFGlCCCGEEHpIijQhhBBCCD0kRZooth4/fqzrCKIEks+VeBXkcyWKQoq0UiQ+Pp779+8DOVNKFVdRUVHUrVuX2bNn6zrKC7l58ya//vorZ8+eJSsrC3j2ZLv6qiR8tkrK5wpKzmdLPlf6RT5X2idFWinx6aefUr16dZYtWwbw1LnC9JWiKAwfPpyqVatStWpVxowZo+tIRTZhwgSqV6/OV199RYsWLRg9ejRXr15FpVIVu//oFffPVkn6XEHJ+WzJ50q/yOdKN/Q7nXhhDx8+5J133uHPP/+kUqVKHD16lOPHjwPF6y+giIgI7O3tOXjwIMeOHWPDhg04ODjoOlaRfPfddxw+fJidO3eyY8cOVqxYwblz5xg6dCgAKpVKxwkLpyR8tkrS5wpKxmdLPlf6Rz5XuiNFWgn05AfO3NwcNzc3pkyZwn//+19u377N5s2bycjI0Pu/gJ7MZmxsjIuLCy1atKB+/focPnyYjz76iNmzZ7Njxw6SkpJ0mPTpcq8j939/+eUXPD09adGiBUZGRrz++uvUq1eP/fv3s2LFCo1t9U1J+GyVlM8VlJzPlnyu9It8rvSIIkqUlJQU5fHjx+r32dnZysP/a+/eg6Kq3ziOPwsEskggumLiBcQFBylRMw1UYIbUyLyhkuGsqIGYozYDmtOUjpfRtCTIW6M2iJhRpmZex2YUG6whxPsF8Rp5RQHxxmXd/fz+oHNyuWjqsufs9/e8/oqzy855j8/Sd8/u2XPnjvxzcnIywsLCsHPnTvl2NarbYTKZsHnzZmg0GgwcOBAdO3ZETEwMunXrBh8fHxgMBgX3tnF1O8rLyxEdHY1PPvkEJpNJ3j5jxgwEBgaiVatWMBqNSuzqU4kwW6LMFSDObPFcqQvPlbrwIk0gs2bNQo8ePRAVFYX09HRUVFQAqB0+6cn1119/ITQ0FAkJCbh165Z8u5o01lFWVgaDwYCwsDAcO3ZMfgKuXr0agYGBWLlypZK7XU/djvLycgC1fxxef/11zJ07F7dv38aMGTOg0+mwceNGvPLKK1i1apWyO94AEWZLlLkCxJktnit14blSz1xJeJEmgOrqaowcORJBQUHIzs6GwWBAUFAQ3nnnHYv7ScOZlpaGnj17IiMjQ75NDcPZWEd0dLR8nzNnziA/P9/iyVZaWorBgwcjMTERjx49Umr3ZY11DBw4EABw7949TJ8+HZ07d4aXlxeCg4ORl5cHAOjbty9SU1OV3H0LIsyWKHMFiDNbPFc8V01BhLmqixdpAjh9+jT0ej327t0rb8vNzYWrqyuWLFkiD500mFVVVYiOjsbo0aNx/PhxbNiwAQsWLFBk3x/3tI6GSG2dO3fG5MmTbbKfT/OkjkWLFsnb/v77bxw/flz+uaqqCjqdDitWrLDp/j6JCLMlylwB4swWzxXPVVMQYa7q4kWaAAoKCqDRaFBaWgrg3z8EixYtQosWLVBUVCTfVxrOn3/+GZ06dULLli3h7OyML7/80vY7XsezdDxu9+7d6NWrFw4ePGizfX2Sp3WcPXvW4v7S7evXr0fv3r1x7do12+7wE4gwW6LMFSDObPFc8Vw1BRHmqi5epAngyJEj6Nq1K5YtWwbg38GsqamBn58fkpOTAUA+tH7+/HkYDAZoNBpMnjwZ9+/fV2bH6/ivHSaTCSdOnMC+ffswadIkeHh4YNasWap56+BZ/j1u376NLVu2IDExEW5ubpg3bx7MZrNqDrmLMFuizBUgzmzxXPFcNQUR5qouXqTZiXv37jV6W1lZGYYNG4bY2Fj5FY10ts3SpUvRtm3bemfltGvXzuKwta1YqyMzMxORkZGIjIzE0aNHm37H67BWR0lJCVJSUhAVFaVIx9PY02w1xp7m6mnsZbae9j9se5grazYoPVfWbOG5si1epKnc2bNnER4ejpSUFACwePX1+GnP3377Lbp164a0tDSL31+7di26du2Ky5cvy8P5+JDairU6Ll68CAC4f/8+zp8/b4M9t2StjkuXLsnb7t6927Q73Yji4mIcOnQIV69erXebvcyWtRqUnivAei1Kz1ZJSYnFEYnHZ8Je5spaDWqYK2u1KD1XN2/elM/SBOxzrp4Hf5mtStXU1NC4ceOoa9eudOjQIcrJySEiIkdHR/laY05OTlRVVUXZ2dk0YcIECgkJoR9++IH2798vP86VK1dIp9NRx44d5ctf2PIyGNbu8PPzIyIiNzc38vf3t9sOX19feZu7u7vNOoiIjEYjTZo0iXr06EETJkygbt260cGDB4mI7Ga2rN2g1Fw1RYtSs2U0GikxMZHCwsLo3XffpfHjx1N5ebnFTNjDXFmzQem5smaLUnP16NEjmjhxIr3xxhsUFRVFcXFxVFpaaldz9UKUXiWy+ubPnw83Nzf0798fhYWFWLJkCXr37i1/l4skPT0dXl5eGDp0KADg2LFjiIuLg7OzMyZPnozExES4u7vL32Fj688McIe6OoDat2mHDBmCyMhIHD58GIWFhRgwYADCw8Mt7qfmFhEaJKK0lJWVISoqCpGRkcjNzcXq1avRvXt3hIaGorCw0C46RGgQrcVoNCIuLg59+vRBTk4OUlNTERwcjL59++L06dN20/EieJGmMidPnkRYWBiys7Plbdu3b4eTk5N8xgoArFy5En5+fvjuu+8sDtmazWYsXLgQCQkJiI6OVuwMIu6opZYOSV5eHvR6Pfbt2ydvW7NmDYYMGSL/4Vq+fDl8fX1V2yJCg0SUlj179iA4ONhiAXD69Gk4ODhg2rRpKC8vR0ZGBjp06KDaDhEaJKK0FBcXQ6/XIysrS952/fp1+Pj4YOrUqSgrK7OLjhfBizSVkP4gN3TGT15eHnx9fbF582Z5m9ForHcmihpeHXBH/cdQk9zcXGg0GvkP1q1btxASEoKkpCR88803AGo/p/HgwQOL31NTiwgNElFaMjMz4enpabHt4MGD8PLygl6vx86dO2E2m1X9HBGhQSJKy5EjR+Dq6opz584BgHzVhuXLl0Ov12P79u0wm82qf368CDt4Q1Zsu3btIqJ/LwTr6OhY7z6tWrWiyspKMhqNRFT7ORUnJydyc3OzuJ9Go2nivW0cd6irg6h+CxFRWFgYRUZG0vjx4+ntt98mb29vatOmDTk7O9Onn35Ko0aNopMnT5JWq7X4PaVaRGiQiNLSUEf79u2pZcuWtHjxYnnb2rVraeLEiWQ2m2nbtm2k0WjI1dXV4rHUNFf21iARpWX16tW0Zs0a+u233+Rter2e2rRpQxs2bCCifz9DNmXKFPLw8KDNmzdTdXU1abVai8dS+t/EqpRZG7IdO3bAx8fH4lV0Q6t/aVtISAimTZvW6P2Uwh3q6gAabjGZTPJbAffv38e5c+cQGhpq8cWNR48eRadOnfDjjz8qst+PE6FBIkpLQx3SkeaysjJ88cUX0Gg0CA0NRfPmzREcHAyj0Yhly5bBx8dHyV2XidAgEaVl48aNaN26Nd58802EhIRAp9PJ3/pfUVGBjz/+GHq9Hjdv3gQAVFZWAgCysrLg4eEh/ywqPpKmgNzcXFq+fDkNHz6cBg0aRNOnTyeihlf/Go2GKisrqUuXLnT16lWqqqpSzasE7lBXB1HjLQ4ODvKrUDc3N7p37x6VlpaSwWCQX4F37dqVysrKqLi4WLH9JxKjQSJKS2Md0pHmFi1aUEpKCuXk5NCYMWNoy5YtdOLECXJycqIHDx6Qn58fVVRUKJkgRINElJaNGzfS4sWLad68efT777/Tzp07afbs2bRo0SK6e/cuvfzyyxQVFUWenp40d+5cIiJycXEhIqJ27dqRi4sLFRUVKZnQ9BReJP5fkY64FBUVITU1FRcvXsShQ4eg1Wqxdu1aAI1/b0tSUhJCQ0OfeB9b4Q51dQDP3lJYWAgHBwcUFBTI27Zu3YoePXrg8OHDtt35f4jQIBGl5UWeI0DtBa+HDRuGqVOn2mR/GyJCg0SUFqlj3bp1SExMxMOHD+XbcnNzERAQgD/++ANA7ZGzr776Cm5ubtiyZQuqq6sBAAsWLEBERITq3smwNl6k2UBBQQHu3LljsU06LG00GpGcnAydTid/KPJx0hNu06ZNcHZ2VvQaadyhrg7g2VukP2ilpaUYM2YMtFotkpKSYDAY4O7ujtmzZ9v8j54IDRJRWl7kOQLULjqLiopgMBjg5+cn/w/XlkRokIjSUlBQgPLycvnnO3fu1Ds56+jRo2jTpg3KysrkbXfv3sXMmTPh7u6O8PBwjBo1Cq6urvKF3UVeqPEirQn99NNPaNeuHfz9/dGhQwfMnj0b169fBwCLa51dvHgR7du3l68r1tDArV+/HklJSaioqLD5QHKHujqA5295/FX2w4cPMWPGDMTHx8NgMNS7iDI3/HeitFjrObJ06VL4+/ujf//+jV5onBueTpSWuh2fffYZbty4Id/++PMgNTUVYWFhACAfNZNs2rQJc+bMQVJSEs6cOWObnVcYL9KaSH5+Prp06YK0tDQcO3YMK1euhE6nw+TJk+Xv15JeQZjNZqxcuRJOTk7yZUSqq6stTitW6i017lBXB/DiLVVVVRaXdXn8kirc8OxEabHGc0TquHbtmsVbt9zw7ERp+S8dJpNJnvvhw4djypQpiuyrGvEizcqkVzCrVq1Cu3btLK41tnz5cvTp0wfz58+v93ulpaUIDQ3F0KFDUVBQgAEDBiArK0uxw7jcoa4OQIwWERokorRYu0OJFzAiNEhEaXnWDpPJBLPZDH9/f+zYsQNA7bWS33vvPRQXF9t251WEz+60MulMv0uXLlFAQAA5OTnJt8XHx1PPnj1p9+7ddOrUKSIiMplMRETk5eVFCQkJ9Msvv1CvXr3I2dmZYmJiFDtzkDvU1UEkRosIDRJRWqzdocT1EEVokIjS8qwdDg4OlJ+fT1qtlnr06EEfffQRvfbaa1RaWkqtW7dWpEEVlF4l2ru9e/di6tSpSEtLQ15enrx927ZtaNasGS5cuADg38PSe/fuRVhYGFJTU+X7VldXY8WKFXBwcEB4eDhOnjxp2whwh9o6pH2z9xYRGiSitIjQIUKDRJQWa3TMmTMHGo0G7u7uCAoKwqFDh2wboUK8SHtO165dw+DBg9G6dWvExcXh1VdfhYeHhzyclZWV6NKlCxITEwFYfoapX79++PDDD+Wfb9y4genTpyMzM9O2EeAOQF0dgBgtIjRIRGkRoUOEBokoLdbsWLBgAXQ6ncUl9/7f8SLtOTx48ADjxo1DbGys/CFNAOjVqxfi4+MB1L5aWL9+PRwcHOpd2DUuLg6RkZE23eeGcEcttXQAYrSI0CARpUWEDhEaJKK0WKMjIiJC/rmkpMQ2O25H+DNpz0Gr1ZKLiwvFx8eTn58fPXr0iIiIBg8eTGfOnCGi2m9+Hj16NA0dOpQ++OADOnDgAAGgGzdu0Llz5yguLk7JBCLiDrV1EInRIkKDRJQWETpEaJCI0mKNjrFjx8qPp9PpFOlQNaVWh/aupqZG/m/pLJaxY8ciISHBYltlZSUiIiLQunVrDBgwAG3btkWfPn1Uc7YKd6irAxCjRYQGiSgtInSI0CARpUWUDrXSAP9cKI69sP79+9OECRMoPj6eAJDZbCZHR0e6efMmHT9+nPLz88nX15fef/99pXf1ibhDfURoEaFBIkqLCB0iNEhEaRGlQxWUWRuK58KFC/D29rY4G6XutyXbA+5QHxFaRGiQiNIiQocIDRJRWkTpUAv+TNoLwj8HInNzc6l58+bUs2dPIiKaO3cuTZ8+nUpKSpTcvf+MO9RHhBYRGiSitIjQIUKDRJQWUTrUxunpd2FPIn1h359//kkxMTH066+/UmJiIj18+JCysrLs5kv4uEN9RGgRoUEiSosIHSI0SERpEaVDdZQ6hCeSyspKdO7cGRqNBi4uLvj888+V3qXnwh3qI0KLCA0SUVpE6BChQSJKiygdasInDljJW2+9RXq9nlJTU6lZs2ZK785z4w71EaFFhAaJKC0idIjQIBGlRZQOteBFmpWYTCZydHRUejdeGHeojwgtIjRIRGkRoUOEBokoLaJ0qAUv0hhjjDHGVIjP7mSMMcYYUyFepDHGGGOMqRAv0hhjjDHGVIgXaYwxxhhjKsSLNMYYY4wxFeJFGmOMMcaYCvEijTHGbCgnJ4c0Gg3duXNH6V1hjKkcf08aY4w1oYiICAoJCaG0tDQiIqqpqaGysjLy9vaWr3fIGGMN4QusM8aYDTk7O1ObNm2U3g3GmB3gtzsZY6yJxMfH04EDByg9PZ00Gg1pNBpat26dxdud69atI09PT9qxYwcFBgaSVqulkSNH0oMHDygzM5N8fX2pRYsWNHXqVDKZTPJj19TU0MyZM8nHx4fc3Nyod+/elJOTo0woY6xJ8JE0xhhrIunp6VRUVETBwcE0b948IiI6depUvfs9fPiQvv76a8rOzqZ79+7RiBEjaMSIEeTp6Um7du2iixcvUkxMDPXt25diY2OJiGj8+PF0+fJlys7OprZt29LWrVtp0KBBdOLECdLr9TbtZIw1DV6kMcZYE/Hw8CBnZ2fSarXyW5yFhYX17mc0GmnVqlXk7+9PREQjR46krKwsunnzJjVv3pyCgoIoMjKS9u/fT7GxsXThwgX6/vvv6cqVK9S2bVsiIkpJSaE9e/ZQRkYGLVy40HaRjLEmw4s0xhhTmFarlRdoRETe3t7k6+tLzZs3t9hWUlJCRESHDx8mABQQEGDxONXV1dSyZUvb7DRjrMnxIo0xxhT20ksvWfys0Wga3GY2m4mIyGw2k6OjIxUUFJCjo6PF/R5f2DHG7Bsv0hhjrAk5OztbfODfGrp3704mk4lKSkqoX79+Vn1sxph68NmdjDHWhHx9fSkvL48uX75Mt2/flo+GvYiAgACKi4sjg8FAW7ZsoUuXLlF+fj4tXryYdu3aZYW9ZoypAS/SGGOsCaWkpJCjoyMFBQWRTqej4uJiqzxuRkYGGQwGSk5OpsDAQBoyZAjl5eVR+/btrfL4jDHl8RUHGGOMMcZUiI+kMcYYY4ypEC/SGGOMMcZUiBdpjDHGGGMqxIs0xhhjjDEV4kUaY4wxxpgK8SKNMcYYY0yFeJHGGGOMMaZCvEhjjDHGGFMhXqQxxhhjjKkQL9IYY4wxxlSIF2mMMcYYYyrEizTGGGOMMRX6H4PRiE6rdifnAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mean_time_series = arr_est.mean(['x','y']).compute()\n",
+    "mean_time_series.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f74dbab-9fe2-4b32-a112-5f80d90af859",
+   "metadata": {},
+   "source": [
+    "Let's try to visualize the time series of the filtered array using the 1st condition mentioned above for masking all the values above 10. We will create a function based on above code with `floor` as a parameter to be used for filtering. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "28aab0c5-e304-4133-b3fe-355fd493cda8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_mean_time_series(da=arr_est, band='estimates', floor=None):\n",
+    "    \"\"\" selects a band from `da`, optionally keeps only values above `floor`, calculates the spatial average and plots the resulting time series\"\"\"\n",
+    "    # da = da.sel(band=band)\n",
+    "    da_filtered = da if floor is None else da.where(da.values >= floor)\n",
+    "    av = da.mean(['x','y']).compute()\n",
+    "    av.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "9d534c06-eea3-4e80-aaf7-a79df8c5e555",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_mean_time_series(da=arr_masked_gt, floor=10)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "esa-cci",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "esa-cci"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

From eca547b78ec116f63f6838c383de40fd8be62636 Mon Sep 17 00:00:00 2001
From: omshinde23 <rajat.shinde@uah.edu>
Date: Fri, 22 Sep 2023 09:46:09 -0700
Subject: [PATCH 3/8] Update ESA CCI notebook to include description

---
 docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb | 89 +++++++++-----------
 1 file changed, 41 insertions(+), 48 deletions(-)

diff --git a/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb b/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
index fa424630..9a018dee 100644
--- a/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
+++ b/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
@@ -23,48 +23,28 @@
     "\n",
     "Description: In this tutorial, we explore accessing and visualizing ESA CCI Version 4 data from the MAAP STAC catalog. We make use of the [stackstac package](https://github.com/gjoseph92/stackstac) that allows us to turn a stack collection imported from the catalog with the [`pystac_client`](https://pystac-client.readthedocs.io/en/stable/) to an xarray dataset, and we plot the time series of the mean aboveground biomass for a selected tile of the dataset across the available temporal range.\n",
     "\n",
+    "### About The Dataset\n",
+    "This dataset comprises estimates of forest above-ground biomass for the years 2017, 2018, 2019 and 2020, version 4. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.\n",
+    "\n",
     "### Run This Notebook\n",
     "To access and run this tutorial within MAAP's Algorithm Development Environment (ADE), please refer to the [\"Getting started with the MAAP\"](https://docs.maap-project.org/en/latest/getting_started/getting_started.html) section of our documentation.\n",
     "\n",
-    "Disclaimer: it is highly recommended to run a tutorial within MAAP's ADE, which already includes packages specific to MAAP, such as maap-py. Running the tutorial outside of the MAAP ADE may lead to errors.\n",
-    "\n",
-    "### Requirements\n",
-    "\n",
-    "Create a conda environment with a configuration file to which you will paste the following"
+    "Disclaimer: it is highly recommended to run a tutorial within MAAP's ADE, which already includes packages specific to MAAP, such as maap-py. Running the tutorial outside of the MAAP ADE may lead to errors. First off, we will install and import the required Python packages if they are not already installed in the workspace.  "
    ]
   },
   {
-   "cell_type": "raw",
-   "id": "f72403f6-7776-4ed2-b5a2-5b2c5e96770e",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "name: esa-cci\n",
-    "channels:\n",
-    "    - conda-forge\n",
-    "    - defaults\n",
-    "dependencies:\n",
-    "    - python=3.10\n",
-    "    - ipykernel\n",
-    "    - numpy\n",
-    "    - matplotlib\n",
-    "    - pip\n",
-    "    - distributed\n",
-    "    - pip:\n",
-    "        - pystac_client\n",
-    "        - stackstac\n",
-    "        - dask[diagnostics]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d2179097-2ce3-4b4e-bf32-9ed50a8fa94e",
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "aea4eb22-0867-4876-bd2b-fe74f5dd7bab",
    "metadata": {
     "tags": []
    },
+   "outputs": [],
    "source": [
-    "Make the `esa-cci` environment available as a notebook kernel and choose it to run this notebook. "
+    "#Install required modules\n",
+    "#!mamba install -c conda-forge stackstac\n",
+    "#!pip install pystac_client\n",
+    "#!pip install dask[diagnostics]"
    ]
   },
   {
@@ -87,12 +67,12 @@
    "id": "26a45833",
    "metadata": {},
    "source": [
-    "We first create a client to access the STAC test catalog."
+    "After installing the require packages, we create a client to access the STAC test catalog."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 35,
    "id": "e6917d57",
    "metadata": {},
    "outputs": [],
@@ -106,17 +86,17 @@
    "id": "df045dc1",
    "metadata": {},
    "source": [
-    "We create a bounding box of interest to find the tile that covers a small region around manaus, brazil (amazon rainforest)."
+    "Now, we define a bounding box of interest to find the tile that covers a small region around Manaus, Brazil (Amazon rainforest)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 37,
    "id": "73868a97",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Filtering for the items in the collection\n",
+    "# BBox for filtering the items in the collection\n",
     "bbox = [-55,-6,-54.8,-5.8]"
    ]
   },
@@ -146,12 +126,12 @@
    "id": "3281f4e8-da53-4fcc-bb3f-bebb43e5143d",
    "metadata": {},
    "source": [
-    "Let's take a quick look at the results of the search. We may be for example interested in knowing where the data is stored."
+    "Let's take a quick look at the results of the search. We can access the link of the location where the data is stored using `href` attribute as shown below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 38,
    "id": "c73362b3-cfb5-48cd-a018-58ca5feb6ef4",
    "metadata": {
     "tags": []
@@ -163,7 +143,7 @@
        "'s3://nasa-maap-data-store/file-staging/nasa-map/ESACCI_Biomass_L4_AGB_V4_100m_2020/N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2020-fv4.0.tif'"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -182,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 39,
    "id": "46bf720c-18d7-4d80-960b-998abd4a5255",
    "metadata": {},
    "outputs": [
@@ -196,7 +176,7 @@
     }
    ],
    "source": [
-    "#Change variable name\n",
+    "#Creating a stack of the filtered items\n",
     "arr = stack(stac_collection.get_all_items())"
    ]
   },
@@ -205,7 +185,7 @@
    "id": "86a57e55-3d3a-473f-85e5-cbf6b17d198a",
    "metadata": {},
    "source": [
-    "We can see that our array has the following dimensions : time (we have four data points, one for each year), latitude, longitude and band (we have two bands : the AGB estimates and the AGB standard deviation)"
+    "We can see that our array has the following dimensions : `time` (we have four data points, one for each year - 2017, 2018, 2019 and 2020), `latitude`, `longitude` and `band` (we have two bands : `estimates` with the AGB estimate values, and `std_dev` storing the AGB standard deviation values)."
    ]
   },
   {
@@ -816,7 +796,7 @@
    "id": "a4e747a9-c390-4d40-9eef-e2211c2a6cb4",
    "metadata": {},
    "source": [
-    "Now, we will perform a spatial filtering operation and select a subset of the created stack as it is compute intensive to run experiments on the entire stack due to its size. "
+    "Now, we will perform a spatial sub-setting operation to select a subset of the created stack as it is compute intensive to run experiments on the entire stack due to its size. "
    ]
   },
   {
@@ -1401,6 +1381,17 @@
     "arr_sub"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "f259efda-7614-4121-8a8f-8533b2435334",
+   "metadata": {},
+   "source": [
+    "As it is evident from the above output that the size of the subset xarray has reduced significantly and it can be used further for our analysis. In this tutorial, we are going to perform following two operations:\n",
+    "\n",
+    "1. Masking some values based on a condition\n",
+    "2. Plotting Mean time-series for the AGB estimates"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c1124341-5f4d-4fe7-80f9-381ed11e0ede",
@@ -1426,7 +1417,9 @@
    "id": "8eecc263-f593-4aad-b724-b93099b56df0",
    "metadata": {},
    "source": [
-    "Let's try to print the values of the `arr_est`."
+    "Let's try to print the values of the `arr_est`.\n",
+    "\n",
+    "Disclaimer - It is not recommended to print values if the xarray is big in size as the notebook can crash. The `.values` requires the xarray to be loaded into the memory before printing the values and is a memory intensive problem."
    ]
   },
   {
@@ -1485,7 +1478,7 @@
    "id": "0558d3c7-6b39-4e6b-ab8f-c399cee52a29",
    "metadata": {},
    "source": [
-    "Now, we are going to filter the values which are less than 10. In the resulting xarray, all the values less than 10 are going to be `nan`. We can also replace `nan` by any other value."
+    "Now, we are going to filter the values which are less than 10 using the `.where` (https://docs.xarray.dev/en/stable/generated/xarray.DataArray.where.html) function. The `.where(<cond>, <True directive>, <False directive>)` function checks a condition and performs specific directives if the condition results in `True` or `False`. In the resulting xarray, all the values less than 10 are going to be `nan` (the default value used if the filtering condition is `True`). We can also replace `nan` by any other value by mentioning it in the `<True directive>`."
    ]
   },
   {
@@ -1547,7 +1540,7 @@
    "id": "1f511c71-5098-4aa2-9d0c-da089c967b7e",
    "metadata": {},
    "source": [
-    "Now, let's try to perform filtering based on the opposite condition so as to mask all the values greater than 10."
+    "Now, let's try to perform filtering based on the opposite condition i.e. to mask all the values greater than 10."
    ]
   },
   {
@@ -1607,7 +1600,7 @@
    "id": "cfeeec10-e023-4234-b528-aa7dc87c7e05",
    "metadata": {},
    "source": [
-    "### Plotting mean time-series\n",
+    "### Plotting Mean time-series for the AGB estimates\n",
     "\n",
     "Below we will look into plotting the mean time-series for the `estimate` band. We will be using the `compute()` module of the `dask` which performs an in-memory computation in a parallel approach."
    ]

From ffc7bf66edfd5e214386ed74c26d4c44b8cf6e10 Mon Sep 17 00:00:00 2001
From: omshinde23 <rajat.shinde@uah.edu>
Date: Wed, 27 Sep 2023 14:29:15 -0700
Subject: [PATCH 4/8] Update ESA CCI notebook for edits

---
 docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb | 365 ++++++++++---------
 1 file changed, 194 insertions(+), 171 deletions(-)

diff --git a/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb b/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
index 9a018dee..8b6b00f8 100644
--- a/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
+++ b/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
@@ -7,7 +7,7 @@
     "tags": []
    },
    "source": [
-    "## Accessing and visualizing ESA CCI version 4 data from the MAAP STAC catalog"
+    "## ESA CCI v4 Access and Visualize"
    ]
   },
   {
@@ -23,18 +23,31 @@
     "\n",
     "Description: In this tutorial, we explore accessing and visualizing ESA CCI Version 4 data from the MAAP STAC catalog. We make use of the [stackstac package](https://github.com/gjoseph92/stackstac) that allows us to turn a stack collection imported from the catalog with the [`pystac_client`](https://pystac-client.readthedocs.io/en/stable/) to an xarray dataset, and we plot the time series of the mean aboveground biomass for a selected tile of the dataset across the available temporal range.\n",
     "\n",
-    "### About The Dataset\n",
-    "This dataset comprises estimates of forest above-ground biomass for the years 2017, 2018, 2019 and 2020, version 4. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.\n",
-    "\n",
     "### Run This Notebook\n",
     "To access and run this tutorial within MAAP's Algorithm Development Environment (ADE), please refer to the [\"Getting started with the MAAP\"](https://docs.maap-project.org/en/latest/getting_started/getting_started.html) section of our documentation.\n",
     "\n",
-    "Disclaimer: it is highly recommended to run a tutorial within MAAP's ADE, which already includes packages specific to MAAP, such as maap-py. Running the tutorial outside of the MAAP ADE may lead to errors. First off, we will install and import the required Python packages if they are not already installed in the workspace.  "
+    "### About The Dataset\n",
+    "This dataset comprises estimates of forest Above-Ground Biomass(AGB) for the years 2017, 2018, 2019 and 2020, version 4. They are derived from a combination of Earth Observation (EO) data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.\n",
+    "\n",
+    "### Additional Resources\n",
+    "* [ESA’s Climate Change Initiative Biomass project](https://climate.esa.int/en/odp/#/project/biomass)\n",
+    "* [xarray Documentation](https://docs.xarray.dev/en/stable/index.html)\n",
+    "\n",
+    "Disclaimer: it is highly recommended to run a tutorial within MAAP's ADE, which already includes packages specific to MAAP, such as maap-py. Running the tutorial outside of the MAAP ADE may lead to errors.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "445b69f2-13ab-4a03-aa41-4fde92c270fa",
+   "metadata": {},
+   "source": [
+    "### Importing and Installing Packages\n",
+    "First off, we will install and import the required Python packages if they are not already installed in the workspace."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 40,
    "id": "aea4eb22-0867-4876-bd2b-fe74f5dd7bab",
    "metadata": {
     "tags": []
@@ -49,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 41,
    "id": "bcc3b0ba",
    "metadata": {},
    "outputs": [],
@@ -62,6 +75,14 @@
     "import xarray"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "e96d41e2-919c-4fc2-9e3e-0fa0f8d692dc",
+   "metadata": {},
+   "source": [
+    "### Accessing and Filtering the Items"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "26a45833",
@@ -72,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 42,
    "id": "e6917d57",
    "metadata": {},
    "outputs": [],
@@ -81,6 +102,14 @@
     "catalog = pystac_client.Client.open(URL)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "69ac94df-819e-4be5-9893-3f3d3c519c37",
+   "metadata": {},
+   "source": [
+    "### Creating an AOI"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "df045dc1",
@@ -91,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 43,
    "id": "73868a97",
    "metadata": {},
    "outputs": [],
@@ -110,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 44,
    "id": "7bb997f0",
    "metadata": {},
    "outputs": [],
@@ -131,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 46,
    "id": "c73362b3-cfb5-48cd-a018-58ca5feb6ef4",
    "metadata": {
     "tags": []
@@ -143,7 +172,7 @@
        "'s3://nasa-maap-data-store/file-staging/nasa-map/ESACCI_Biomass_L4_AGB_V4_100m_2020/N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2020-fv4.0.tif'"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -796,12 +825,12 @@
    "id": "a4e747a9-c390-4d40-9eef-e2211c2a6cb4",
    "metadata": {},
    "source": [
-    "Now, we will perform a spatial sub-setting operation to select a subset of the created stack as it is compute intensive to run experiments on the entire stack due to its size. "
+    "Now, we will perform a spatial sub-setting operation to select a subset of the created stack based on the user input for running our experiments. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 55,
    "id": "e9d173bd-fbfc-4f5c-b0bb-d948b49259ce",
    "metadata": {
     "tags": []
@@ -1173,16 +1202,16 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;stackstac-0d88a6aa99e2eb93c2b68b6e8780c93c&#x27; (time: 4,\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;stackstac-33ee8af46de39450b1f613ed794698ab&#x27; (time: 4,\n",
        "                                                                band: 2,\n",
-       "                                                                y: 2250, x: 5625)&gt;\n",
-       "dask.array&lt;getitem, shape=(4, 2, 2250, 5625), dtype=float64, chunksize=(1, 1, 1024, 1024), chunktype=numpy.ndarray&gt;\n",
+       "                                                                y: 225, x: 225)&gt;\n",
+       "dask.array&lt;getitem, shape=(4, 2, 225, 225), dtype=float64, chunksize=(1, 1, 225, 225), chunktype=numpy.ndarray&gt;\n",
        "Coordinates: (12/13)\n",
        "  * time            (time) datetime64[ns] 2017-01-01 2018-01-01 ... 2020-01-01\n",
        "    id              (time) &lt;U52 &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-20...\n",
        "  * band            (band) &lt;U18 &#x27;estimates&#x27; &#x27;standard_deviation&#x27;\n",
-       "  * x               (x) float64 -55.0 -55.0 -55.0 -55.0 ... -50.0 -50.0 -50.0\n",
-       "  * y               (y) float64 -3.001 -3.002 -3.003 ... -4.998 -4.999 -5.0\n",
+       "  * x               (x) float64 -55.0 -55.0 -55.0 -55.0 ... -54.8 -54.8 -54.8\n",
+       "  * y               (y) float64 -5.801 -5.802 -5.803 ... -5.998 -5.999 -6.0\n",
        "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
        "    ...              ...\n",
        "    proj:shape      object {11250}\n",
@@ -1195,7 +1224,7 @@
        "    spec:        RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999...\n",
        "    crs:         epsg:4326\n",
        "    transform:   | 0.00, 0.00,-60.00|\\n| 0.00,-0.00, 0.00|\\n| 0.00, 0.00, 1.00|\n",
-       "    resolution:  0.00088888888888</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'stackstac-0d88a6aa99e2eb93c2b68b6e8780c93c'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>band</span>: 2</li><li><span class='xr-has-index'>y</span>: 2250</li><li><span class='xr-has-index'>x</span>: 5625</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5dcb3ec3-3dee-4721-9486-389c93ff0732' class='xr-array-in' type='checkbox' checked><label for='section-5dcb3ec3-3dee-4721-9486-389c93ff0732' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 720, 518), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    resolution:  0.00088888888888</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'stackstac-33ee8af46de39450b1f613ed794698ab'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>band</span>: 2</li><li><span class='xr-has-index'>y</span>: 225</li><li><span class='xr-has-index'>x</span>: 225</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0269dadd-9124-417f-91d9-df4f23e3c95a' class='xr-array-in' type='checkbox' checked><label for='section-0269dadd-9124-417f-91d9-df4f23e3c95a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 225, 225), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1210,18 +1239,18 @@
        "                    \n",
        "                    <tr>\n",
        "                        <th> Bytes </th>\n",
-       "                        <td> 772.48 MiB </td>\n",
-       "                        <td> 8.00 MiB </td>\n",
+       "                        <td> 3.09 MiB </td>\n",
+       "                        <td> 395.51 kiB </td>\n",
        "                    </tr>\n",
        "                    \n",
        "                    <tr>\n",
        "                        <th> Shape </th>\n",
-       "                        <td> (4, 2, 2250, 5625) </td>\n",
-       "                        <td> (1, 1, 1024, 1024) </td>\n",
+       "                        <td> (4, 2, 225, 225) </td>\n",
+       "                        <td> (1, 1, 225, 225) </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 144 chunks in 4 graph layers </td>\n",
+       "                        <td colspan=\"2\"> 8 chunks in 4 graph layers </td>\n",
        "                    </tr>\n",
        "                    <tr>\n",
        "                        <th> Data type </th>\n",
@@ -1231,126 +1260,112 @@
        "            </table>\n",
        "        </td>\n",
        "        <td>\n",
-       "        <svg width=\"374\" height=\"112\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "        <svg width=\"380\" height=\"184\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"28\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"25\" x2=\"28\" y2=\"25\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
        "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"6\" y1=\"0\" x2=\"6\" y2=\"25\" />\n",
-       "  <line x1=\"12\" y1=\"0\" x2=\"12\" y2=\"25\" />\n",
-       "  <line x1=\"19\" y1=\"0\" x2=\"19\" y2=\"25\" />\n",
-       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
+       "  <line x1=\"14\" y1=\"0\" x2=\"14\" y2=\"25\" />\n",
+       "  <line x1=\"21\" y1=\"0\" x2=\"21\" y2=\"25\" />\n",
+       "  <line x1=\"28\" y1=\"0\" x2=\"28\" y2=\"25\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
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+       "  <polygon points=\"0.0,0.0 28.375360691843483,0.0 28.375360691843483,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
-       "  <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n",
+       "  <text x=\"14.187680\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
+       "  <text x=\"48.375361\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,48.375361,12.706308)\">1</text>\n",
        "\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"95\" y1=\"15\" x2=\"109\" y2=\"30\" />\n",
-       "  <line x1=\"95\" y1=\"37\" x2=\"109\" y2=\"52\" />\n",
-       "  <line x1=\"95\" y1=\"48\" x2=\"109\" y2=\"62\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"98\" y1=\"0\" x2=\"112\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"98\" y1=\"120\" x2=\"112\" y2=\"134\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"48\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"102\" y1=\"7\" x2=\"102\" y2=\"55\" />\n",
-       "  <line x1=\"109\" y1=\"14\" x2=\"109\" y2=\"62\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"98\" y1=\"0\" x2=\"98\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"105\" y1=\"7\" x2=\"105\" y2=\"127\" />\n",
+       "  <line x1=\"112\" y1=\"14\" x2=\"112\" y2=\"134\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"95.0,0.0 109.9485979497544,14.948597949754403 109.9485979497544,62.9485979497544 95.0,48.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"98.0,0.0 112.9485979497544,14.948597949754403 112.9485979497544,134.9485979497544 98.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"102\" y1=\"7\" x2=\"222\" y2=\"7\" />\n",
-       "  <line x1=\"109\" y1=\"14\" x2=\"229\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"98\" y1=\"0\" x2=\"218\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"105\" y1=\"7\" x2=\"225\" y2=\"7\" />\n",
+       "  <line x1=\"112\" y1=\"14\" x2=\"232\" y2=\"14\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"95\" y1=\"0\" x2=\"109\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"106\" y1=\"0\" x2=\"120\" y2=\"14\" />\n",
-       "  <line x1=\"127\" y1=\"0\" x2=\"142\" y2=\"14\" />\n",
-       "  <line x1=\"149\" y1=\"0\" x2=\"164\" y2=\"14\" />\n",
-       "  <line x1=\"171\" y1=\"0\" x2=\"186\" y2=\"14\" />\n",
-       "  <line x1=\"193\" y1=\"0\" x2=\"208\" y2=\"14\" />\n",
-       "  <line x1=\"215\" y1=\"0\" x2=\"229\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"98\" y1=\"0\" x2=\"112\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"218\" y1=\"0\" x2=\"232\" y2=\"14\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"95.0,0.0 215.0,0.0 229.9485979497544,14.948597949754403 109.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"98.0,0.0 218.0,0.0 232.9485979497544,14.948597949754403 112.9485979497544,14.948597949754403\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"109\" y1=\"14\" x2=\"229\" y2=\"14\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"109\" y1=\"30\" x2=\"229\" y2=\"30\" />\n",
-       "  <line x1=\"109\" y1=\"52\" x2=\"229\" y2=\"52\" />\n",
-       "  <line x1=\"109\" y1=\"62\" x2=\"229\" y2=\"62\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"112\" y1=\"14\" x2=\"232\" y2=\"14\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"112\" y1=\"134\" x2=\"232\" y2=\"134\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Vertical lines -->\n",
-       "  <line x1=\"109\" y1=\"14\" x2=\"109\" y2=\"62\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"14\" x2=\"120\" y2=\"62\" />\n",
-       "  <line x1=\"142\" y1=\"14\" x2=\"142\" y2=\"62\" />\n",
-       "  <line x1=\"164\" y1=\"14\" x2=\"164\" y2=\"62\" />\n",
-       "  <line x1=\"186\" y1=\"14\" x2=\"186\" y2=\"62\" />\n",
-       "  <line x1=\"208\" y1=\"14\" x2=\"208\" y2=\"62\" />\n",
-       "  <line x1=\"229\" y1=\"14\" x2=\"229\" y2=\"62\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"112\" y1=\"14\" x2=\"112\" y2=\"134\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"232\" y1=\"14\" x2=\"232\" y2=\"134\" style=\"stroke-width:2\" />\n",
        "\n",
        "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"109.9485979497544,14.948597949754403 229.9485979497544,14.948597949754403 229.9485979497544,62.9485979497544 109.9485979497544,62.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "  <polygon points=\"112.9485979497544,14.948597949754403 232.9485979497544,14.948597949754403 232.9485979497544,134.9485979497544 112.9485979497544,134.9485979497544\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
        "\n",
        "  <!-- Text -->\n",
-       "  <text x=\"169.948598\" y=\"82.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >5625</text>\n",
-       "  <text x=\"249.948598\" y=\"38.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,249.948598,38.948598)\">2250</text>\n",
-       "  <text x=\"92.474299\" y=\"75.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,92.474299,75.474299)\">2</text>\n",
+       "  <text x=\"172.948598\" y=\"154.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >225</text>\n",
+       "  <text x=\"252.948598\" y=\"74.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,252.948598,74.948598)\">225</text>\n",
+       "  <text x=\"95.474299\" y=\"147.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,95.474299,147.474299)\">2</text>\n",
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-d83d7e01-ace9-41c3-8ba9-f641b6ccb86a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d83d7e01-ace9-41c3-8ba9-f641b6ccb86a' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-01 ... 2020-01-01</div><input id='attrs-ca39f9dc-c13e-4e26-8247-6c88b2408e96' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ca39f9dc-c13e-4e26-8247-6c88b2408e96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8988ae1a-ba3a-4f47-b0cc-d0c4a6ff630f' class='xr-var-data-in' type='checkbox'><label for='data-8988ae1a-ba3a-4f47-b0cc-d0c4a6ff630f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-59b9ac53-76c0-4463-adb0-524ed98e9da7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-59b9ac53-76c0-4463-adb0-524ed98e9da7' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-01 ... 2020-01-01</div><input id='attrs-a0d369fc-b85b-4490-a793-617b5570def6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a0d369fc-b85b-4490-a793-617b5570def6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce3e69e5-a3c2-42fa-8012-610f693f334c' class='xr-var-data-in' type='checkbox'><label for='data-ce3e69e5-a3c2-42fa-8012-610f693f334c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U52</div><div class='xr-var-preview xr-preview'>&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-M...</div><input id='attrs-fdd4a64b-0af1-4c8b-a03b-b57462fa9ede' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdd4a64b-0af1-4c8b-a03b-b57462fa9ede' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d09a8525-5a10-468d-ab91-d4703c7fd80e' class='xr-var-data-in' type='checkbox'><label for='data-d09a8525-5a10-468d-ab91-d4703c7fd80e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2017-fv4.0&#x27;,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U52</div><div class='xr-var-preview xr-preview'>&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-M...</div><input id='attrs-34ad2ed3-0d7a-42da-b5d1-59370c65989c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34ad2ed3-0d7a-42da-b5d1-59370c65989c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20bade00-1654-4638-81ec-435b9f1f3a6e' class='xr-var-data-in' type='checkbox'><label for='data-20bade00-1654-4638-81ec-435b9f1f3a6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2017-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2018-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2019-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2020-fv4.0&#x27;],\n",
-       "      dtype=&#x27;&lt;U52&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;estimates&#x27; &#x27;standard_deviation&#x27;</div><input id='attrs-cdfcfdd9-c91c-4481-90aa-85eb3c49b076' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cdfcfdd9-c91c-4481-90aa-85eb3c49b076' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01423b6a-0d13-4b18-8de8-77a6ac13c374' class='xr-var-data-in' type='checkbox'><label for='data-01423b6a-0d13-4b18-8de8-77a6ac13c374' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-55.0 -55.0 -55.0 ... -50.0 -50.0</div><input id='attrs-ee85812c-8dc3-4bf8-a75e-7167f6b02ce3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee85812c-8dc3-4bf8-a75e-7167f6b02ce3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90ba73ab-b5df-4898-8453-c3a865a39df2' class='xr-var-data-in' type='checkbox'><label for='data-90ba73ab-b5df-4898-8453-c3a865a39df2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-55.      , -54.999111, -54.998222, ..., -50.002667, -50.001778,\n",
-       "       -50.000889])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.001 -3.002 ... -4.999 -5.0</div><input id='attrs-6b8c838c-8073-4c7b-a00c-86cab51f145c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6b8c838c-8073-4c7b-a00c-86cab51f145c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ae0f6ae-9c37-4c55-b5f2-ad3b7c59068f' class='xr-var-data-in' type='checkbox'><label for='data-6ae0f6ae-9c37-4c55-b5f2-ad3b7c59068f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-3.000889, -3.001778, -3.002667, ..., -4.998222, -4.999111, -5.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:transform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, 1.0, -0.00088888888888, -6...</div><input id='attrs-78129de4-ec15-47eb-a51d-a6e6ced5d85c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78129de4-ec15-47eb-a51d-a6e6ced5d85c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ea19ff5-1b38-4705-bb1d-0f58b56dc4a5' class='xr-var-data-in' type='checkbox'><label for='data-2ea19ff5-1b38-4705-bb1d-0f58b56dc4a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888888}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:geometry</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates...</div><input id='attrs-f4d0e9e7-5327-48a5-913b-5f72321bd757' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4d0e9e7-5327-48a5-913b-5f72321bd757' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-77238c93-efef-407f-bb12-60736f2d8a1b' class='xr-var-data-in' type='checkbox'><label for='data-77238c93-efef-407f-bb12-60736f2d8a1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.9999999999], [-50.0000000001, -9.9999999999], [-50.0000000001, 0.0], [-60.0, 0.0], [-60.0, -9.9999999999]]]},\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:shape</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{11250}</div><input id='attrs-80ff2960-920d-4c6d-8995-b43191024b5e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-80ff2960-920d-4c6d-8995-b43191024b5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-109e60ca-0ab5-499a-b4f2-02d326306fd9' class='xr-var-data-in' type='checkbox'><label for='data-109e60ca-0ab5-499a-b4f2-02d326306fd9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({11250}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:bbox</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, -60.0, -50.0000000001, -9....</div><input id='attrs-07d24a84-8bb5-451c-b91c-67bcfa0ebeed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07d24a84-8bb5-451c-b91c-67bcfa0ebeed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-544bdd02-f9bb-46d9-b7ca-50b3f5c80f01' class='xr-var-data-in' type='checkbox'><label for='data-544bdd02-f9bb-46d9-b7ca-50b3f5c80f01' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, -60.0, -50.0000000001, -9.9999999999}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-d7d2dfaf-b53b-4720-a16a-3209c112f41a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7d2dfaf-b53b-4720-a16a-3209c112f41a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae52eae8-a186-40b3-89af-f4d159ba9dee' class='xr-var-data-in' type='checkbox'><label for='data-ae52eae8-a186-40b3-89af-f4d159ba9dee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-93315d07-a5b5-4e18-afcb-44f7a15fd347' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93315d07-a5b5-4e18-afcb-44f7a15fd347' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26c2feb9-154e-4f2f-88e5-5b7a6cda5c9b' class='xr-var-data-in' type='checkbox'><label for='data-26c2feb9-154e-4f2f-88e5-5b7a6cda5c9b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
-       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>description</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-2d929175-3be8-4d7d-ba3c-046d820caa39' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d929175-3be8-4d7d-ba3c-046d820caa39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64f48bea-270a-4262-bf3e-dbcbc46e91e3' class='xr-var-data-in' type='checkbox'><label for='data-64f48bea-270a-4262-bf3e-dbcbc46e91e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
-       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-8a17978e-d6da-44ed-8777-5aeaeedffe70' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8a17978e-d6da-44ed-8777-5aeaeedffe70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aecc405e-ef57-4d63-9d1d-aea9aa3f9d7a' class='xr-var-data-in' type='checkbox'><label for='data-aecc405e-ef57-4d63-9d1d-aea9aa3f9d7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9d766367-7e7a-4ef0-81e3-1069fa02f101' class='xr-section-summary-in' type='checkbox'  ><label for='section-9d766367-7e7a-4ef0-81e3-1069fa02f101' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8e3687e2-0be8-4531-864c-4b3dbbfb8969' class='xr-index-data-in' type='checkbox'/><label for='index-8e3687e2-0be8-4531-864c-4b3dbbfb8969' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4adcc4c4-668a-4a93-88e4-69b03ef6fa7a' class='xr-index-data-in' type='checkbox'/><label for='index-4adcc4c4-668a-4a93-88e4-69b03ef6fa7a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-00728d1b-9712-48c7-a95e-8e9e396a391f' class='xr-index-data-in' type='checkbox'/><label for='index-00728d1b-9712-48c7-a95e-8e9e396a391f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    -54.99999999945,  -54.99911111056112, -54.998222221672236,\n",
+       "      dtype=&#x27;&lt;U52&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;estimates&#x27; &#x27;standard_deviation&#x27;</div><input id='attrs-4a94c41b-3aa3-4350-ae0d-c3b027bc45b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a94c41b-3aa3-4350-ae0d-c3b027bc45b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f6e24a3-df03-480b-a594-f62c706e59db' class='xr-var-data-in' type='checkbox'><label for='data-9f6e24a3-df03-480b-a594-f62c706e59db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-55.0 -55.0 -55.0 ... -54.8 -54.8</div><input id='attrs-a13c9822-4eb3-4d5b-885a-3622f9b35652' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a13c9822-4eb3-4d5b-885a-3622f9b35652' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06ac1481-906f-4645-a65b-bc2045f01ceb' class='xr-var-data-in' type='checkbox'><label for='data-06ac1481-906f-4645-a65b-bc2045f01ceb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-55.      , -54.999111, -54.998222, ..., -54.802667, -54.801778,\n",
+       "       -54.800889])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.801 -5.802 ... -5.999 -6.0</div><input id='attrs-af3092db-5d0e-4b80-a4a6-5456bb9b4c4a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af3092db-5d0e-4b80-a4a6-5456bb9b4c4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6da8d1a9-5667-4296-ba60-eb88c7b028f9' class='xr-var-data-in' type='checkbox'><label for='data-6da8d1a9-5667-4296-ba60-eb88c7b028f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-5.800889, -5.801778, -5.802667, ..., -5.998222, -5.999111, -6.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:transform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, 1.0, -0.00088888888888, -6...</div><input id='attrs-62a188f7-507c-4d09-9cb2-7a80c79f9208' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-62a188f7-507c-4d09-9cb2-7a80c79f9208' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99f1e234-8c9f-455f-b070-b71410dd6312' class='xr-var-data-in' type='checkbox'><label for='data-99f1e234-8c9f-455f-b070-b71410dd6312' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888888}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:geometry</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates...</div><input id='attrs-6468b19e-8935-45e5-8929-532430813ea3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6468b19e-8935-45e5-8929-532430813ea3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-202ff2c0-3f9b-47e8-8eef-38503188bf31' class='xr-var-data-in' type='checkbox'><label for='data-202ff2c0-3f9b-47e8-8eef-38503188bf31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.9999999999], [-50.0000000001, -9.9999999999], [-50.0000000001, 0.0], [-60.0, 0.0], [-60.0, -9.9999999999]]]},\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:shape</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{11250}</div><input id='attrs-f956f951-f48a-4bfe-95c9-102c0d300b50' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f956f951-f48a-4bfe-95c9-102c0d300b50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d97789b0-3f23-44e6-af73-21c036552147' class='xr-var-data-in' type='checkbox'><label for='data-d97789b0-3f23-44e6-af73-21c036552147' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({11250}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:bbox</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, -60.0, -50.0000000001, -9....</div><input id='attrs-4d7729f9-bd9b-48d2-a2fa-accc10667959' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d7729f9-bd9b-48d2-a2fa-accc10667959' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-885f5085-4972-4ee2-bbbd-39f1caa8441c' class='xr-var-data-in' type='checkbox'><label for='data-885f5085-4972-4ee2-bbbd-39f1caa8441c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, -60.0, -50.0000000001, -9.9999999999}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-f9e00aa3-4f79-4c03-bae5-067e7e27bccb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9e00aa3-4f79-4c03-bae5-067e7e27bccb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3619bda0-fe37-44e6-b3cc-2f923884e710' class='xr-var-data-in' type='checkbox'><label for='data-3619bda0-fe37-44e6-b3cc-2f923884e710' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-d4b9dab6-957d-4a40-af64-485508ebffee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4b9dab6-957d-4a40-af64-485508ebffee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbb88cd9-70e2-4296-a133-9076285fc3cf' class='xr-var-data-in' type='checkbox'><label for='data-fbb88cd9-70e2-4296-a133-9076285fc3cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
+       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>description</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-577cfd98-42ce-4d5d-b36b-7a80da43839d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-577cfd98-42ce-4d5d-b36b-7a80da43839d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b94dfff2-3017-4b1e-a403-fceaca2bbfa9' class='xr-var-data-in' type='checkbox'><label for='data-b94dfff2-3017-4b1e-a403-fceaca2bbfa9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
+       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-c0734cbe-a34e-4e34-a3c8-fa5bedfb9302' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c0734cbe-a34e-4e34-a3c8-fa5bedfb9302' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bd387cc-3d9c-4d71-b08f-b39ef4baba62' class='xr-var-data-in' type='checkbox'><label for='data-2bd387cc-3d9c-4d71-b08f-b39ef4baba62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ddfb6deb-ca33-4671-80e9-05c74336de7e' class='xr-section-summary-in' type='checkbox'  ><label for='section-ddfb6deb-ca33-4671-80e9-05c74336de7e' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fb2c616d-fc6b-4030-8219-32ff7f1a6d4f' class='xr-index-data-in' type='checkbox'/><label for='index-fb2c616d-fc6b-4030-8219-32ff7f1a6d4f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ed41731e-5101-46b0-9687-e50febcc0df9' class='xr-index-data-in' type='checkbox'/><label for='index-ed41731e-5101-46b0-9687-e50febcc0df9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e2fb26bd-1b7a-4cce-b765-371ed8daa85a' class='xr-index-data-in' type='checkbox'/><label for='index-e2fb26bd-1b7a-4cce-b765-371ed8daa85a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    -54.99999999945,  -54.99911111056112, -54.998222221672236,\n",
        "       -54.997333332783356, -54.996444443894475, -54.995555555005595,\n",
        "       -54.994666666116714, -54.993777777227834,  -54.99288888833896,\n",
        "        -54.99199999945008,\n",
        "       ...\n",
-       "       -50.008888888388796, -50.007999999499916, -50.007111110611035,\n",
-       "       -50.006222221722155, -50.005333332833274,   -50.0044444439444,\n",
-       "        -50.00355555505552,  -50.00266666616664,  -50.00177777727776,\n",
-       "        -50.00088888838888],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=5625))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8a185561-90df-4272-9c79-5c0a72246b0c' class='xr-index-data-in' type='checkbox'/><label for='index-8a185561-90df-4272-9c79-5c0a72246b0c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  -3.00088888885888,   -3.00177777774776,   -3.00266666663664,\n",
-       "         -3.00355555552552,    -3.0044444444144,   -3.00533333330328,\n",
-       "         -3.00622222219216,   -3.00711111108104,   -3.00799999996992,\n",
-       "          -3.0088888888588,\n",
+       "       -54.808888888340796, -54.807999999451916, -54.807111110563035,\n",
+       "       -54.806222221674155,  -54.80533333278528,   -54.8044444438964,\n",
+       "        -54.80355555500752,  -54.80266666611864,  -54.80177777722976,\n",
+       "        -54.80088888834088],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=225))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-07dd3472-33c4-4c52-a4b5-0e6d49abb935' class='xr-index-data-in' type='checkbox'/><label for='index-07dd3472-33c4-4c52-a4b5-0e6d49abb935' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  -5.80088888883088,   -5.80177777771976,   -5.80266666660864,\n",
+       "         -5.80355555549752,    -5.8044444443864,   -5.80533333327528,\n",
+       "         -5.80622222216416,   -5.80711111105304,   -5.80799999994192,\n",
+       "          -5.8088888888308,\n",
        "       ...\n",
-       "         -4.99199999995008, -4.9928888888389595,   -4.99377777772784,\n",
-       "         -4.99466666661672,    -4.9955555555056,   -4.99644444439448,\n",
-       "         -4.99733333328336,   -4.99822222217224,   -4.99911111106112,\n",
-       "            -4.99999999995],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=2250))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cec76e9c-290a-490c-9119-11b79d575c40' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cec76e9c-290a-490c-9119-11b79d575c40' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999999, -49.9999999995, 0.0), resolutions_xy=(0.00088888888888, 0.00088888888888))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.00, 0.00,-60.00|\n",
+       "         -5.99199999994008, -5.9928888888289595,   -5.99377777771784,\n",
+       "         -5.99466666660672,    -5.9955555554956,   -5.99644444438448,\n",
+       "         -5.99733333327336,   -5.99822222216224,   -5.99911111105112,\n",
+       "            -5.99999999994],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=225))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0343b3a9-2d57-4040-8bdc-a9023e39cf5b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0343b3a9-2d57-4040-8bdc-a9023e39cf5b' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999999, -49.9999999995, 0.0), resolutions_xy=(0.00088888888888, 0.00088888888888))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.00, 0.00,-60.00|\n",
        "| 0.00,-0.00, 0.00|\n",
        "| 0.00, 0.00, 1.00|</dd><dt><span>resolution :</span></dt><dd>0.00088888888888</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'stackstac-0d88a6aa99e2eb93c2b68b6e8780c93c' (time: 4,\n",
+       "<xarray.DataArray 'stackstac-33ee8af46de39450b1f613ed794698ab' (time: 4,\n",
        "                                                                band: 2,\n",
-       "                                                                y: 2250, x: 5625)>\n",
-       "dask.array<getitem, shape=(4, 2, 2250, 5625), dtype=float64, chunksize=(1, 1, 1024, 1024), chunktype=numpy.ndarray>\n",
+       "                                                                y: 225, x: 225)>\n",
+       "dask.array<getitem, shape=(4, 2, 225, 225), dtype=float64, chunksize=(1, 1, 225, 225), chunktype=numpy.ndarray>\n",
        "Coordinates: (12/13)\n",
        "  * time            (time) datetime64[ns] 2017-01-01 2018-01-01 ... 2020-01-01\n",
        "    id              (time) <U52 'N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-20...\n",
        "  * band            (band) <U18 'estimates' 'standard_deviation'\n",
-       "  * x               (x) float64 -55.0 -55.0 -55.0 -55.0 ... -50.0 -50.0 -50.0\n",
-       "  * y               (y) float64 -3.001 -3.002 -3.003 ... -4.998 -4.999 -5.0\n",
+       "  * x               (x) float64 -55.0 -55.0 -55.0 -55.0 ... -54.8 -54.8 -54.8\n",
+       "  * y               (y) float64 -5.801 -5.802 -5.803 ... -5.998 -5.999 -6.0\n",
        "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
        "    ...              ...\n",
        "    proj:shape      object {11250}\n",
@@ -1366,21 +1381,29 @@
        "    resolution:  0.00088888888888"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "min_x = -55\n",
-    "min_y = -3.0\n",
-    "max_x = -50.0 \n",
-    "max_y = -5.0\n",
+    "max_y = -5.8\n",
+    "max_x = -54.8 \n",
+    "min_y = -6.0\n",
     "\n",
-    "arr_sub = arr.sel(x=slice(min_x, max_x), y=slice(min_y, max_y))\n",
+    "arr_sub = arr.sel(x=slice(min_x, max_x), y=slice(max_y, min_y))\n",
     "arr_sub"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "1c9f0436-57a6-4e50-98b5-44803b320611",
+   "metadata": {},
+   "source": [
+    "### Accessing the Subset for further Processing"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "f259efda-7614-4121-8a8f-8533b2435334",
@@ -1397,14 +1420,14 @@
    "id": "c1124341-5f4d-4fe7-80f9-381ed11e0ede",
    "metadata": {},
    "source": [
-    "### Masking values based on a condition\n",
+    "#### Masking Values Based on a Condition\n",
     "\n",
     "Below, we will explore how to mask few values based on a condition and plot the time series for the filtered array."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 48,
    "id": "1b9c7539-793e-41b6-86fe-2d9e9abb6d79",
    "metadata": {},
    "outputs": [],
@@ -1424,47 +1447,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 49,
    "id": "c60c3de4-8cd3-46cf-8b2a-99e103250575",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[[274., 215., 295., ..., 221., 181., 143.],\n",
-       "        [248., 285., 284., ..., 165., 190., 165.],\n",
-       "        [244., 224., 274., ..., 188., 182., 162.],\n",
+       "array([[[222., 132., 193., ..., 360., 246., 271.],\n",
+       "        [153., 113., 152., ..., 331., 180., 261.],\n",
+       "        [189., 112., 173., ..., 381., 269., 260.],\n",
        "        ...,\n",
-       "        [351., 382., 376., ...,  22.,  41.,  41.],\n",
-       "        [340., 343., 358., ...,   0.,   0.,  50.],\n",
-       "        [289., 320., 334., ...,   0.,   0.,  51.]],\n",
+       "        [231., 193., 310., ..., 145., 392., 392.],\n",
+       "        [333., 255., 297., ..., 133., 392., 348.],\n",
+       "        [370., 189., 266., ..., 251., 293., 338.]],\n",
        "\n",
-       "       [[219., 184., 278., ..., 219., 237., 214.],\n",
-       "        [213., 227., 275., ..., 188., 236., 239.],\n",
-       "        [241., 233., 313., ..., 205., 210., 197.],\n",
+       "       [[219., 100., 138., ..., 363., 271., 198.],\n",
+       "        [188., 114., 101., ..., 345., 198., 254.],\n",
+       "        [142., 144., 179., ..., 325., 306., 372.],\n",
        "        ...,\n",
-       "        [379., 379., 379., ...,  41.,  68.,  34.],\n",
-       "        [333., 368., 371., ...,   0.,   0.,  39.],\n",
-       "        [329., 365., 379., ...,   0.,   0.,  38.]],\n",
+       "        [353., 302., 379., ..., 162., 385., 385.],\n",
+       "        [332., 348., 321., ..., 151., 385., 314.],\n",
+       "        [375., 243., 283., ..., 201., 336., 342.]],\n",
        "\n",
-       "       [[325., 182., 273., ..., 257., 262., 261.],\n",
-       "        [234., 302., 204., ..., 228., 252., 202.],\n",
-       "        [316., 301., 292., ..., 166., 256., 144.],\n",
+       "       [[271., 255., 219., ..., 363., 278., 286.],\n",
+       "        [253., 321., 116., ..., 226., 333., 233.],\n",
+       "        [222., 277., 176., ..., 169., 355., 190.],\n",
        "        ...,\n",
-       "        [385., 302., 388., ...,   7.,   5.,   4.],\n",
-       "        [323., 327., 300., ...,   0.,   0.,   8.],\n",
-       "        [406., 406., 309., ...,   0.,   0.,  10.]],\n",
+       "        [304., 336., 275., ..., 318., 308., 234.],\n",
+       "        [340., 234., 348., ..., 315., 356., 330.],\n",
+       "        [243., 323., 264., ..., 288., 307., 318.]],\n",
        "\n",
-       "       [[287., 128., 292., ..., 192., 167., 206.],\n",
-       "        [268., 264., 192., ..., 262., 236., 233.],\n",
-       "        [314., 244., 227., ..., 171., 237., 210.],\n",
+       "       [[157., 217., 171., ..., 206., 161., 253.],\n",
+       "        [190., 229., 226., ..., 214., 210., 314.],\n",
+       "        [269., 212., 152., ..., 268., 402., 324.],\n",
        "        ...,\n",
-       "        [379., 232., 343., ...,   5.,   1.,   3.],\n",
-       "        [339., 300., 301., ...,   0.,   0.,   3.],\n",
-       "        [376., 411., 277., ...,   0.,   0.,   2.]]])"
+       "        [247., 292., 246., ..., 373., 321., 159.],\n",
+       "        [329., 200., 318., ..., 278., 284., 255.],\n",
+       "        [237., 361., 238., ..., 245., 292., 316.]]])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1483,7 +1506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 50,
    "id": "91ab0e4d-47c5-4ffb-a602-719c2bb29f76",
    "metadata": {
     "tags": []
@@ -1492,40 +1515,40 @@
     {
      "data": {
       "text/plain": [
-       "array([[[274., 215., 295., ..., 221., 181., 143.],\n",
-       "        [248., 285., 284., ..., 165., 190., 165.],\n",
-       "        [244., 224., 274., ..., 188., 182., 162.],\n",
+       "array([[[222., 132., 193., ..., 360., 246., 271.],\n",
+       "        [153., 113., 152., ..., 331., 180., 261.],\n",
+       "        [189., 112., 173., ..., 381., 269., 260.],\n",
        "        ...,\n",
-       "        [351., 382., 376., ...,  22.,  41.,  41.],\n",
-       "        [340., 343., 358., ...,  nan,  nan,  50.],\n",
-       "        [289., 320., 334., ...,  nan,  nan,  51.]],\n",
+       "        [231., 193., 310., ..., 145., 392., 392.],\n",
+       "        [333., 255., 297., ..., 133., 392., 348.],\n",
+       "        [370., 189., 266., ..., 251., 293., 338.]],\n",
        "\n",
-       "       [[219., 184., 278., ..., 219., 237., 214.],\n",
-       "        [213., 227., 275., ..., 188., 236., 239.],\n",
-       "        [241., 233., 313., ..., 205., 210., 197.],\n",
+       "       [[219., 100., 138., ..., 363., 271., 198.],\n",
+       "        [188., 114., 101., ..., 345., 198., 254.],\n",
+       "        [142., 144., 179., ..., 325., 306., 372.],\n",
        "        ...,\n",
-       "        [379., 379., 379., ...,  41.,  68.,  34.],\n",
-       "        [333., 368., 371., ...,  nan,  nan,  39.],\n",
-       "        [329., 365., 379., ...,  nan,  nan,  38.]],\n",
+       "        [353., 302., 379., ..., 162., 385., 385.],\n",
+       "        [332., 348., 321., ..., 151., 385., 314.],\n",
+       "        [375., 243., 283., ..., 201., 336., 342.]],\n",
        "\n",
-       "       [[325., 182., 273., ..., 257., 262., 261.],\n",
-       "        [234., 302., 204., ..., 228., 252., 202.],\n",
-       "        [316., 301., 292., ..., 166., 256., 144.],\n",
+       "       [[271., 255., 219., ..., 363., 278., 286.],\n",
+       "        [253., 321., 116., ..., 226., 333., 233.],\n",
+       "        [222., 277., 176., ..., 169., 355., 190.],\n",
        "        ...,\n",
-       "        [385., 302., 388., ...,  nan,  nan,  nan],\n",
-       "        [323., 327., 300., ...,  nan,  nan,  nan],\n",
-       "        [406., 406., 309., ...,  nan,  nan,  10.]],\n",
+       "        [304., 336., 275., ..., 318., 308., 234.],\n",
+       "        [340., 234., 348., ..., 315., 356., 330.],\n",
+       "        [243., 323., 264., ..., 288., 307., 318.]],\n",
        "\n",
-       "       [[287., 128., 292., ..., 192., 167., 206.],\n",
-       "        [268., 264., 192., ..., 262., 236., 233.],\n",
-       "        [314., 244., 227., ..., 171., 237., 210.],\n",
+       "       [[157., 217., 171., ..., 206., 161., 253.],\n",
+       "        [190., 229., 226., ..., 214., 210., 314.],\n",
+       "        [269., 212., 152., ..., 268., 402., 324.],\n",
        "        ...,\n",
-       "        [379., 232., 343., ...,  nan,  nan,  nan],\n",
-       "        [339., 300., 301., ...,  nan,  nan,  nan],\n",
-       "        [376., 411., 277., ...,  nan,  nan,  nan]]])"
+       "        [247., 292., 246., ..., 373., 321., 159.],\n",
+       "        [329., 200., 318., ..., 278., 284., 255.],\n",
+       "        [237., 361., 238., ..., 245., 292., 316.]]])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1545,7 +1568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 51,
    "id": "d7101951-869a-40e8-b5f0-79a9b625ba5b",
    "metadata": {},
    "outputs": [
@@ -1557,35 +1580,35 @@
        "        [nan, nan, nan, ..., nan, nan, nan],\n",
        "        ...,\n",
        "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ...,  0.,  0., nan],\n",
-       "        [nan, nan, nan, ...,  0.,  0., nan]],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
        "\n",
        "       [[nan, nan, nan, ..., nan, nan, nan],\n",
        "        [nan, nan, nan, ..., nan, nan, nan],\n",
        "        [nan, nan, nan, ..., nan, nan, nan],\n",
        "        ...,\n",
        "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ...,  0.,  0., nan],\n",
-       "        [nan, nan, nan, ...,  0.,  0., nan]],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
        "\n",
        "       [[nan, nan, nan, ..., nan, nan, nan],\n",
        "        [nan, nan, nan, ..., nan, nan, nan],\n",
        "        [nan, nan, nan, ..., nan, nan, nan],\n",
        "        ...,\n",
-       "        [nan, nan, nan, ...,  7.,  5.,  4.],\n",
-       "        [nan, nan, nan, ...,  0.,  0.,  8.],\n",
-       "        [nan, nan, nan, ...,  0.,  0., 10.]],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
        "\n",
        "       [[nan, nan, nan, ..., nan, nan, nan],\n",
        "        [nan, nan, nan, ..., nan, nan, nan],\n",
        "        [nan, nan, nan, ..., nan, nan, nan],\n",
        "        ...,\n",
-       "        [nan, nan, nan, ...,  5.,  1.,  3.],\n",
-       "        [nan, nan, nan, ...,  0.,  0.,  3.],\n",
-       "        [nan, nan, nan, ...,  0.,  0.,  2.]]])"
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan],\n",
+       "        [nan, nan, nan, ..., nan, nan, nan]]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1600,14 +1623,14 @@
    "id": "cfeeec10-e023-4234-b528-aa7dc87c7e05",
    "metadata": {},
    "source": [
-    "### Plotting Mean time-series for the AGB estimates\n",
+    "#### Plotting Mean Time-Series for the AGB Estimates\n",
     "\n",
     "Below we will look into plotting the mean time-series for the `estimate` band. We will be using the `compute()` module of the `dask` which performs an in-memory computation in a parallel approach."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 52,
    "id": "1d960b78-e15b-4e2a-970f-a07239a49272",
    "metadata": {
     "tags": []
@@ -1616,16 +1639,16 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fc22818e410>]"
+       "[<matplotlib.lines.Line2D at 0x7fc20871f340>]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1649,7 +1672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 53,
    "id": "28aab0c5-e304-4133-b3fe-355fd493cda8",
    "metadata": {},
    "outputs": [],

From 00b1b025d156e93579b93307ad1cac64c6d19bca Mon Sep 17 00:00:00 2001
From: omshinde23 <rajat.shinde@uah.edu>
Date: Thu, 28 Sep 2023 12:06:55 -0700
Subject: [PATCH 5/8] Update ESA CCI notebook for minor edits

---
 docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb b/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
index 8b6b00f8..55e33b56 100644
--- a/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
+++ b/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
@@ -26,14 +26,15 @@
     "### Run This Notebook\n",
     "To access and run this tutorial within MAAP's Algorithm Development Environment (ADE), please refer to the [\"Getting started with the MAAP\"](https://docs.maap-project.org/en/latest/getting_started/getting_started.html) section of our documentation.\n",
     "\n",
+    "Disclaimer: it is highly recommended to run a tutorial within MAAP's ADE, which already includes packages specific to MAAP, such as maap-py. Running the tutorial outside of the MAAP ADE may lead to errors. \n",
+    "\n",
     "### About The Dataset\n",
-    "This dataset comprises estimates of forest Above-Ground Biomass(AGB) for the years 2017, 2018, 2019 and 2020, version 4. They are derived from a combination of Earth Observation (EO) data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.\n",
+    "This dataset comprises estimates of forest Above-Ground Biomass (AGB) for the years 2017, 2018, 2019 and 2020, version 4. They are derived from a combination of Earth Observation (EO) data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team.\n",
     "\n",
     "### Additional Resources\n",
     "* [ESA’s Climate Change Initiative Biomass project](https://climate.esa.int/en/odp/#/project/biomass)\n",
     "* [xarray Documentation](https://docs.xarray.dev/en/stable/index.html)\n",
-    "\n",
-    "Disclaimer: it is highly recommended to run a tutorial within MAAP's ADE, which already includes packages specific to MAAP, such as maap-py. Running the tutorial outside of the MAAP ADE may lead to errors.  "
+    " "
    ]
   },
   {
@@ -1501,7 +1502,7 @@
    "id": "0558d3c7-6b39-4e6b-ab8f-c399cee52a29",
    "metadata": {},
    "source": [
-    "Now, we are going to filter the values which are less than 10 using the `.where` (https://docs.xarray.dev/en/stable/generated/xarray.DataArray.where.html) function. The `.where(<cond>, <True directive>, <False directive>)` function checks a condition and performs specific directives if the condition results in `True` or `False`. In the resulting xarray, all the values less than 10 are going to be `nan` (the default value used if the filtering condition is `True`). We can also replace `nan` by any other value by mentioning it in the `<True directive>`."
+    "Now, we are going to filter the values which are less than 10 using the [DataArray.where](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.where.html) function. The `.where(<cond>, <True directive>, <False directive>)` function checks a condition and performs specific directives if the condition results in `True` or `False`. In the resulting xarray, all the values less than 10 are going to be `nan` (the default value used if the filtering condition is `True`). We can also replace `nan` by any other value by mentioning it in the `<True directive>`."
    ]
   },
   {

From 5c38ca36a0445f16a163392e8a1d94a11c1d311e Mon Sep 17 00:00:00 2001
From: omshinde23 <rajat.shinde@uah.edu>
Date: Thu, 28 Sep 2023 14:54:02 -0700
Subject: [PATCH 6/8] Fixing minor issues

---
 docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb | 255 +++++--------------
 1 file changed, 70 insertions(+), 185 deletions(-)

diff --git a/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb b/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
index 55e33b56..4837fba3 100644
--- a/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
+++ b/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
@@ -48,22 +48,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 2,
    "id": "aea4eb22-0867-4876-bd2b-fe74f5dd7bab",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "#Install required modules\n",
-    "#!mamba install -c conda-forge stackstac\n",
-    "#!pip install pystac_client\n",
-    "#!pip install dask[diagnostics]"
+    "# !mamba install -c conda-forge stackstac\n",
+    "# !pip install pystac_client\n",
+    "# !pip install dask[diagnostics]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 4,
    "id": "bcc3b0ba",
    "metadata": {},
    "outputs": [],
@@ -72,7 +71,6 @@
     "import pystac_client\n",
     "import matplotlib.pyplot as plt \n",
     "import numpy as np\n",
-    "import pystac\n",
     "import xarray"
    ]
   },
@@ -94,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 5,
    "id": "e6917d57",
    "metadata": {},
    "outputs": [],
@@ -121,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 6,
    "id": "73868a97",
    "metadata": {},
    "outputs": [],
@@ -140,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 7,
    "id": "7bb997f0",
    "metadata": {},
    "outputs": [],
@@ -161,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 8,
    "id": "c73362b3-cfb5-48cd-a018-58ca5feb6ef4",
    "metadata": {
     "tags": []
@@ -173,7 +171,7 @@
        "'s3://nasa-maap-data-store/file-staging/nasa-map/ESACCI_Biomass_L4_AGB_V4_100m_2020/N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2020-fv4.0.tif'"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -192,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 9,
    "id": "46bf720c-18d7-4d80-960b-998abd4a5255",
    "metadata": {},
    "outputs": [
@@ -220,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "id": "ed6f411a-4643-40a6-b263-f2169845af6a",
    "metadata": {},
    "outputs": [
@@ -590,7 +588,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;stackstac-0d88a6aa99e2eb93c2b68b6e8780c93c&#x27; (time: 4,\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;stackstac-869d919e6d4941245ca2070e9aa6fcec&#x27; (time: 4,\n",
        "                                                                band: 2,\n",
        "                                                                y: 11250,\n",
        "                                                                x: 11251)&gt;\n",
@@ -601,19 +599,19 @@
        "  * band            (band) &lt;U18 &#x27;estimates&#x27; &#x27;standard_deviation&#x27;\n",
        "  * x               (x) float64 -60.0 -60.0 -60.0 -60.0 ... -50.0 -50.0 -50.0\n",
        "  * y               (y) float64 0.0 -0.0008889 -0.001778 ... -9.998 -9.999\n",
-       "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
+       "    proj:epsg       int64 4326\n",
        "    ...              ...\n",
-       "    proj:shape      object {11250}\n",
+       "    proj:geometry   object {&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.99...\n",
+       "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
        "    proj:bbox       object {0.0, -60.0, -50.0000000001, -9.9999999999}\n",
-       "    proj:epsg       int64 4326\n",
-       "    title           (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
        "    description     (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
+       "    title           (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
        "    epsg            int64 4326\n",
        "Attributes:\n",
        "    spec:        RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999...\n",
        "    crs:         epsg:4326\n",
        "    transform:   | 0.00, 0.00,-60.00|\\n| 0.00,-0.00, 0.00|\\n| 0.00, 0.00, 1.00|\n",
-       "    resolution:  0.00088888888888</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'stackstac-0d88a6aa99e2eb93c2b68b6e8780c93c'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>band</span>: 2</li><li><span class='xr-has-index'>y</span>: 11250</li><li><span class='xr-has-index'>x</span>: 11251</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5afbc5ab-29db-44c1-9aad-0abd93db3099' class='xr-array-in' type='checkbox' checked><label for='section-5afbc5ab-29db-44c1-9aad-0abd93db3099' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 1024, 1024), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    resolution:  0.00088888888888</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'stackstac-869d919e6d4941245ca2070e9aa6fcec'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>band</span>: 2</li><li><span class='xr-has-index'>y</span>: 11250</li><li><span class='xr-has-index'>x</span>: 11251</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e1489931-933f-4edc-9ce2-334225091394' class='xr-array-in' type='checkbox' checked><label for='section-e1489931-933f-4edc-9ce2-334225091394' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 1024, 1024), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -752,18 +750,18 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-68941179-f4f7-464d-8150-894a3648980d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-68941179-f4f7-464d-8150-894a3648980d' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-01 ... 2020-01-01</div><input id='attrs-70a5a18b-3a79-4316-abd9-6e9591aca463' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70a5a18b-3a79-4316-abd9-6e9591aca463' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6d35674-1cf1-407d-aff7-2e73b93b40c4' class='xr-var-data-in' type='checkbox'><label for='data-e6d35674-1cf1-407d-aff7-2e73b93b40c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-50c67922-5919-469d-9cf3-d88b6d51f0a1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-50c67922-5919-469d-9cf3-d88b6d51f0a1' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-01 ... 2020-01-01</div><input id='attrs-95df36a1-6b61-4e45-a37b-7208e3cde230' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95df36a1-6b61-4e45-a37b-7208e3cde230' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e983bf1e-7fe0-4f0a-8eb3-2f02cffa24ba' class='xr-var-data-in' type='checkbox'><label for='data-e983bf1e-7fe0-4f0a-8eb3-2f02cffa24ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U52</div><div class='xr-var-preview xr-preview'>&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-M...</div><input id='attrs-e70233fc-9336-4f3b-a0a6-2833ce771329' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e70233fc-9336-4f3b-a0a6-2833ce771329' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c8dc5ab-36d0-4b9f-8cf0-331e87d71cd0' class='xr-var-data-in' type='checkbox'><label for='data-3c8dc5ab-36d0-4b9f-8cf0-331e87d71cd0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2017-fv4.0&#x27;,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U52</div><div class='xr-var-preview xr-preview'>&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-M...</div><input id='attrs-7e4a9170-34c1-4710-959e-ccfa05b9bad6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e4a9170-34c1-4710-959e-ccfa05b9bad6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2106a91d-17bf-47c5-8a7b-6770746eb06f' class='xr-var-data-in' type='checkbox'><label for='data-2106a91d-17bf-47c5-8a7b-6770746eb06f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2017-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2018-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2019-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2020-fv4.0&#x27;],\n",
-       "      dtype=&#x27;&lt;U52&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;estimates&#x27; &#x27;standard_deviation&#x27;</div><input id='attrs-e25dae1c-b198-436e-92b8-05cf8f094838' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e25dae1c-b198-436e-92b8-05cf8f094838' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d51d5f56-893d-40ff-a44d-1646576fbfdc' class='xr-var-data-in' type='checkbox'><label for='data-d51d5f56-893d-40ff-a44d-1646576fbfdc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -60.0 -60.0 ... -50.0 -50.0</div><input id='attrs-b8206be8-f64c-41da-a743-4804603575ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8206be8-f64c-41da-a743-4804603575ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9168accf-ccd9-49ff-b661-807282e62f3a' class='xr-var-data-in' type='checkbox'><label for='data-9168accf-ccd9-49ff-b661-807282e62f3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-60.000889, -60.      , -59.999111, ..., -50.002667, -50.001778,\n",
-       "       -50.000889])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 -0.0008889 ... -9.998 -9.999</div><input id='attrs-1959eba2-668c-4859-89f3-6a2f6ec0588e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1959eba2-668c-4859-89f3-6a2f6ec0588e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ac215a0-ec1a-4bd0-8ff3-f8799a33958a' class='xr-var-data-in' type='checkbox'><label for='data-1ac215a0-ec1a-4bd0-8ff3-f8799a33958a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.000000e+00, -8.888889e-04, -1.777778e-03, ..., -9.997333e+00,\n",
-       "       -9.998222e+00, -9.999111e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:transform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, 1.0, -0.00088888888888, -6...</div><input id='attrs-d7f8c229-d444-44f3-b4f9-a0a274d2aa85' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d7f8c229-d444-44f3-b4f9-a0a274d2aa85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f16c7d44-6496-4996-a958-abaa902f3020' class='xr-var-data-in' type='checkbox'><label for='data-f16c7d44-6496-4996-a958-abaa902f3020' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888888}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:geometry</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates...</div><input id='attrs-a7ef44c6-2aec-4018-bda3-70bce146ca53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7ef44c6-2aec-4018-bda3-70bce146ca53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcc6e0c2-fe12-4100-925e-8b9a57b819a8' class='xr-var-data-in' type='checkbox'><label for='data-bcc6e0c2-fe12-4100-925e-8b9a57b819a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.9999999999], [-50.0000000001, -9.9999999999], [-50.0000000001, 0.0], [-60.0, 0.0], [-60.0, -9.9999999999]]]},\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:shape</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{11250}</div><input id='attrs-612d53a5-94ae-4e20-836d-acbe26df8777' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-612d53a5-94ae-4e20-836d-acbe26df8777' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0de61d4-5057-4d47-a393-e4d00fc7a292' class='xr-var-data-in' type='checkbox'><label for='data-e0de61d4-5057-4d47-a393-e4d00fc7a292' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({11250}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:bbox</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, -60.0, -50.0000000001, -9....</div><input id='attrs-52cf1f2c-ee08-4d5f-8c5b-e6229bfba1a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-52cf1f2c-ee08-4d5f-8c5b-e6229bfba1a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-647086be-7e5d-408a-87b9-bbbdf8e3884a' class='xr-var-data-in' type='checkbox'><label for='data-647086be-7e5d-408a-87b9-bbbdf8e3884a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, -60.0, -50.0000000001, -9.9999999999}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-06fad089-d3c0-480f-bc71-00ffcca55ed2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06fad089-d3c0-480f-bc71-00ffcca55ed2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcdb82db-1cbd-4415-a7db-3c5ab8da9113' class='xr-var-data-in' type='checkbox'><label for='data-bcdb82db-1cbd-4415-a7db-3c5ab8da9113' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-03ff452c-51bc-499d-a221-90ff4b075bd1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-03ff452c-51bc-499d-a221-90ff4b075bd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8657e9c9-ae92-4ea9-848f-666713d717fa' class='xr-var-data-in' type='checkbox'><label for='data-8657e9c9-ae92-4ea9-848f-666713d717fa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
-       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>description</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-5f5cb2a1-9268-42d4-a510-f2ea15e8355f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f5cb2a1-9268-42d4-a510-f2ea15e8355f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8261839-8254-4ea5-988f-390d7dc5f164' class='xr-var-data-in' type='checkbox'><label for='data-d8261839-8254-4ea5-988f-390d7dc5f164' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
-       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-67b7f004-0a3f-490f-8042-9648d367086f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-67b7f004-0a3f-490f-8042-9648d367086f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-266a3a95-aefe-4feb-845f-9be66bc209c7' class='xr-var-data-in' type='checkbox'><label for='data-266a3a95-aefe-4feb-845f-9be66bc209c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-68ca9d35-27ec-42bd-8690-dc697da71452' class='xr-section-summary-in' type='checkbox'  ><label for='section-68ca9d35-27ec-42bd-8690-dc697da71452' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3d3767ec-d5f9-4f21-ada8-4788aa77add0' class='xr-index-data-in' type='checkbox'/><label for='index-3d3767ec-d5f9-4f21-ada8-4788aa77add0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4de854d4-78c4-48e4-ace7-f13c695d2cf4' class='xr-index-data-in' type='checkbox'/><label for='index-4de854d4-78c4-48e4-ace7-f13c695d2cf4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-25ad9d76-4bb5-4378-bc25-14ae564c1151' class='xr-index-data-in' type='checkbox'/><label for='index-25ad9d76-4bb5-4378-bc25-14ae564c1151' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -60.00088888828888, -59.999999999399996, -59.999111110511116,\n",
+       "      dtype=&#x27;&lt;U52&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;estimates&#x27; &#x27;standard_deviation&#x27;</div><input id='attrs-374fc37c-a796-4692-a025-c12154553b1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-374fc37c-a796-4692-a025-c12154553b1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81c2fd81-3f9c-497d-880d-f52b836eaa58' class='xr-var-data-in' type='checkbox'><label for='data-81c2fd81-3f9c-497d-880d-f52b836eaa58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -60.0 -60.0 ... -50.0 -50.0</div><input id='attrs-bfc0d264-7640-4c40-a5a0-dea5552bd6c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bfc0d264-7640-4c40-a5a0-dea5552bd6c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10b982a7-26d7-466d-abcc-72248031afc0' class='xr-var-data-in' type='checkbox'><label for='data-10b982a7-26d7-466d-abcc-72248031afc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-60.000889, -60.      , -59.999111, ..., -50.002667, -50.001778,\n",
+       "       -50.000889])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 -0.0008889 ... -9.998 -9.999</div><input id='attrs-18cb7706-f3f2-442c-9a28-a856f48fcaa2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-18cb7706-f3f2-442c-9a28-a856f48fcaa2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78e3a086-a857-4df0-bbff-c7ea3a1e9585' class='xr-var-data-in' type='checkbox'><label for='data-78e3a086-a857-4df0-bbff-c7ea3a1e9585' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.000000e+00, -8.888889e-04, -1.777778e-03, ..., -9.997333e+00,\n",
+       "       -9.998222e+00, -9.999111e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-31863655-6ef0-448e-ab13-167b69ffd38e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31863655-6ef0-448e-ab13-167b69ffd38e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49fbebb6-3de9-46f4-9cec-deb6895a8515' class='xr-var-data-in' type='checkbox'><label for='data-49fbebb6-3de9-46f4-9cec-deb6895a8515' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:shape</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{11250}</div><input id='attrs-9561d524-54b0-4647-8040-4b51e6b65dd6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9561d524-54b0-4647-8040-4b51e6b65dd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79f40d8b-907c-45d4-8b92-eeea5abe8a40' class='xr-var-data-in' type='checkbox'><label for='data-79f40d8b-907c-45d4-8b92-eeea5abe8a40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({11250}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:geometry</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates...</div><input id='attrs-28c6113c-3b16-4ce1-9a8c-7a1e6b4602f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28c6113c-3b16-4ce1-9a8c-7a1e6b4602f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ce5bb2a-fab4-4f48-8b87-95b715861301' class='xr-var-data-in' type='checkbox'><label for='data-7ce5bb2a-fab4-4f48-8b87-95b715861301' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.9999999999], [-50.0000000001, -9.9999999999], [-50.0000000001, 0.0], [-60.0, 0.0], [-60.0, -9.9999999999]]]},\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:transform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, 1.0, -0.00088888888888, -6...</div><input id='attrs-f3acf9c0-556d-4dc4-a6cb-08aaa20f1667' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f3acf9c0-556d-4dc4-a6cb-08aaa20f1667' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4300e7c7-1e14-4bb7-824d-4df949d2063a' class='xr-var-data-in' type='checkbox'><label for='data-4300e7c7-1e14-4bb7-824d-4df949d2063a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888888}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:bbox</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, -60.0, -50.0000000001, -9....</div><input id='attrs-ccb61515-3c7c-4f9d-a913-9346956b51ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ccb61515-3c7c-4f9d-a913-9346956b51ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ac89429-4c5e-4e1c-b961-cbfb3227d839' class='xr-var-data-in' type='checkbox'><label for='data-1ac89429-4c5e-4e1c-b961-cbfb3227d839' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, -60.0, -50.0000000001, -9.9999999999}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>description</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-ba82828a-86d8-4713-ba68-58ad3f16b850' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba82828a-86d8-4713-ba68-58ad3f16b850' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8307ae69-0da2-41d0-add8-8d42116d1b7e' class='xr-var-data-in' type='checkbox'><label for='data-8307ae69-0da2-41d0-add8-8d42116d1b7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
+       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-09139057-d58f-4e26-87e5-ff43462e476b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09139057-d58f-4e26-87e5-ff43462e476b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e64a175d-990d-4a32-9f7f-a3db93262517' class='xr-var-data-in' type='checkbox'><label for='data-e64a175d-990d-4a32-9f7f-a3db93262517' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
+       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-c01bab80-aff3-4aaa-bbf5-a63dcb7a8d6e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c01bab80-aff3-4aaa-bbf5-a63dcb7a8d6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57871752-51e9-4c31-a47c-73b181b39421' class='xr-var-data-in' type='checkbox'><label for='data-57871752-51e9-4c31-a47c-73b181b39421' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d14a9c63-4b2d-4b59-83f8-0515d239b7f4' class='xr-section-summary-in' type='checkbox'  ><label for='section-d14a9c63-4b2d-4b59-83f8-0515d239b7f4' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0897b77b-2044-4ffe-8989-c056e918f64f' class='xr-index-data-in' type='checkbox'/><label for='index-0897b77b-2044-4ffe-8989-c056e918f64f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c2715891-21d4-4525-9745-14577b6742cb' class='xr-index-data-in' type='checkbox'/><label for='index-c2715891-21d4-4525-9745-14577b6742cb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9524e479-dcf7-42ef-b16a-17977251938f' class='xr-index-data-in' type='checkbox'/><label for='index-9524e479-dcf7-42ef-b16a-17977251938f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -60.00088888828888, -59.999999999399996, -59.999111110511116,\n",
        "       -59.998222221622235, -59.997333332733355, -59.996444443844474,\n",
        "       -59.995555554955594,  -59.99466666606671,  -59.99377777717784,\n",
        "        -59.99288888828896,\n",
@@ -772,7 +770,7 @@
        "       -50.006222221722155, -50.005333332833274,   -50.0044444439444,\n",
        "        -50.00355555505552,  -50.00266666616664,  -50.00177777727776,\n",
        "        -50.00088888838888],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=11251))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3a769702-4546-434a-b256-c69767e91060' class='xr-index-data-in' type='checkbox'/><label for='index-3a769702-4546-434a-b256-c69767e91060' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0,  -0.00088888888888,  -0.00177777777776,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=11251))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b24489c3-6cf1-4bb1-a09a-2e60715d3168' class='xr-index-data-in' type='checkbox'/><label for='index-b24489c3-6cf1-4bb1-a09a-2e60715d3168' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0,  -0.00088888888888,  -0.00177777777776,\n",
        "        -0.00266666666664,  -0.00355555555552,   -0.0044444444444,\n",
        "        -0.00533333333328,  -0.00622222222216,  -0.00711111111104,\n",
        "        -0.00799999999992,\n",
@@ -781,12 +779,12 @@
        "        -9.99377777767784,  -9.99466666656672,   -9.9955555554556,\n",
        "        -9.99644444434448,  -9.99733333323336,  -9.99822222212224,\n",
        "        -9.99911111101112],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=11250))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1115830d-193c-4bfa-8d28-0893bd9f561c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1115830d-193c-4bfa-8d28-0893bd9f561c' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999999, -49.9999999995, 0.0), resolutions_xy=(0.00088888888888, 0.00088888888888))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.00, 0.00,-60.00|\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=11250))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ccd5a2ca-0bea-486f-b763-b522bdbca3ee' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ccd5a2ca-0bea-486f-b763-b522bdbca3ee' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999999, -49.9999999995, 0.0), resolutions_xy=(0.00088888888888, 0.00088888888888))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.00, 0.00,-60.00|\n",
        "| 0.00,-0.00, 0.00|\n",
        "| 0.00, 0.00, 1.00|</dd><dt><span>resolution :</span></dt><dd>0.00088888888888</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'stackstac-0d88a6aa99e2eb93c2b68b6e8780c93c' (time: 4,\n",
+       "<xarray.DataArray 'stackstac-869d919e6d4941245ca2070e9aa6fcec' (time: 4,\n",
        "                                                                band: 2,\n",
        "                                                                y: 11250,\n",
        "                                                                x: 11251)>\n",
@@ -797,13 +795,13 @@
        "  * band            (band) <U18 'estimates' 'standard_deviation'\n",
        "  * x               (x) float64 -60.0 -60.0 -60.0 -60.0 ... -50.0 -50.0 -50.0\n",
        "  * y               (y) float64 0.0 -0.0008889 -0.001778 ... -9.998 -9.999\n",
-       "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
+       "    proj:epsg       int64 4326\n",
        "    ...              ...\n",
-       "    proj:shape      object {11250}\n",
+       "    proj:geometry   object {'type': 'Polygon', 'coordinates': [[[-60.0, -9.99...\n",
+       "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
        "    proj:bbox       object {0.0, -60.0, -50.0000000001, -9.9999999999}\n",
-       "    proj:epsg       int64 4326\n",
-       "    title           (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
        "    description     (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
+       "    title           (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
        "    epsg            int64 4326\n",
        "Attributes:\n",
        "    spec:        RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999...\n",
@@ -812,7 +810,7 @@
        "    resolution:  0.00088888888888"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -831,7 +829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 11,
    "id": "e9d173bd-fbfc-4f5c-b0bb-d948b49259ce",
    "metadata": {
     "tags": []
@@ -1203,7 +1201,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;stackstac-33ee8af46de39450b1f613ed794698ab&#x27; (time: 4,\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;stackstac-869d919e6d4941245ca2070e9aa6fcec&#x27; (time: 4,\n",
        "                                                                band: 2,\n",
        "                                                                y: 225, x: 225)&gt;\n",
        "dask.array&lt;getitem, shape=(4, 2, 225, 225), dtype=float64, chunksize=(1, 1, 225, 225), chunktype=numpy.ndarray&gt;\n",
@@ -1213,19 +1211,19 @@
        "  * band            (band) &lt;U18 &#x27;estimates&#x27; &#x27;standard_deviation&#x27;\n",
        "  * x               (x) float64 -55.0 -55.0 -55.0 -55.0 ... -54.8 -54.8 -54.8\n",
        "  * y               (y) float64 -5.801 -5.802 -5.803 ... -5.998 -5.999 -6.0\n",
-       "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
+       "    proj:epsg       int64 4326\n",
        "    ...              ...\n",
-       "    proj:shape      object {11250}\n",
+       "    proj:geometry   object {&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.99...\n",
+       "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
        "    proj:bbox       object {0.0, -60.0, -50.0000000001, -9.9999999999}\n",
-       "    proj:epsg       int64 4326\n",
-       "    title           (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
        "    description     (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
+       "    title           (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
        "    epsg            int64 4326\n",
        "Attributes:\n",
        "    spec:        RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999...\n",
        "    crs:         epsg:4326\n",
        "    transform:   | 0.00, 0.00,-60.00|\\n| 0.00,-0.00, 0.00|\\n| 0.00, 0.00, 1.00|\n",
-       "    resolution:  0.00088888888888</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'stackstac-33ee8af46de39450b1f613ed794698ab'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>band</span>: 2</li><li><span class='xr-has-index'>y</span>: 225</li><li><span class='xr-has-index'>x</span>: 225</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0269dadd-9124-417f-91d9-df4f23e3c95a' class='xr-array-in' type='checkbox' checked><label for='section-0269dadd-9124-417f-91d9-df4f23e3c95a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 225, 225), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    resolution:  0.00088888888888</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'stackstac-869d919e6d4941245ca2070e9aa6fcec'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>band</span>: 2</li><li><span class='xr-has-index'>y</span>: 225</li><li><span class='xr-has-index'>x</span>: 225</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5af97235-a439-427e-8f49-30d302f7001d' class='xr-array-in' type='checkbox' checked><label for='section-5af97235-a439-427e-8f49-30d302f7001d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 225, 225), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1324,17 +1322,17 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-59b9ac53-76c0-4463-adb0-524ed98e9da7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-59b9ac53-76c0-4463-adb0-524ed98e9da7' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-01 ... 2020-01-01</div><input id='attrs-a0d369fc-b85b-4490-a793-617b5570def6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a0d369fc-b85b-4490-a793-617b5570def6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce3e69e5-a3c2-42fa-8012-610f693f334c' class='xr-var-data-in' type='checkbox'><label for='data-ce3e69e5-a3c2-42fa-8012-610f693f334c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-3aacbb11-0fe6-456a-8b9c-e3136cc2f1d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3aacbb11-0fe6-456a-8b9c-e3136cc2f1d4' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-01 ... 2020-01-01</div><input id='attrs-8fab4255-e78d-4ba7-8b92-4181efa9996b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8fab4255-e78d-4ba7-8b92-4181efa9996b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03a3fcc2-f392-41f4-a4b4-87adb5866b85' class='xr-var-data-in' type='checkbox'><label for='data-03a3fcc2-f392-41f4-a4b4-87adb5866b85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U52</div><div class='xr-var-preview xr-preview'>&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-M...</div><input id='attrs-34ad2ed3-0d7a-42da-b5d1-59370c65989c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34ad2ed3-0d7a-42da-b5d1-59370c65989c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20bade00-1654-4638-81ec-435b9f1f3a6e' class='xr-var-data-in' type='checkbox'><label for='data-20bade00-1654-4638-81ec-435b9f1f3a6e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2017-fv4.0&#x27;,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U52</div><div class='xr-var-preview xr-preview'>&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-M...</div><input id='attrs-f6cc8327-02a6-42bc-ab9f-a4b6579187ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6cc8327-02a6-42bc-ab9f-a4b6579187ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38c18e72-5ead-4e36-9988-498cd34adbe2' class='xr-var-data-in' type='checkbox'><label for='data-38c18e72-5ead-4e36-9988-498cd34adbe2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2017-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2018-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2019-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2020-fv4.0&#x27;],\n",
-       "      dtype=&#x27;&lt;U52&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;estimates&#x27; &#x27;standard_deviation&#x27;</div><input id='attrs-4a94c41b-3aa3-4350-ae0d-c3b027bc45b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a94c41b-3aa3-4350-ae0d-c3b027bc45b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f6e24a3-df03-480b-a594-f62c706e59db' class='xr-var-data-in' type='checkbox'><label for='data-9f6e24a3-df03-480b-a594-f62c706e59db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-55.0 -55.0 -55.0 ... -54.8 -54.8</div><input id='attrs-a13c9822-4eb3-4d5b-885a-3622f9b35652' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a13c9822-4eb3-4d5b-885a-3622f9b35652' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06ac1481-906f-4645-a65b-bc2045f01ceb' class='xr-var-data-in' type='checkbox'><label for='data-06ac1481-906f-4645-a65b-bc2045f01ceb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-55.      , -54.999111, -54.998222, ..., -54.802667, -54.801778,\n",
-       "       -54.800889])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.801 -5.802 ... -5.999 -6.0</div><input id='attrs-af3092db-5d0e-4b80-a4a6-5456bb9b4c4a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af3092db-5d0e-4b80-a4a6-5456bb9b4c4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6da8d1a9-5667-4296-ba60-eb88c7b028f9' class='xr-var-data-in' type='checkbox'><label for='data-6da8d1a9-5667-4296-ba60-eb88c7b028f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-5.800889, -5.801778, -5.802667, ..., -5.998222, -5.999111, -6.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:transform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, 1.0, -0.00088888888888, -6...</div><input id='attrs-62a188f7-507c-4d09-9cb2-7a80c79f9208' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-62a188f7-507c-4d09-9cb2-7a80c79f9208' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99f1e234-8c9f-455f-b070-b71410dd6312' class='xr-var-data-in' type='checkbox'><label for='data-99f1e234-8c9f-455f-b070-b71410dd6312' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888888}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:geometry</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates...</div><input id='attrs-6468b19e-8935-45e5-8929-532430813ea3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6468b19e-8935-45e5-8929-532430813ea3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-202ff2c0-3f9b-47e8-8eef-38503188bf31' class='xr-var-data-in' type='checkbox'><label for='data-202ff2c0-3f9b-47e8-8eef-38503188bf31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.9999999999], [-50.0000000001, -9.9999999999], [-50.0000000001, 0.0], [-60.0, 0.0], [-60.0, -9.9999999999]]]},\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:shape</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{11250}</div><input id='attrs-f956f951-f48a-4bfe-95c9-102c0d300b50' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f956f951-f48a-4bfe-95c9-102c0d300b50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d97789b0-3f23-44e6-af73-21c036552147' class='xr-var-data-in' type='checkbox'><label for='data-d97789b0-3f23-44e6-af73-21c036552147' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({11250}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:bbox</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, -60.0, -50.0000000001, -9....</div><input id='attrs-4d7729f9-bd9b-48d2-a2fa-accc10667959' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d7729f9-bd9b-48d2-a2fa-accc10667959' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-885f5085-4972-4ee2-bbbd-39f1caa8441c' class='xr-var-data-in' type='checkbox'><label for='data-885f5085-4972-4ee2-bbbd-39f1caa8441c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, -60.0, -50.0000000001, -9.9999999999}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-f9e00aa3-4f79-4c03-bae5-067e7e27bccb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9e00aa3-4f79-4c03-bae5-067e7e27bccb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3619bda0-fe37-44e6-b3cc-2f923884e710' class='xr-var-data-in' type='checkbox'><label for='data-3619bda0-fe37-44e6-b3cc-2f923884e710' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-d4b9dab6-957d-4a40-af64-485508ebffee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4b9dab6-957d-4a40-af64-485508ebffee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbb88cd9-70e2-4296-a133-9076285fc3cf' class='xr-var-data-in' type='checkbox'><label for='data-fbb88cd9-70e2-4296-a133-9076285fc3cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
-       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>description</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-577cfd98-42ce-4d5d-b36b-7a80da43839d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-577cfd98-42ce-4d5d-b36b-7a80da43839d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b94dfff2-3017-4b1e-a403-fceaca2bbfa9' class='xr-var-data-in' type='checkbox'><label for='data-b94dfff2-3017-4b1e-a403-fceaca2bbfa9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
-       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-c0734cbe-a34e-4e34-a3c8-fa5bedfb9302' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c0734cbe-a34e-4e34-a3c8-fa5bedfb9302' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bd387cc-3d9c-4d71-b08f-b39ef4baba62' class='xr-var-data-in' type='checkbox'><label for='data-2bd387cc-3d9c-4d71-b08f-b39ef4baba62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ddfb6deb-ca33-4671-80e9-05c74336de7e' class='xr-section-summary-in' type='checkbox'  ><label for='section-ddfb6deb-ca33-4671-80e9-05c74336de7e' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fb2c616d-fc6b-4030-8219-32ff7f1a6d4f' class='xr-index-data-in' type='checkbox'/><label for='index-fb2c616d-fc6b-4030-8219-32ff7f1a6d4f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ed41731e-5101-46b0-9687-e50febcc0df9' class='xr-index-data-in' type='checkbox'/><label for='index-ed41731e-5101-46b0-9687-e50febcc0df9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e2fb26bd-1b7a-4cce-b765-371ed8daa85a' class='xr-index-data-in' type='checkbox'/><label for='index-e2fb26bd-1b7a-4cce-b765-371ed8daa85a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    -54.99999999945,  -54.99911111056112, -54.998222221672236,\n",
+       "      dtype=&#x27;&lt;U52&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;estimates&#x27; &#x27;standard_deviation&#x27;</div><input id='attrs-69663a2e-6ede-4f45-a9a6-5fc8a55120c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69663a2e-6ede-4f45-a9a6-5fc8a55120c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-238f133b-dd4c-4da0-bd50-cca8921e7791' class='xr-var-data-in' type='checkbox'><label for='data-238f133b-dd4c-4da0-bd50-cca8921e7791' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-55.0 -55.0 -55.0 ... -54.8 -54.8</div><input id='attrs-6c69fb00-b5d2-4a83-9ec1-2cd2cdc4b0f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c69fb00-b5d2-4a83-9ec1-2cd2cdc4b0f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf2edc44-261d-4fc6-b198-81329dc062dc' class='xr-var-data-in' type='checkbox'><label for='data-cf2edc44-261d-4fc6-b198-81329dc062dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-55.      , -54.999111, -54.998222, ..., -54.802667, -54.801778,\n",
+       "       -54.800889])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.801 -5.802 ... -5.999 -6.0</div><input id='attrs-7f0e8ef2-3d71-4b59-bba1-4bce595ce575' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f0e8ef2-3d71-4b59-bba1-4bce595ce575' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1dfef45-2929-4701-81b1-cec8d92d7668' class='xr-var-data-in' type='checkbox'><label for='data-a1dfef45-2929-4701-81b1-cec8d92d7668' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-5.800889, -5.801778, -5.802667, ..., -5.998222, -5.999111, -6.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-3e2a11b4-73dd-45da-a075-8fe4615a7434' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e2a11b4-73dd-45da-a075-8fe4615a7434' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13abd89f-e71b-47a7-80bf-27f61f5754b8' class='xr-var-data-in' type='checkbox'><label for='data-13abd89f-e71b-47a7-80bf-27f61f5754b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:shape</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{11250}</div><input id='attrs-233978cc-ee9c-4ff4-85e4-bf88bbe032f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-233978cc-ee9c-4ff4-85e4-bf88bbe032f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e96a290d-7b41-4ec3-9052-35fd9ade859c' class='xr-var-data-in' type='checkbox'><label for='data-e96a290d-7b41-4ec3-9052-35fd9ade859c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({11250}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:geometry</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates...</div><input id='attrs-ed318f4f-098e-42a7-815f-459357c5172e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed318f4f-098e-42a7-815f-459357c5172e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8065704-0489-4b05-ae75-fce03a1636cb' class='xr-var-data-in' type='checkbox'><label for='data-c8065704-0489-4b05-ae75-fce03a1636cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.9999999999], [-50.0000000001, -9.9999999999], [-50.0000000001, 0.0], [-60.0, 0.0], [-60.0, -9.9999999999]]]},\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:transform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, 1.0, -0.00088888888888, -6...</div><input id='attrs-97d974ea-5f58-45e9-ba01-b69334311b11' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-97d974ea-5f58-45e9-ba01-b69334311b11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64a23e01-76bb-4553-9cb3-66af3a6d521f' class='xr-var-data-in' type='checkbox'><label for='data-64a23e01-76bb-4553-9cb3-66af3a6d521f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888888}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:bbox</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, -60.0, -50.0000000001, -9....</div><input id='attrs-1fdb9dac-5705-45d3-9481-6835c1bc9fdc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1fdb9dac-5705-45d3-9481-6835c1bc9fdc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9f13a89-0aca-4dd7-b153-71b54d756507' class='xr-var-data-in' type='checkbox'><label for='data-f9f13a89-0aca-4dd7-b153-71b54d756507' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, -60.0, -50.0000000001, -9.9999999999}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>description</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-a705cf10-f90b-4111-a58c-bce255b81b40' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a705cf10-f90b-4111-a58c-bce255b81b40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dde75c14-1640-4622-9769-bbd85739f052' class='xr-var-data-in' type='checkbox'><label for='data-dde75c14-1640-4622-9769-bbd85739f052' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
+       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-b67c8a10-ef59-4e8b-80fc-194df1636e34' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b67c8a10-ef59-4e8b-80fc-194df1636e34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55024521-faec-42aa-94ef-7d0c35d226ee' class='xr-var-data-in' type='checkbox'><label for='data-55024521-faec-42aa-94ef-7d0c35d226ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
+       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-122662a8-c35c-4e76-af3f-9642c4c02bad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-122662a8-c35c-4e76-af3f-9642c4c02bad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a710e70d-f5d7-4766-bd6e-574e28661b1a' class='xr-var-data-in' type='checkbox'><label for='data-a710e70d-f5d7-4766-bd6e-574e28661b1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f1dc5a9e-2917-4826-8af9-02d99f46f621' class='xr-section-summary-in' type='checkbox'  ><label for='section-f1dc5a9e-2917-4826-8af9-02d99f46f621' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b9f27960-83ac-42c1-a902-dc29d686ff9b' class='xr-index-data-in' type='checkbox'/><label for='index-b9f27960-83ac-42c1-a902-dc29d686ff9b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c298849d-f1bb-4b3c-86c6-c1c64f18cd40' class='xr-index-data-in' type='checkbox'/><label for='index-c298849d-f1bb-4b3c-86c6-c1c64f18cd40' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c07a2743-b9cf-44a9-8a70-546ea9514d77' class='xr-index-data-in' type='checkbox'/><label for='index-c07a2743-b9cf-44a9-8a70-546ea9514d77' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    -54.99999999945,  -54.99911111056112, -54.998222221672236,\n",
        "       -54.997333332783356, -54.996444443894475, -54.995555555005595,\n",
        "       -54.994666666116714, -54.993777777227834,  -54.99288888833896,\n",
        "        -54.99199999945008,\n",
@@ -1343,7 +1341,7 @@
        "       -54.806222221674155,  -54.80533333278528,   -54.8044444438964,\n",
        "        -54.80355555500752,  -54.80266666611864,  -54.80177777722976,\n",
        "        -54.80088888834088],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=225))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-07dd3472-33c4-4c52-a4b5-0e6d49abb935' class='xr-index-data-in' type='checkbox'/><label for='index-07dd3472-33c4-4c52-a4b5-0e6d49abb935' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  -5.80088888883088,   -5.80177777771976,   -5.80266666660864,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=225))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dd863825-88da-418a-a385-541621272213' class='xr-index-data-in' type='checkbox'/><label for='index-dd863825-88da-418a-a385-541621272213' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  -5.80088888883088,   -5.80177777771976,   -5.80266666660864,\n",
        "         -5.80355555549752,    -5.8044444443864,   -5.80533333327528,\n",
        "         -5.80622222216416,   -5.80711111105304,   -5.80799999994192,\n",
        "          -5.8088888888308,\n",
@@ -1352,12 +1350,12 @@
        "         -5.99466666660672,    -5.9955555554956,   -5.99644444438448,\n",
        "         -5.99733333327336,   -5.99822222216224,   -5.99911111105112,\n",
        "            -5.99999999994],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=225))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0343b3a9-2d57-4040-8bdc-a9023e39cf5b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0343b3a9-2d57-4040-8bdc-a9023e39cf5b' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999999, -49.9999999995, 0.0), resolutions_xy=(0.00088888888888, 0.00088888888888))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.00, 0.00,-60.00|\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=225))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ece6ca35-f901-4d46-ac7e-856ec809f0d1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ece6ca35-f901-4d46-ac7e-856ec809f0d1' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999999, -49.9999999995, 0.0), resolutions_xy=(0.00088888888888, 0.00088888888888))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.00, 0.00,-60.00|\n",
        "| 0.00,-0.00, 0.00|\n",
        "| 0.00, 0.00, 1.00|</dd><dt><span>resolution :</span></dt><dd>0.00088888888888</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'stackstac-33ee8af46de39450b1f613ed794698ab' (time: 4,\n",
+       "<xarray.DataArray 'stackstac-869d919e6d4941245ca2070e9aa6fcec' (time: 4,\n",
        "                                                                band: 2,\n",
        "                                                                y: 225, x: 225)>\n",
        "dask.array<getitem, shape=(4, 2, 225, 225), dtype=float64, chunksize=(1, 1, 225, 225), chunktype=numpy.ndarray>\n",
@@ -1367,13 +1365,13 @@
        "  * band            (band) <U18 'estimates' 'standard_deviation'\n",
        "  * x               (x) float64 -55.0 -55.0 -55.0 -55.0 ... -54.8 -54.8 -54.8\n",
        "  * y               (y) float64 -5.801 -5.802 -5.803 ... -5.998 -5.999 -6.0\n",
-       "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
+       "    proj:epsg       int64 4326\n",
        "    ...              ...\n",
-       "    proj:shape      object {11250}\n",
+       "    proj:geometry   object {'type': 'Polygon', 'coordinates': [[[-60.0, -9.99...\n",
+       "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
        "    proj:bbox       object {0.0, -60.0, -50.0000000001, -9.9999999999}\n",
-       "    proj:epsg       int64 4326\n",
-       "    title           (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
        "    description     (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
+       "    title           (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
        "    epsg            int64 4326\n",
        "Attributes:\n",
        "    spec:        RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999...\n",
@@ -1382,7 +1380,7 @@
        "    resolution:  0.00088888888888"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1428,7 +1426,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 12,
    "id": "1b9c7539-793e-41b6-86fe-2d9e9abb6d79",
    "metadata": {},
    "outputs": [],
@@ -1436,78 +1434,17 @@
     "arr_est = arr_sub.sel(band='estimates')"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "8eecc263-f593-4aad-b724-b93099b56df0",
-   "metadata": {},
-   "source": [
-    "Let's try to print the values of the `arr_est`.\n",
-    "\n",
-    "Disclaimer - It is not recommended to print values if the xarray is big in size as the notebook can crash. The `.values` requires the xarray to be loaded into the memory before printing the values and is a memory intensive problem."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "c60c3de4-8cd3-46cf-8b2a-99e103250575",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[222., 132., 193., ..., 360., 246., 271.],\n",
-       "        [153., 113., 152., ..., 331., 180., 261.],\n",
-       "        [189., 112., 173., ..., 381., 269., 260.],\n",
-       "        ...,\n",
-       "        [231., 193., 310., ..., 145., 392., 392.],\n",
-       "        [333., 255., 297., ..., 133., 392., 348.],\n",
-       "        [370., 189., 266., ..., 251., 293., 338.]],\n",
-       "\n",
-       "       [[219., 100., 138., ..., 363., 271., 198.],\n",
-       "        [188., 114., 101., ..., 345., 198., 254.],\n",
-       "        [142., 144., 179., ..., 325., 306., 372.],\n",
-       "        ...,\n",
-       "        [353., 302., 379., ..., 162., 385., 385.],\n",
-       "        [332., 348., 321., ..., 151., 385., 314.],\n",
-       "        [375., 243., 283., ..., 201., 336., 342.]],\n",
-       "\n",
-       "       [[271., 255., 219., ..., 363., 278., 286.],\n",
-       "        [253., 321., 116., ..., 226., 333., 233.],\n",
-       "        [222., 277., 176., ..., 169., 355., 190.],\n",
-       "        ...,\n",
-       "        [304., 336., 275., ..., 318., 308., 234.],\n",
-       "        [340., 234., 348., ..., 315., 356., 330.],\n",
-       "        [243., 323., 264., ..., 288., 307., 318.]],\n",
-       "\n",
-       "       [[157., 217., 171., ..., 206., 161., 253.],\n",
-       "        [190., 229., 226., ..., 214., 210., 314.],\n",
-       "        [269., 212., 152., ..., 268., 402., 324.],\n",
-       "        ...,\n",
-       "        [247., 292., 246., ..., 373., 321., 159.],\n",
-       "        [329., 200., 318., ..., 278., 284., 255.],\n",
-       "        [237., 361., 238., ..., 245., 292., 316.]]])"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "arr_est.values"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "0558d3c7-6b39-4e6b-ab8f-c399cee52a29",
    "metadata": {},
    "source": [
-    "Now, we are going to filter the values which are less than 10 using the [DataArray.where](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.where.html) function. The `.where(<cond>, <True directive>, <False directive>)` function checks a condition and performs specific directives if the condition results in `True` or `False`. In the resulting xarray, all the values less than 10 are going to be `nan` (the default value used if the filtering condition is `True`). We can also replace `nan` by any other value by mentioning it in the `<True directive>`."
+    "Now, we are going to filter the values which are less than 10 (and keep values greater than 10) using the [DataArray.where](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.where.html) function. The `.where(<cond>, <True directive>, <False directive>)` function checks a condition and performs specific directives if the condition results in `True` or `False`. In the resulting xarray, all the values less than 10 are going to be `nan` (the default value used if the filtering condition is `True`). We can also replace `nan` by any other value by mentioning it in the `<True directive>`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 13,
    "id": "91ab0e4d-47c5-4ffb-a602-719c2bb29f76",
    "metadata": {
     "tags": []
@@ -1549,7 +1486,7 @@
        "        [237., 361., 238., ..., 245., 292., 316.]]])"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1564,59 +1501,7 @@
    "id": "1f511c71-5098-4aa2-9d0c-da089c967b7e",
    "metadata": {},
    "source": [
-    "Now, let's try to perform filtering based on the opposite condition i.e. to mask all the values greater than 10."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "d7101951-869a-40e8-b5f0-79a9b625ba5b",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        ...,\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
-       "\n",
-       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        ...,\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
-       "\n",
-       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        ...,\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
-       "\n",
-       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        ...,\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan],\n",
-       "        [nan, nan, nan, ..., nan, nan, nan]]])"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "arr_masked_gt = arr_est.where(arr_est <= 10)\n",
-    "arr_masked_gt.values"
+    "Similarly, we could also show filtering values greater than 10 (and keeping values less than 10) based on above approach and updating the condition to `arr_masked_lt = arr_est.where(arr_est <= 10)`."
    ]
   },
   {
@@ -1631,7 +1516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 15,
    "id": "1d960b78-e15b-4e2a-970f-a07239a49272",
    "metadata": {
     "tags": []
@@ -1640,16 +1525,16 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fc20871f340>]"
+       "[<matplotlib.lines.Line2D at 0x7fea4071f5b0>]"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1668,12 +1553,12 @@
    "id": "6f74dbab-9fe2-4b32-a112-5f80d90af859",
    "metadata": {},
    "source": [
-    "Let's try to visualize the time series of the filtered array using the 1st condition mentioned above for masking all the values above 10. We will create a function based on above code with `floor` as a parameter to be used for filtering. "
+    "Let's try to visualize the mean time-series of the filtered array using the condition mentioned above for masking all the values less than 10 using a function. This function is based on above code with `floor` as a parameter to be used for filtering. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 20,
    "id": "28aab0c5-e304-4133-b3fe-355fd493cda8",
    "metadata": {},
    "outputs": [],
@@ -1688,13 +1573,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 21,
    "id": "9d534c06-eea3-4e80-aaf7-a79df8c5e555",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1704,7 +1589,7 @@
     }
    ],
    "source": [
-    "plot_mean_time_series(da=arr_masked_gt, floor=10)"
+    "plot_mean_time_series(da=arr_masked_lt, floor=10)"
    ]
   }
  ],

From 3edf7f6114d4159e8e914888a1af0e8d96f44e5a Mon Sep 17 00:00:00 2001
From: emileten <e.tenezakis@gmail.com>
Date: Mon, 2 Oct 2023 08:58:00 +0000
Subject: [PATCH 7/8] remove unused imports, remove the plot function and last
 plot

---
 docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb | 209 ++++++++-----------
 1 file changed, 91 insertions(+), 118 deletions(-)

diff --git a/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb b/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
index 4837fba3..aabb3782 100644
--- a/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
+++ b/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
@@ -48,30 +48,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "aea4eb22-0867-4876-bd2b-fe74f5dd7bab",
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "# !mamba install -c conda-forge stackstac\n",
-    "# !pip install pystac_client\n",
-    "# !pip install dask[diagnostics]"
+    "# !mamba install -y -c conda-forge stackstac\n",
+    "# !pip install pystac_client"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "id": "bcc3b0ba",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from stackstac import stack, mosaic\n",
-    "import pystac_client\n",
-    "import matplotlib.pyplot as plt \n",
-    "import numpy as np\n",
-    "import xarray"
+    "import pystac_client"
    ]
   },
   {
@@ -92,9 +90,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "e6917d57",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "URL = \"https://stac.maap-project.org\"\n",
@@ -119,9 +119,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "73868a97",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "# BBox for filtering the items in the collection\n",
@@ -138,9 +140,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "id": "7bb997f0",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "stac_collection = catalog.search(\n",
@@ -159,19 +163,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "id": "c73362b3-cfb5-48cd-a018-58ca5feb6ef4",
    "metadata": {
     "tags": []
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/pystac_client/item_search.py:850: FutureWarning: get_all_items() is deprecated, use item_collection() instead.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
        "'s3://nasa-maap-data-store/file-staging/nasa-map/ESACCI_Biomass_L4_AGB_V4_100m_2020/N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2020-fv4.0.tif'"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -190,9 +202,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "id": "46bf720c-18d7-4d80-960b-998abd4a5255",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -218,9 +232,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "id": "ed6f411a-4643-40a6-b263-f2169845af6a",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "data": {
@@ -588,7 +604,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;stackstac-869d919e6d4941245ca2070e9aa6fcec&#x27; (time: 4,\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;stackstac-5517ee4c53a7b2c13f76bf727c00d1fc&#x27; (time: 4,\n",
        "                                                                band: 2,\n",
        "                                                                y: 11250,\n",
        "                                                                x: 11251)&gt;\n",
@@ -601,17 +617,17 @@
        "  * y               (y) float64 0.0 -0.0008889 -0.001778 ... -9.998 -9.999\n",
        "    proj:epsg       int64 4326\n",
        "    ...              ...\n",
+       "    proj:transform  object {0.0, 1.0, -60.0, -0.00088888888888, 0.00088888888...\n",
+       "    proj:shape      object {11250}\n",
        "    proj:geometry   object {&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.99...\n",
-       "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
-       "    proj:bbox       object {0.0, -60.0, -50.0000000001, -9.9999999999}\n",
-       "    description     (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
        "    title           (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
+       "    description     (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
        "    epsg            int64 4326\n",
        "Attributes:\n",
        "    spec:        RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999...\n",
        "    crs:         epsg:4326\n",
        "    transform:   | 0.00, 0.00,-60.00|\\n| 0.00,-0.00, 0.00|\\n| 0.00, 0.00, 1.00|\n",
-       "    resolution:  0.00088888888888</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'stackstac-869d919e6d4941245ca2070e9aa6fcec'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>band</span>: 2</li><li><span class='xr-has-index'>y</span>: 11250</li><li><span class='xr-has-index'>x</span>: 11251</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e1489931-933f-4edc-9ce2-334225091394' class='xr-array-in' type='checkbox' checked><label for='section-e1489931-933f-4edc-9ce2-334225091394' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 1024, 1024), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    resolution:  0.00088888888888</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'stackstac-5517ee4c53a7b2c13f76bf727c00d1fc'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>band</span>: 2</li><li><span class='xr-has-index'>y</span>: 11250</li><li><span class='xr-has-index'>x</span>: 11251</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0be1a8ce-bf34-4af7-82f3-dbe2345d33e6' class='xr-array-in' type='checkbox' checked><label for='section-0be1a8ce-bf34-4af7-82f3-dbe2345d33e6' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 1024, 1024), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -750,18 +766,18 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-50c67922-5919-469d-9cf3-d88b6d51f0a1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-50c67922-5919-469d-9cf3-d88b6d51f0a1' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-01 ... 2020-01-01</div><input id='attrs-95df36a1-6b61-4e45-a37b-7208e3cde230' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95df36a1-6b61-4e45-a37b-7208e3cde230' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e983bf1e-7fe0-4f0a-8eb3-2f02cffa24ba' class='xr-var-data-in' type='checkbox'><label for='data-e983bf1e-7fe0-4f0a-8eb3-2f02cffa24ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-808a9e16-380d-4daa-9554-ad8db89f0a94' class='xr-section-summary-in' type='checkbox'  checked><label for='section-808a9e16-380d-4daa-9554-ad8db89f0a94' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-01 ... 2020-01-01</div><input id='attrs-4a1a5832-7af2-45b5-82f1-d38dac3cd5be' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a1a5832-7af2-45b5-82f1-d38dac3cd5be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0ad5951-6fa2-4a0b-94d4-64400cccfcb7' class='xr-var-data-in' type='checkbox'><label for='data-c0ad5951-6fa2-4a0b-94d4-64400cccfcb7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U52</div><div class='xr-var-preview xr-preview'>&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-M...</div><input id='attrs-7e4a9170-34c1-4710-959e-ccfa05b9bad6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e4a9170-34c1-4710-959e-ccfa05b9bad6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2106a91d-17bf-47c5-8a7b-6770746eb06f' class='xr-var-data-in' type='checkbox'><label for='data-2106a91d-17bf-47c5-8a7b-6770746eb06f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2017-fv4.0&#x27;,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U52</div><div class='xr-var-preview xr-preview'>&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-M...</div><input id='attrs-60267aa1-7abe-45d3-9f36-a44323a03f90' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-60267aa1-7abe-45d3-9f36-a44323a03f90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd9072c7-285a-4744-b487-008938a4fdd8' class='xr-var-data-in' type='checkbox'><label for='data-fd9072c7-285a-4744-b487-008938a4fdd8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2017-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2018-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2019-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2020-fv4.0&#x27;],\n",
-       "      dtype=&#x27;&lt;U52&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;estimates&#x27; &#x27;standard_deviation&#x27;</div><input id='attrs-374fc37c-a796-4692-a025-c12154553b1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-374fc37c-a796-4692-a025-c12154553b1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81c2fd81-3f9c-497d-880d-f52b836eaa58' class='xr-var-data-in' type='checkbox'><label for='data-81c2fd81-3f9c-497d-880d-f52b836eaa58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -60.0 -60.0 ... -50.0 -50.0</div><input id='attrs-bfc0d264-7640-4c40-a5a0-dea5552bd6c5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bfc0d264-7640-4c40-a5a0-dea5552bd6c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10b982a7-26d7-466d-abcc-72248031afc0' class='xr-var-data-in' type='checkbox'><label for='data-10b982a7-26d7-466d-abcc-72248031afc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-60.000889, -60.      , -59.999111, ..., -50.002667, -50.001778,\n",
-       "       -50.000889])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 -0.0008889 ... -9.998 -9.999</div><input id='attrs-18cb7706-f3f2-442c-9a28-a856f48fcaa2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-18cb7706-f3f2-442c-9a28-a856f48fcaa2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78e3a086-a857-4df0-bbff-c7ea3a1e9585' class='xr-var-data-in' type='checkbox'><label for='data-78e3a086-a857-4df0-bbff-c7ea3a1e9585' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.000000e+00, -8.888889e-04, -1.777778e-03, ..., -9.997333e+00,\n",
-       "       -9.998222e+00, -9.999111e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-31863655-6ef0-448e-ab13-167b69ffd38e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-31863655-6ef0-448e-ab13-167b69ffd38e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49fbebb6-3de9-46f4-9cec-deb6895a8515' class='xr-var-data-in' type='checkbox'><label for='data-49fbebb6-3de9-46f4-9cec-deb6895a8515' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:shape</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{11250}</div><input id='attrs-9561d524-54b0-4647-8040-4b51e6b65dd6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9561d524-54b0-4647-8040-4b51e6b65dd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79f40d8b-907c-45d4-8b92-eeea5abe8a40' class='xr-var-data-in' type='checkbox'><label for='data-79f40d8b-907c-45d4-8b92-eeea5abe8a40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({11250}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:geometry</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates...</div><input id='attrs-28c6113c-3b16-4ce1-9a8c-7a1e6b4602f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28c6113c-3b16-4ce1-9a8c-7a1e6b4602f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ce5bb2a-fab4-4f48-8b87-95b715861301' class='xr-var-data-in' type='checkbox'><label for='data-7ce5bb2a-fab4-4f48-8b87-95b715861301' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.9999999999], [-50.0000000001, -9.9999999999], [-50.0000000001, 0.0], [-60.0, 0.0], [-60.0, -9.9999999999]]]},\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:transform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, 1.0, -0.00088888888888, -6...</div><input id='attrs-f3acf9c0-556d-4dc4-a6cb-08aaa20f1667' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f3acf9c0-556d-4dc4-a6cb-08aaa20f1667' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4300e7c7-1e14-4bb7-824d-4df949d2063a' class='xr-var-data-in' type='checkbox'><label for='data-4300e7c7-1e14-4bb7-824d-4df949d2063a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888888}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:bbox</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, -60.0, -50.0000000001, -9....</div><input id='attrs-ccb61515-3c7c-4f9d-a913-9346956b51ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ccb61515-3c7c-4f9d-a913-9346956b51ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ac89429-4c5e-4e1c-b961-cbfb3227d839' class='xr-var-data-in' type='checkbox'><label for='data-1ac89429-4c5e-4e1c-b961-cbfb3227d839' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, -60.0, -50.0000000001, -9.9999999999}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>description</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-ba82828a-86d8-4713-ba68-58ad3f16b850' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba82828a-86d8-4713-ba68-58ad3f16b850' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8307ae69-0da2-41d0-add8-8d42116d1b7e' class='xr-var-data-in' type='checkbox'><label for='data-8307ae69-0da2-41d0-add8-8d42116d1b7e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
-       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-09139057-d58f-4e26-87e5-ff43462e476b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09139057-d58f-4e26-87e5-ff43462e476b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e64a175d-990d-4a32-9f7f-a3db93262517' class='xr-var-data-in' type='checkbox'><label for='data-e64a175d-990d-4a32-9f7f-a3db93262517' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
-       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-c01bab80-aff3-4aaa-bbf5-a63dcb7a8d6e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c01bab80-aff3-4aaa-bbf5-a63dcb7a8d6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57871752-51e9-4c31-a47c-73b181b39421' class='xr-var-data-in' type='checkbox'><label for='data-57871752-51e9-4c31-a47c-73b181b39421' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d14a9c63-4b2d-4b59-83f8-0515d239b7f4' class='xr-section-summary-in' type='checkbox'  ><label for='section-d14a9c63-4b2d-4b59-83f8-0515d239b7f4' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0897b77b-2044-4ffe-8989-c056e918f64f' class='xr-index-data-in' type='checkbox'/><label for='index-0897b77b-2044-4ffe-8989-c056e918f64f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c2715891-21d4-4525-9745-14577b6742cb' class='xr-index-data-in' type='checkbox'/><label for='index-c2715891-21d4-4525-9745-14577b6742cb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9524e479-dcf7-42ef-b16a-17977251938f' class='xr-index-data-in' type='checkbox'/><label for='index-9524e479-dcf7-42ef-b16a-17977251938f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -60.00088888828888, -59.999999999399996, -59.999111110511116,\n",
+       "      dtype=&#x27;&lt;U52&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;estimates&#x27; &#x27;standard_deviation&#x27;</div><input id='attrs-039426cc-3e2f-46db-8cc8-4534903a5cbc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-039426cc-3e2f-46db-8cc8-4534903a5cbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b1e508c-a86d-412e-850c-4f40accb6c9d' class='xr-var-data-in' type='checkbox'><label for='data-1b1e508c-a86d-412e-850c-4f40accb6c9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-60.0 -60.0 -60.0 ... -50.0 -50.0</div><input id='attrs-6c14caa8-2600-4d26-8d45-7c03c11c8a65' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c14caa8-2600-4d26-8d45-7c03c11c8a65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3968374f-a513-4bfd-bafb-b07b4e017436' class='xr-var-data-in' type='checkbox'><label for='data-3968374f-a513-4bfd-bafb-b07b4e017436' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-60.000889, -60.      , -59.999111, ..., -50.002667, -50.001778,\n",
+       "       -50.000889])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 -0.0008889 ... -9.998 -9.999</div><input id='attrs-47d86e93-7c2b-4bc3-9408-43e2ece9e403' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47d86e93-7c2b-4bc3-9408-43e2ece9e403' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f44d181-9913-4cb4-8849-7768f3a6ad37' class='xr-var-data-in' type='checkbox'><label for='data-0f44d181-9913-4cb4-8849-7768f3a6ad37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0.000000e+00, -8.888889e-04, -1.777778e-03, ..., -9.997333e+00,\n",
+       "       -9.998222e+00, -9.999111e+00])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-aa89b093-617b-4c0d-ac73-c32b3470dcd7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aa89b093-617b-4c0d-ac73-c32b3470dcd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1df9c48-c0ac-4f99-a389-a93b410cc848' class='xr-var-data-in' type='checkbox'><label for='data-a1df9c48-c0ac-4f99-a389-a93b410cc848' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:bbox</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, -60.0, -50.0000000001, -9....</div><input id='attrs-908fdeb9-c7ff-4212-88fe-c4766ee2608f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-908fdeb9-c7ff-4212-88fe-c4766ee2608f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4c8d873-c7b4-46b3-b46d-62358ecbf8a1' class='xr-var-data-in' type='checkbox'><label for='data-b4c8d873-c7b4-46b3-b46d-62358ecbf8a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, -60.0, -50.0000000001, -9.9999999999}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:transform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, 1.0, -60.0, -0.00088888888...</div><input id='attrs-285ca437-6c0f-4176-95a0-87863d339d5f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-285ca437-6c0f-4176-95a0-87863d339d5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10dc45f7-3cc0-48c1-89ee-cd56a7a83e1f' class='xr-var-data-in' type='checkbox'><label for='data-10dc45f7-3cc0-48c1-89ee-cd56a7a83e1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, 1.0, -60.0, -0.00088888888888, 0.00088888888888}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:shape</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{11250}</div><input id='attrs-7c2890de-2cb0-49a3-a9ae-f9b092bff35a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7c2890de-2cb0-49a3-a9ae-f9b092bff35a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd473274-2dee-4a3a-8021-00450c5b83f2' class='xr-var-data-in' type='checkbox'><label for='data-cd473274-2dee-4a3a-8021-00450c5b83f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({11250}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:geometry</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates...</div><input id='attrs-8758d963-4b3e-4e60-8bc8-f7087a074dbd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8758d963-4b3e-4e60-8bc8-f7087a074dbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93fd5be0-e954-491e-a50f-c30785a0c02b' class='xr-var-data-in' type='checkbox'><label for='data-93fd5be0-e954-491e-a50f-c30785a0c02b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.9999999999], [-50.0000000001, -9.9999999999], [-50.0000000001, 0.0], [-60.0, 0.0], [-60.0, -9.9999999999]]]},\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-d268454c-f1b6-4195-9edc-1390f825bf21' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d268454c-f1b6-4195-9edc-1390f825bf21' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-637392ae-0bdc-4bcc-88ee-1e3c1763c8a7' class='xr-var-data-in' type='checkbox'><label for='data-637392ae-0bdc-4bcc-88ee-1e3c1763c8a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
+       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>description</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-65398c5a-d4dc-473a-a4c8-8f4c50fac142' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65398c5a-d4dc-473a-a4c8-8f4c50fac142' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b358d650-a51e-478f-857c-b26b4e35891c' class='xr-var-data-in' type='checkbox'><label for='data-b358d650-a51e-478f-857c-b26b4e35891c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
+       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-731873cd-9cf1-4941-85ed-fefcc88122c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-731873cd-9cf1-4941-85ed-fefcc88122c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bb1027f-cbe8-4e66-bf44-c5970d609428' class='xr-var-data-in' type='checkbox'><label for='data-4bb1027f-cbe8-4e66-bf44-c5970d609428' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-705c4395-0a9a-4f3a-81cb-8dc5969d5b3a' class='xr-section-summary-in' type='checkbox'  ><label for='section-705c4395-0a9a-4f3a-81cb-8dc5969d5b3a' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e2cb3e8a-b05d-4cfa-920d-8ff6b48663cf' class='xr-index-data-in' type='checkbox'/><label for='index-e2cb3e8a-b05d-4cfa-920d-8ff6b48663cf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-703091ce-0f4f-4456-a443-8c47a7f04969' class='xr-index-data-in' type='checkbox'/><label for='index-703091ce-0f4f-4456-a443-8c47a7f04969' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6923793a-edc9-49d6-8d28-486017684d71' class='xr-index-data-in' type='checkbox'/><label for='index-6923793a-edc9-49d6-8d28-486017684d71' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ -60.00088888828888, -59.999999999399996, -59.999111110511116,\n",
        "       -59.998222221622235, -59.997333332733355, -59.996444443844474,\n",
        "       -59.995555554955594,  -59.99466666606671,  -59.99377777717784,\n",
        "        -59.99288888828896,\n",
@@ -770,7 +786,7 @@
        "       -50.006222221722155, -50.005333332833274,   -50.0044444439444,\n",
        "        -50.00355555505552,  -50.00266666616664,  -50.00177777727776,\n",
        "        -50.00088888838888],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=11251))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b24489c3-6cf1-4bb1-a09a-2e60715d3168' class='xr-index-data-in' type='checkbox'/><label for='index-b24489c3-6cf1-4bb1-a09a-2e60715d3168' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0,  -0.00088888888888,  -0.00177777777776,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=11251))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-11d9b43b-8d74-4efe-967d-e3321f5798e8' class='xr-index-data-in' type='checkbox'/><label for='index-11d9b43b-8d74-4efe-967d-e3321f5798e8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               0.0,  -0.00088888888888,  -0.00177777777776,\n",
        "        -0.00266666666664,  -0.00355555555552,   -0.0044444444444,\n",
        "        -0.00533333333328,  -0.00622222222216,  -0.00711111111104,\n",
        "        -0.00799999999992,\n",
@@ -779,12 +795,12 @@
        "        -9.99377777767784,  -9.99466666656672,   -9.9955555554556,\n",
        "        -9.99644444434448,  -9.99733333323336,  -9.99822222212224,\n",
        "        -9.99911111101112],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=11250))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ccd5a2ca-0bea-486f-b763-b522bdbca3ee' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ccd5a2ca-0bea-486f-b763-b522bdbca3ee' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999999, -49.9999999995, 0.0), resolutions_xy=(0.00088888888888, 0.00088888888888))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.00, 0.00,-60.00|\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=11250))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-85e3e562-0f5e-48b1-8eb0-53f3dd117b8b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-85e3e562-0f5e-48b1-8eb0-53f3dd117b8b' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999999, -49.9999999995, 0.0), resolutions_xy=(0.00088888888888, 0.00088888888888))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.00, 0.00,-60.00|\n",
        "| 0.00,-0.00, 0.00|\n",
        "| 0.00, 0.00, 1.00|</dd><dt><span>resolution :</span></dt><dd>0.00088888888888</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'stackstac-869d919e6d4941245ca2070e9aa6fcec' (time: 4,\n",
+       "<xarray.DataArray 'stackstac-5517ee4c53a7b2c13f76bf727c00d1fc' (time: 4,\n",
        "                                                                band: 2,\n",
        "                                                                y: 11250,\n",
        "                                                                x: 11251)>\n",
@@ -797,11 +813,11 @@
        "  * y               (y) float64 0.0 -0.0008889 -0.001778 ... -9.998 -9.999\n",
        "    proj:epsg       int64 4326\n",
        "    ...              ...\n",
+       "    proj:transform  object {0.0, 1.0, -60.0, -0.00088888888888, 0.00088888888...\n",
+       "    proj:shape      object {11250}\n",
        "    proj:geometry   object {'type': 'Polygon', 'coordinates': [[[-60.0, -9.99...\n",
-       "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
-       "    proj:bbox       object {0.0, -60.0, -50.0000000001, -9.9999999999}\n",
-       "    description     (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
        "    title           (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
+       "    description     (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
        "    epsg            int64 4326\n",
        "Attributes:\n",
        "    spec:        RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999...\n",
@@ -810,7 +826,7 @@
        "    resolution:  0.00088888888888"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -829,7 +845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "id": "e9d173bd-fbfc-4f5c-b0bb-d948b49259ce",
    "metadata": {
     "tags": []
@@ -1201,7 +1217,7 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;stackstac-869d919e6d4941245ca2070e9aa6fcec&#x27; (time: 4,\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;stackstac-5517ee4c53a7b2c13f76bf727c00d1fc&#x27; (time: 4,\n",
        "                                                                band: 2,\n",
        "                                                                y: 225, x: 225)&gt;\n",
        "dask.array&lt;getitem, shape=(4, 2, 225, 225), dtype=float64, chunksize=(1, 1, 225, 225), chunktype=numpy.ndarray&gt;\n",
@@ -1213,17 +1229,17 @@
        "  * y               (y) float64 -5.801 -5.802 -5.803 ... -5.998 -5.999 -6.0\n",
        "    proj:epsg       int64 4326\n",
        "    ...              ...\n",
+       "    proj:transform  object {0.0, 1.0, -60.0, -0.00088888888888, 0.00088888888...\n",
+       "    proj:shape      object {11250}\n",
        "    proj:geometry   object {&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.99...\n",
-       "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
-       "    proj:bbox       object {0.0, -60.0, -50.0000000001, -9.9999999999}\n",
-       "    description     (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
        "    title           (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
+       "    description     (band) &lt;U49 &#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27; &#x27;C...\n",
        "    epsg            int64 4326\n",
        "Attributes:\n",
        "    spec:        RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999...\n",
        "    crs:         epsg:4326\n",
        "    transform:   | 0.00, 0.00,-60.00|\\n| 0.00,-0.00, 0.00|\\n| 0.00, 0.00, 1.00|\n",
-       "    resolution:  0.00088888888888</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'stackstac-869d919e6d4941245ca2070e9aa6fcec'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>band</span>: 2</li><li><span class='xr-has-index'>y</span>: 225</li><li><span class='xr-has-index'>x</span>: 225</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5af97235-a439-427e-8f49-30d302f7001d' class='xr-array-in' type='checkbox' checked><label for='section-5af97235-a439-427e-8f49-30d302f7001d' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 225, 225), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
+       "    resolution:  0.00088888888888</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'stackstac-5517ee4c53a7b2c13f76bf727c00d1fc'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 4</li><li><span class='xr-has-index'>band</span>: 2</li><li><span class='xr-has-index'>y</span>: 225</li><li><span class='xr-has-index'>x</span>: 225</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-257caae7-8a75-4bb3-86a0-bba8e539f304' class='xr-array-in' type='checkbox' checked><label for='section-257caae7-8a75-4bb3-86a0-bba8e539f304' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 1, 225, 225), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
        "    <tr>\n",
        "        <td>\n",
        "            <table style=\"border-collapse: collapse;\">\n",
@@ -1322,17 +1338,17 @@
        "</svg>\n",
        "        </td>\n",
        "    </tr>\n",
-       "</table></div></div></li><li class='xr-section-item'><input id='section-3aacbb11-0fe6-456a-8b9c-e3136cc2f1d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3aacbb11-0fe6-456a-8b9c-e3136cc2f1d4' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-01 ... 2020-01-01</div><input id='attrs-8fab4255-e78d-4ba7-8b92-4181efa9996b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8fab4255-e78d-4ba7-8b92-4181efa9996b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03a3fcc2-f392-41f4-a4b4-87adb5866b85' class='xr-var-data-in' type='checkbox'><label for='data-03a3fcc2-f392-41f4-a4b4-87adb5866b85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
+       "</table></div></div></li><li class='xr-section-item'><input id='section-e1d3c362-4be1-4c24-b269-a69352a3794f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e1d3c362-4be1-4c24-b269-a69352a3794f' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2017-01-01 ... 2020-01-01</div><input id='attrs-2814b9cd-f4d1-4737-8fe7-f4df42fa3a6b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2814b9cd-f4d1-4737-8fe7-f4df42fa3a6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3c16447-fb70-4b06-a9cb-c41a1aab2704' class='xr-var-data-in' type='checkbox'><label for='data-a3c16447-fb70-4b06-a9cb-c41a1aab2704' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2018-01-01T00:00:00.000000000&#x27;,\n",
        "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U52</div><div class='xr-var-preview xr-preview'>&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-M...</div><input id='attrs-f6cc8327-02a6-42bc-ab9f-a4b6579187ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6cc8327-02a6-42bc-ab9f-a4b6579187ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38c18e72-5ead-4e36-9988-498cd34adbe2' class='xr-var-data-in' type='checkbox'><label for='data-38c18e72-5ead-4e36-9988-498cd34adbe2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2017-fv4.0&#x27;,\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>&lt;U52</div><div class='xr-var-preview xr-preview'>&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-M...</div><input id='attrs-c6de504e-7a6e-4154-a25e-273222751c2f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6de504e-7a6e-4154-a25e-273222751c2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e904b0a8-8a46-46eb-aed2-ca23faee8c07' class='xr-var-data-in' type='checkbox'><label for='data-e904b0a8-8a46-46eb-aed2-ca23faee8c07' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2017-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2018-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2019-fv4.0&#x27;,\n",
        "       &#x27;N00W060_ESACCI-BIOMASS-L4-AGB-MERGED-100m-2020-fv4.0&#x27;],\n",
-       "      dtype=&#x27;&lt;U52&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;estimates&#x27; &#x27;standard_deviation&#x27;</div><input id='attrs-69663a2e-6ede-4f45-a9a6-5fc8a55120c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69663a2e-6ede-4f45-a9a6-5fc8a55120c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-238f133b-dd4c-4da0-bd50-cca8921e7791' class='xr-var-data-in' type='checkbox'><label for='data-238f133b-dd4c-4da0-bd50-cca8921e7791' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-55.0 -55.0 -55.0 ... -54.8 -54.8</div><input id='attrs-6c69fb00-b5d2-4a83-9ec1-2cd2cdc4b0f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6c69fb00-b5d2-4a83-9ec1-2cd2cdc4b0f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf2edc44-261d-4fc6-b198-81329dc062dc' class='xr-var-data-in' type='checkbox'><label for='data-cf2edc44-261d-4fc6-b198-81329dc062dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-55.      , -54.999111, -54.998222, ..., -54.802667, -54.801778,\n",
-       "       -54.800889])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.801 -5.802 ... -5.999 -6.0</div><input id='attrs-7f0e8ef2-3d71-4b59-bba1-4bce595ce575' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f0e8ef2-3d71-4b59-bba1-4bce595ce575' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1dfef45-2929-4701-81b1-cec8d92d7668' class='xr-var-data-in' type='checkbox'><label for='data-a1dfef45-2929-4701-81b1-cec8d92d7668' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-5.800889, -5.801778, -5.802667, ..., -5.998222, -5.999111, -6.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-3e2a11b4-73dd-45da-a075-8fe4615a7434' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e2a11b4-73dd-45da-a075-8fe4615a7434' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13abd89f-e71b-47a7-80bf-27f61f5754b8' class='xr-var-data-in' type='checkbox'><label for='data-13abd89f-e71b-47a7-80bf-27f61f5754b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:shape</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{11250}</div><input id='attrs-233978cc-ee9c-4ff4-85e4-bf88bbe032f5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-233978cc-ee9c-4ff4-85e4-bf88bbe032f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e96a290d-7b41-4ec3-9052-35fd9ade859c' class='xr-var-data-in' type='checkbox'><label for='data-e96a290d-7b41-4ec3-9052-35fd9ade859c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({11250}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:geometry</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates...</div><input id='attrs-ed318f4f-098e-42a7-815f-459357c5172e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed318f4f-098e-42a7-815f-459357c5172e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8065704-0489-4b05-ae75-fce03a1636cb' class='xr-var-data-in' type='checkbox'><label for='data-c8065704-0489-4b05-ae75-fce03a1636cb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.9999999999], [-50.0000000001, -9.9999999999], [-50.0000000001, 0.0], [-60.0, 0.0], [-60.0, -9.9999999999]]]},\n",
-       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:transform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, 1.0, -0.00088888888888, -6...</div><input id='attrs-97d974ea-5f58-45e9-ba01-b69334311b11' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-97d974ea-5f58-45e9-ba01-b69334311b11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64a23e01-76bb-4553-9cb3-66af3a6d521f' class='xr-var-data-in' type='checkbox'><label for='data-64a23e01-76bb-4553-9cb3-66af3a6d521f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888888}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:bbox</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, -60.0, -50.0000000001, -9....</div><input id='attrs-1fdb9dac-5705-45d3-9481-6835c1bc9fdc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1fdb9dac-5705-45d3-9481-6835c1bc9fdc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9f13a89-0aca-4dd7-b153-71b54d756507' class='xr-var-data-in' type='checkbox'><label for='data-f9f13a89-0aca-4dd7-b153-71b54d756507' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, -60.0, -50.0000000001, -9.9999999999}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>description</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-a705cf10-f90b-4111-a58c-bce255b81b40' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a705cf10-f90b-4111-a58c-bce255b81b40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dde75c14-1640-4622-9769-bbd85739f052' class='xr-var-data-in' type='checkbox'><label for='data-dde75c14-1640-4622-9769-bbd85739f052' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
-       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-b67c8a10-ef59-4e8b-80fc-194df1636e34' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b67c8a10-ef59-4e8b-80fc-194df1636e34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55024521-faec-42aa-94ef-7d0c35d226ee' class='xr-var-data-in' type='checkbox'><label for='data-55024521-faec-42aa-94ef-7d0c35d226ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
-       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-122662a8-c35c-4e76-af3f-9642c4c02bad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-122662a8-c35c-4e76-af3f-9642c4c02bad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a710e70d-f5d7-4766-bd6e-574e28661b1a' class='xr-var-data-in' type='checkbox'><label for='data-a710e70d-f5d7-4766-bd6e-574e28661b1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f1dc5a9e-2917-4826-8af9-02d99f46f621' class='xr-section-summary-in' type='checkbox'  ><label for='section-f1dc5a9e-2917-4826-8af9-02d99f46f621' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b9f27960-83ac-42c1-a902-dc29d686ff9b' class='xr-index-data-in' type='checkbox'/><label for='index-b9f27960-83ac-42c1-a902-dc29d686ff9b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c298849d-f1bb-4b3c-86c6-c1c64f18cd40' class='xr-index-data-in' type='checkbox'/><label for='index-c298849d-f1bb-4b3c-86c6-c1c64f18cd40' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c07a2743-b9cf-44a9-8a70-546ea9514d77' class='xr-index-data-in' type='checkbox'/><label for='index-c07a2743-b9cf-44a9-8a70-546ea9514d77' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    -54.99999999945,  -54.99911111056112, -54.998222221672236,\n",
+       "      dtype=&#x27;&lt;U52&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U18</div><div class='xr-var-preview xr-preview'>&#x27;estimates&#x27; &#x27;standard_deviation&#x27;</div><input id='attrs-51c6be65-d490-44dd-9769-8deaed358457' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-51c6be65-d490-44dd-9769-8deaed358457' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f755d374-10b8-4cd8-8d6d-6ef000538c06' class='xr-var-data-in' type='checkbox'><label for='data-f755d374-10b8-4cd8-8d6d-6ef000538c06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;&lt;U18&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-55.0 -55.0 -55.0 ... -54.8 -54.8</div><input id='attrs-cba41eed-a3e0-478d-9c3b-ce919f115b9f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cba41eed-a3e0-478d-9c3b-ce919f115b9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d5b827f-4a9d-48e3-bcfe-e02130941d61' class='xr-var-data-in' type='checkbox'><label for='data-4d5b827f-4a9d-48e3-bcfe-e02130941d61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-55.      , -54.999111, -54.998222, ..., -54.802667, -54.801778,\n",
+       "       -54.800889])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-5.801 -5.802 ... -5.999 -6.0</div><input id='attrs-d8fa78bd-e71d-4f0f-ae0e-f5cd71adc41e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8fa78bd-e71d-4f0f-ae0e-f5cd71adc41e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66f201ea-9194-4c5a-90de-bb0f4cc5242b' class='xr-var-data-in' type='checkbox'><label for='data-66f201ea-9194-4c5a-90de-bb0f4cc5242b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-5.800889, -5.801778, -5.802667, ..., -5.998222, -5.999111, -6.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-3b2df9fd-2d2e-4c30-8eaa-1ef113aea621' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3b2df9fd-2d2e-4c30-8eaa-1ef113aea621' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fa0de61-57aa-4e0b-93ce-2e726d864967' class='xr-var-data-in' type='checkbox'><label for='data-2fa0de61-57aa-4e0b-93ce-2e726d864967' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:bbox</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, -60.0, -50.0000000001, -9....</div><input id='attrs-954bb13d-aeb1-46ad-9e67-dd7ff9e78ea9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-954bb13d-aeb1-46ad-9e67-dd7ff9e78ea9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6080c80c-34ea-436d-86ff-757c64f47f0e' class='xr-var-data-in' type='checkbox'><label for='data-6080c80c-34ea-436d-86ff-757c64f47f0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, -60.0, -50.0000000001, -9.9999999999}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:transform</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{0.0, 1.0, -60.0, -0.00088888888...</div><input id='attrs-0418d5ac-1fca-480a-921e-9fef56899812' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0418d5ac-1fca-480a-921e-9fef56899812' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55309286-99fe-493f-80cc-9f085ad98103' class='xr-var-data-in' type='checkbox'><label for='data-55309286-99fe-493f-80cc-9f085ad98103' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({0.0, 1.0, -60.0, -0.00088888888888, 0.00088888888888}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:shape</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{11250}</div><input id='attrs-19d03e73-6e6b-4597-8ce9-5ed2589d2c2b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-19d03e73-6e6b-4597-8ce9-5ed2589d2c2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1049222-853c-4319-8d16-3bd9b230947e' class='xr-var-data-in' type='checkbox'><label for='data-f1049222-853c-4319-8d16-3bd9b230947e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({11250}, dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>proj:geometry</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates...</div><input id='attrs-d1a359f6-8e57-45d0-8a4a-b03cad441c71' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1a359f6-8e57-45d0-8a4a-b03cad441c71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d64d6667-1053-4730-81b0-9e331b6dd12a' class='xr-var-data-in' type='checkbox'><label for='data-d64d6667-1053-4730-81b0-9e331b6dd12a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array({&#x27;type&#x27;: &#x27;Polygon&#x27;, &#x27;coordinates&#x27;: [[[-60.0, -9.9999999999], [-50.0000000001, -9.9999999999], [-50.0000000001, 0.0], [-60.0, 0.0], [-60.0, -9.9999999999]]]},\n",
+       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>title</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-95d9b478-fd07-45ad-8d6a-72e6a6d8c80e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-95d9b478-fd07-45ad-8d6a-72e6a6d8c80e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7572063a-8730-4ec0-9e20-4b071dd8c261' class='xr-var-data-in' type='checkbox'><label for='data-7572063a-8730-4ec0-9e20-4b071dd8c261' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
+       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>description</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U49</div><div class='xr-var-preview xr-preview'>&#x27;Cloud Optimized GeoTIFF of AGB ...</div><input id='attrs-59838362-3b4e-4d37-ae58-5300c0576567' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-59838362-3b4e-4d37-ae58-5300c0576567' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2b8f6d1-deb9-4542-b13e-6ccaec9bba02' class='xr-var-data-in' type='checkbox'><label for='data-a2b8f6d1-deb9-4542-b13e-6ccaec9bba02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Cloud Optimized GeoTIFF of AGB estimates&#x27;,\n",
+       "       &#x27;Cloud Optimized GeoTIFF of AGB standard deviation&#x27;], dtype=&#x27;&lt;U49&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>epsg</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>4326</div><input id='attrs-3e4f038d-351f-417d-b3a1-09eba454c745' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e4f038d-351f-417d-b3a1-09eba454c745' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21aeb203-3dee-48bb-8abe-d4702ba31db7' class='xr-var-data-in' type='checkbox'><label for='data-21aeb203-3dee-48bb-8abe-d4702ba31db7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(4326)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b0abd974-8cc5-498b-9e30-792bc586048b' class='xr-section-summary-in' type='checkbox'  ><label for='section-b0abd974-8cc5-498b-9e30-792bc586048b' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6bc6d637-ec7e-4b72-8aa2-f4fad32c14ff' class='xr-index-data-in' type='checkbox'/><label for='index-6bc6d637-ec7e-4b72-8aa2-f4fad32c14ff' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2017-01-01&#x27;, &#x27;2018-01-01&#x27;, &#x27;2019-01-01&#x27;, &#x27;2020-01-01&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6ea93f7d-3973-4661-8486-67095c769b7e' class='xr-index-data-in' type='checkbox'/><label for='index-6ea93f7d-3973-4661-8486-67095c769b7e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;estimates&#x27;, &#x27;standard_deviation&#x27;], dtype=&#x27;object&#x27;, name=&#x27;band&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4aa6f3e0-840c-4551-8988-61273e3dac90' class='xr-index-data-in' type='checkbox'/><label for='index-4aa6f3e0-840c-4551-8988-61273e3dac90' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([    -54.99999999945,  -54.99911111056112, -54.998222221672236,\n",
        "       -54.997333332783356, -54.996444443894475, -54.995555555005595,\n",
        "       -54.994666666116714, -54.993777777227834,  -54.99288888833896,\n",
        "        -54.99199999945008,\n",
@@ -1341,7 +1357,7 @@
        "       -54.806222221674155,  -54.80533333278528,   -54.8044444438964,\n",
        "        -54.80355555500752,  -54.80266666611864,  -54.80177777722976,\n",
        "        -54.80088888834088],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=225))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dd863825-88da-418a-a385-541621272213' class='xr-index-data-in' type='checkbox'/><label for='index-dd863825-88da-418a-a385-541621272213' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  -5.80088888883088,   -5.80177777771976,   -5.80266666660864,\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=225))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a37e2f3e-24e9-4145-9bac-db0d64da93e0' class='xr-index-data-in' type='checkbox'/><label for='index-a37e2f3e-24e9-4145-9bac-db0d64da93e0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  -5.80088888883088,   -5.80177777771976,   -5.80266666660864,\n",
        "         -5.80355555549752,    -5.8044444443864,   -5.80533333327528,\n",
        "         -5.80622222216416,   -5.80711111105304,   -5.80799999994192,\n",
        "          -5.8088888888308,\n",
@@ -1350,12 +1366,12 @@
        "         -5.99466666660672,    -5.9955555554956,   -5.99644444438448,\n",
        "         -5.99733333327336,   -5.99822222216224,   -5.99911111105112,\n",
        "            -5.99999999994],\n",
-       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=225))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ece6ca35-f901-4d46-ac7e-856ec809f0d1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ece6ca35-f901-4d46-ac7e-856ec809f0d1' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999999, -49.9999999995, 0.0), resolutions_xy=(0.00088888888888, 0.00088888888888))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.00, 0.00,-60.00|\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=225))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-22379f44-f198-4844-a129-893561b0ae1e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-22379f44-f198-4844-a129-893561b0ae1e' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>spec :</span></dt><dd>RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999999, -49.9999999995, 0.0), resolutions_xy=(0.00088888888888, 0.00088888888888))</dd><dt><span>crs :</span></dt><dd>epsg:4326</dd><dt><span>transform :</span></dt><dd>| 0.00, 0.00,-60.00|\n",
        "| 0.00,-0.00, 0.00|\n",
        "| 0.00, 0.00, 1.00|</dd><dt><span>resolution :</span></dt><dd>0.00088888888888</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.DataArray 'stackstac-869d919e6d4941245ca2070e9aa6fcec' (time: 4,\n",
+       "<xarray.DataArray 'stackstac-5517ee4c53a7b2c13f76bf727c00d1fc' (time: 4,\n",
        "                                                                band: 2,\n",
        "                                                                y: 225, x: 225)>\n",
        "dask.array<getitem, shape=(4, 2, 225, 225), dtype=float64, chunksize=(1, 1, 225, 225), chunktype=numpy.ndarray>\n",
@@ -1367,11 +1383,11 @@
        "  * y               (y) float64 -5.801 -5.802 -5.803 ... -5.998 -5.999 -6.0\n",
        "    proj:epsg       int64 4326\n",
        "    ...              ...\n",
+       "    proj:transform  object {0.0, 1.0, -60.0, -0.00088888888888, 0.00088888888...\n",
+       "    proj:shape      object {11250}\n",
        "    proj:geometry   object {'type': 'Polygon', 'coordinates': [[[-60.0, -9.99...\n",
-       "    proj:transform  object {0.0, 1.0, -0.00088888888888, -60.0, 0.00088888888...\n",
-       "    proj:bbox       object {0.0, -60.0, -50.0000000001, -9.9999999999}\n",
-       "    description     (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
        "    title           (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
+       "    description     (band) <U49 'Cloud Optimized GeoTIFF of AGB estimates' 'C...\n",
        "    epsg            int64 4326\n",
        "Attributes:\n",
        "    spec:        RasterSpec(epsg=4326, bounds=(-60.00088888828888, -9.9999999...\n",
@@ -1380,7 +1396,7 @@
        "    resolution:  0.00088888888888"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1426,9 +1442,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "id": "1b9c7539-793e-41b6-86fe-2d9e9abb6d79",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "arr_est = arr_sub.sel(band='estimates')"
@@ -1444,7 +1462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "91ab0e4d-47c5-4ffb-a602-719c2bb29f76",
    "metadata": {
     "tags": []
@@ -1486,7 +1504,7 @@
        "        [237., 361., 238., ..., 245., 292., 316.]]])"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1511,12 +1529,12 @@
    "source": [
     "#### Plotting Mean Time-Series for the AGB Estimates\n",
     "\n",
-    "Below we will look into plotting the mean time-series for the `estimate` band. We will be using the `compute()` module of the `dask` which performs an in-memory computation in a parallel approach."
+    "Below we plot the mean time-series of the masked array."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "id": "1d960b78-e15b-4e2a-970f-a07239a49272",
    "metadata": {
     "tags": []
@@ -1525,61 +1543,16 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fea4071f5b0>]"
+       "[<matplotlib.lines.Line2D at 0x7fd668b54e80>]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mean_time_series = arr_est.mean(['x','y']).compute()\n",
-    "mean_time_series.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6f74dbab-9fe2-4b32-a112-5f80d90af859",
-   "metadata": {},
-   "source": [
-    "Let's try to visualize the mean time-series of the filtered array using the condition mentioned above for masking all the values less than 10 using a function. This function is based on above code with `floor` as a parameter to be used for filtering. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "28aab0c5-e304-4133-b3fe-355fd493cda8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_mean_time_series(da=arr_est, band='estimates', floor=None):\n",
-    "    \"\"\" selects a band from `da`, optionally keeps only values above `floor`, calculates the spatial average and plots the resulting time series\"\"\"\n",
-    "    # da = da.sel(band=band)\n",
-    "    da_filtered = da if floor is None else da.where(da.values >= floor)\n",
-    "    av = da.mean(['x','y']).compute()\n",
-    "    av.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "9d534c06-eea3-4e80-aaf7-a79df8c5e555",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1589,7 +1562,7 @@
     }
    ],
    "source": [
-    "plot_mean_time_series(da=arr_masked_lt, floor=10)"
+    "arr_masked_lt.mean(['x','y']).plot()"
    ]
   }
  ],
@@ -1609,7 +1582,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.8"
   }
  },
  "nbformat": 4,

From 1d9a9424854a4b6e1a90d6d6713e35fcdcd617a6 Mon Sep 17 00:00:00 2001
From: emileten <e.tenezakis@gmail.com>
Date: Mon, 2 Oct 2023 09:08:28 +0000
Subject: [PATCH 8/8] change date

---
 docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb b/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
index aabb3782..27727306 100644
--- a/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
+++ b/docs/source/science/ESA_CCI/ESA_CCI_V4.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "Authors: Emile Tenezakis (DevSeed), Rajat Shinde (UAH)\n",
     "\n",
-    "Date: September 12, 2023\n",
+    "Date: October 2, 2023\n",
     "\n",
     "Description: In this tutorial, we explore accessing and visualizing ESA CCI Version 4 data from the MAAP STAC catalog. We make use of the [stackstac package](https://github.com/gjoseph92/stackstac) that allows us to turn a stack collection imported from the catalog with the [`pystac_client`](https://pystac-client.readthedocs.io/en/stable/) to an xarray dataset, and we plot the time series of the mean aboveground biomass for a selected tile of the dataset across the available temporal range.\n",
     "\n",