{
"cells": [
{
"cell_type": "markdown",
"id": "23374c2d-0b01-496c-8171-3abf13dc77d4",
"metadata": {},
"source": [
"## Reading Data from the STAC API\n",
"\n",
"The Planetary Computer catalogs the datasets we host using the [STAC](http://stacspec.org/) (SpatioTemporal Asset Catalog) specification. We provide a [STAC API](https://github.com/radiantearth/stac-api-spec) endpoint for searching our datasets by space, time, and more. This quickstart will show you how to search for data using our STAC API and open-source Python libraries. To use our STAC API from R, see [Reading data from the STAC API with R](https://planetarycomputer.microsoft.com/docs/quickstarts/reading-stac-r/).\n",
"\n",
"To get started you'll need the [pystac-client](https://github.com/stac-utils/pystac-client) library installed. You can install it via pip:\n",
"\n",
"```\n",
"> python -m pip install pystac-client\n",
"```\n",
"\n",
"To access the data, we'll create a `pystac_client.Client`. We'll explain the `modifier` part later on, but it's what lets us download the data assets Azure Blob Storage."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "fda5e533-84b8-4051-9198-2df74338ae71",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import pystac_client\n",
"import planetary_computer\n",
"\n",
"catalog = pystac_client.Client.open(\n",
" \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n",
" modifier=planetary_computer.sign_inplace,\n",
")"
]
},
{
"cell_type": "markdown",
"id": "b1f7ef57-45db-49ef-9065-4515157d23fb",
"metadata": {},
"source": [
"### Searching\n",
"\n",
"We can use the STAC API to search for assets meeting some criteria. This might include the date and time the asset covers, is spatial extent, or any other property captured in the STAC item's metadata.\n",
"\n",
"In this example we'll search for imagery from [Landsat Collection 2 Level-2](https://planetarycomputer.microsoft.com/dataset/landsat-c2-l2) area around Microsoft's main campus in December of 2020."
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "2ec73f94-3989-49cf-920a-fde66c7946b8",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/srv/conda/envs/notebook/lib/python3.11/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": [
"8"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"time_range = \"2020-12-01/2020-12-31\"\n",
"bbox = [-122.2751, 47.5469, -121.9613, 47.7458]\n",
"\n",
"search = catalog.search(collections=[\"landsat-c2-l2\"], bbox=bbox, datetime=time_range)\n",
"items = search.get_all_items()\n",
"len(items)"
]
},
{
"cell_type": "markdown",
"id": "84143827-ff63-4946-a995-ebc101f18d52",
"metadata": {},
"source": [
"In that example our spatial query used a bounding box with a `bbox`. Alternatively, you can pass a GeoJSON object as `intersects`\n",
"\n",
"```python\n",
"area_of_interest = {\n",
" \"type\": \"Polygon\",\n",
" \"coordinates\": [\n",
" [\n",
" [-122.2751, 47.5469],\n",
" [-121.9613, 47.9613],\n",
" [-121.9613, 47.9613],\n",
" [-122.2751, 47.9613],\n",
" [-122.2751, 47.5469],\n",
" ]\n",
" ],\n",
"}\n",
"\n",
"time_range = \"2020-12-01/2020-12-31\"\n",
"\n",
"search = catalog.search(\n",
" collections=[\"landsat-c2-l2\"], intersects=area_of_interest, datetime=time_range\n",
")\n",
"```"
]
},
{
"cell_type": "markdown",
"id": "aedaf9f7-1ba2-472d-85e1-5838e7df9ffb",
"metadata": {},
"source": [
"`items` is a [`pystac.ItemCollection`](https://pystac.readthedocs.io/en/stable/api/item_collection.html#pystac-item-collection). We can see that 4 items matched our search criteria."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c62e1ca1-0be3-45a6-bb8a-5fd048a5478c",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(items)"
]
},
{
"cell_type": "markdown",
"id": "6b36c181-a163-4e40-9ad0-3accacc82010",
"metadata": {},
"source": [
"Each [`pystac.Item`](https://pystac.readthedocs.io/en/stable/api/pystac.html#pystac.Item) in this `ItemCollection` includes all the metadata for that scene. [STAC Items](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md) are GeoJSON features, and so can be loaded by libraries like [geopandas](http://geopandas.readthedocs.io/)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "b95c28ed-0ab1-4d4c-aa0f-a0382f694238",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>geometry</th>\n",
" <th>gsd</th>\n",
" <th>created</th>\n",
" <th>sci:doi</th>\n",
" <th>datetime</th>\n",
" <th>platform</th>\n",
" <th>proj:epsg</th>\n",
" <th>proj:shape</th>\n",
" <th>description</th>\n",
" <th>instruments</th>\n",
" <th>...</th>\n",
" <th>landsat:wrs_row</th>\n",
" <th>landsat:scene_id</th>\n",
" <th>landsat:wrs_path</th>\n",
" <th>landsat:wrs_type</th>\n",
" <th>view:sun_azimuth</th>\n",
" <th>landsat:correction</th>\n",
" <th>view:sun_elevation</th>\n",
" <th>landsat:cloud_cover_land</th>\n",
" <th>landsat:collection_number</th>\n",
" <th>landsat:collection_category</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>POLYGON ((-122.72549 48.50884, -120.29248 48.0...</td>\n",
" <td>30</td>\n",
" <td>2022-05-06T18:04:17.126358Z</td>\n",
" <td>10.5066/P9OGBGM6</td>\n",
" <td>2020-12-29T18:55:56.738265Z</td>\n",
" <td>landsat-8</td>\n",
" <td>32610</td>\n",
" <td>[7881, 7781]</td>\n",
" <td>Landsat Collection 2 Level-2</td>\n",
" <td>[oli, tirs]</td>\n",
" <td>...</td>\n",
" <td>027</td>\n",
" <td>LC80460272020364LGN00</td>\n",
" <td>046</td>\n",
" <td>2</td>\n",
" <td>162.253231</td>\n",
" <td>L2SP</td>\n",
" <td>17.458298</td>\n",
" <td>100.00</td>\n",
" <td>02</td>\n",
" <td>T2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>POLYGON ((-124.52046 48.44245, -121.93932 48.0...</td>\n",
" <td>30</td>\n",
" <td>2022-05-06T17:25:29.626986Z</td>\n",
" <td>10.5066/P9C7I13B</td>\n",
" <td>2020-12-28T18:20:32.609164Z</td>\n",
" <td>landsat-7</td>\n",
" <td>32610</td>\n",
" <td>[7361, 8341]</td>\n",
" <td>Landsat Collection 2 Level-2</td>\n",
" <td>[etm+]</td>\n",
" <td>...</td>\n",
" <td>027</td>\n",
" <td>LE70470272020363EDC00</td>\n",
" <td>047</td>\n",
" <td>2</td>\n",
" <td>152.689113</td>\n",
" <td>L2SP</td>\n",
" <td>14.678880</td>\n",
" <td>32.00</td>\n",
" <td>02</td>\n",
" <td>T1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>POLYGON ((-122.96802 48.44547, -120.39024 48.0...</td>\n",
" <td>30</td>\n",
" <td>2022-05-06T18:01:04.319403Z</td>\n",
" <td>10.5066/P9C7I13B</td>\n",
" <td>2020-12-21T18:14:50.812768Z</td>\n",
" <td>landsat-7</td>\n",
" <td>32610</td>\n",
" <td>[7251, 8251]</td>\n",
" <td>Landsat Collection 2 Level-2</td>\n",
" <td>[etm+]</td>\n",
" <td>...</td>\n",
" <td>027</td>\n",
" <td>LE70460272020356EDC00</td>\n",
" <td>046</td>\n",
" <td>2</td>\n",
" <td>153.649177</td>\n",
" <td>L2SP</td>\n",
" <td>14.779612</td>\n",
" <td>24.00</td>\n",
" <td>02</td>\n",
" <td>T2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>POLYGON ((-124.27547 48.50831, -121.84167 48.0...</td>\n",
" <td>30</td>\n",
" <td>2022-05-06T17:46:22.246696Z</td>\n",
" <td>10.5066/P9OGBGM6</td>\n",
" <td>2020-12-20T19:02:09.878796Z</td>\n",
" <td>landsat-8</td>\n",
" <td>32610</td>\n",
" <td>[7971, 7861]</td>\n",
" <td>Landsat Collection 2 Level-2</td>\n",
" <td>[oli, tirs]</td>\n",
" <td>...</td>\n",
" <td>027</td>\n",
" <td>LC80470272020355LGN00</td>\n",
" <td>047</td>\n",
" <td>2</td>\n",
" <td>163.360118</td>\n",
" <td>L2SP</td>\n",
" <td>17.414441</td>\n",
" <td>100.00</td>\n",
" <td>02</td>\n",
" <td>T2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>POLYGON ((-122.72996 48.50858, -120.29690 48.0...</td>\n",
" <td>30</td>\n",
" <td>2022-05-06T18:04:16.935800Z</td>\n",
" <td>10.5066/P9OGBGM6</td>\n",
" <td>2020-12-13T18:56:00.096447Z</td>\n",
" <td>landsat-8</td>\n",
" <td>32610</td>\n",
" <td>[7881, 7781]</td>\n",
" <td>Landsat Collection 2 Level-2</td>\n",
" <td>[oli, tirs]</td>\n",
" <td>...</td>\n",
" <td>027</td>\n",
" <td>LC80460272020348LGN00</td>\n",
" <td>046</td>\n",
" <td>2</td>\n",
" <td>164.126188</td>\n",
" <td>L2SP</td>\n",
" <td>17.799744</td>\n",
" <td>98.64</td>\n",
" <td>02</td>\n",
" <td>T2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>POLYGON ((-124.51935 48.44597, -121.93965 48.0...</td>\n",
" <td>30</td>\n",
" <td>2022-05-06T17:25:29.412798Z</td>\n",
" <td>10.5066/P9C7I13B</td>\n",
" <td>2020-12-12T18:21:42.991249Z</td>\n",
" <td>landsat-7</td>\n",
" <td>32610</td>\n",
" <td>[7361, 8341]</td>\n",
" <td>Landsat Collection 2 Level-2</td>\n",
" <td>[etm+]</td>\n",
" <td>...</td>\n",
" <td>027</td>\n",
" <td>LE70470272020347EDC00</td>\n",
" <td>047</td>\n",
" <td>2</td>\n",
" <td>154.692691</td>\n",
" <td>L2SP</td>\n",
" <td>15.427422</td>\n",
" <td>12.00</td>\n",
" <td>02</td>\n",
" <td>T1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>POLYGON ((-122.98709 48.44790, -120.40945 48.0...</td>\n",
" <td>30</td>\n",
" <td>2022-05-06T18:01:04.178839Z</td>\n",
" <td>10.5066/P9C7I13B</td>\n",
" <td>2020-12-05T18:16:03.755599Z</td>\n",
" <td>landsat-7</td>\n",
" <td>32610</td>\n",
" <td>[7281, 8251]</td>\n",
" <td>Landsat Collection 2 Level-2</td>\n",
" <td>[etm+]</td>\n",
" <td>...</td>\n",
" <td>027</td>\n",
" <td>LE70460272020340EDC00</td>\n",
" <td>046</td>\n",
" <td>2</td>\n",
" <td>155.308739</td>\n",
" <td>L2SP</td>\n",
" <td>16.313570</td>\n",
" <td>2.00</td>\n",
" <td>02</td>\n",
" <td>T1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>POLYGON ((-124.27385 48.50833, -121.83965 48.0...</td>\n",
" <td>30</td>\n",
" <td>2022-05-06T17:46:22.097338Z</td>\n",
" <td>10.5066/P9OGBGM6</td>\n",
" <td>2020-12-04T19:02:11.194486Z</td>\n",
" <td>landsat-8</td>\n",
" <td>32610</td>\n",
" <td>[7971, 7861]</td>\n",
" <td>Landsat Collection 2 Level-2</td>\n",
" <td>[oli, tirs]</td>\n",
" <td>...</td>\n",
" <td>027</td>\n",
" <td>LC80470272020339LGN00</td>\n",
" <td>047</td>\n",
" <td>2</td>\n",
" <td>164.914060</td>\n",
" <td>L2SP</td>\n",
" <td>18.807230</td>\n",
" <td>1.90</td>\n",
" <td>02</td>\n",
" <td>T1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8 rows × 23 columns</p>\n",
"</div>"
],
"text/plain": [
" geometry gsd \\\n",
"0 POLYGON ((-122.72549 48.50884, -120.29248 48.0... 30 \n",
"1 POLYGON ((-124.52046 48.44245, -121.93932 48.0... 30 \n",
"2 POLYGON ((-122.96802 48.44547, -120.39024 48.0... 30 \n",
"3 POLYGON ((-124.27547 48.50831, -121.84167 48.0... 30 \n",
"4 POLYGON ((-122.72996 48.50858, -120.29690 48.0... 30 \n",
"5 POLYGON ((-124.51935 48.44597, -121.93965 48.0... 30 \n",
"6 POLYGON ((-122.98709 48.44790, -120.40945 48.0... 30 \n",
"7 POLYGON ((-124.27385 48.50833, -121.83965 48.0... 30 \n",
"\n",
" created sci:doi datetime \\\n",
"0 2022-05-06T18:04:17.126358Z 10.5066/P9OGBGM6 2020-12-29T18:55:56.738265Z \n",
"1 2022-05-06T17:25:29.626986Z 10.5066/P9C7I13B 2020-12-28T18:20:32.609164Z \n",
"2 2022-05-06T18:01:04.319403Z 10.5066/P9C7I13B 2020-12-21T18:14:50.812768Z \n",
"3 2022-05-06T17:46:22.246696Z 10.5066/P9OGBGM6 2020-12-20T19:02:09.878796Z \n",
"4 2022-05-06T18:04:16.935800Z 10.5066/P9OGBGM6 2020-12-13T18:56:00.096447Z \n",
"5 2022-05-06T17:25:29.412798Z 10.5066/P9C7I13B 2020-12-12T18:21:42.991249Z \n",
"6 2022-05-06T18:01:04.178839Z 10.5066/P9C7I13B 2020-12-05T18:16:03.755599Z \n",
"7 2022-05-06T17:46:22.097338Z 10.5066/P9OGBGM6 2020-12-04T19:02:11.194486Z \n",
"\n",
" platform proj:epsg proj:shape description \\\n",
"0 landsat-8 32610 [7881, 7781] Landsat Collection 2 Level-2 \n",
"1 landsat-7 32610 [7361, 8341] Landsat Collection 2 Level-2 \n",
"2 landsat-7 32610 [7251, 8251] Landsat Collection 2 Level-2 \n",
"3 landsat-8 32610 [7971, 7861] Landsat Collection 2 Level-2 \n",
"4 landsat-8 32610 [7881, 7781] Landsat Collection 2 Level-2 \n",
"5 landsat-7 32610 [7361, 8341] Landsat Collection 2 Level-2 \n",
"6 landsat-7 32610 [7281, 8251] Landsat Collection 2 Level-2 \n",
"7 landsat-8 32610 [7971, 7861] Landsat Collection 2 Level-2 \n",
"\n",
" instruments ... landsat:wrs_row landsat:scene_id landsat:wrs_path \\\n",
"0 [oli, tirs] ... 027 LC80460272020364LGN00 046 \n",
"1 [etm+] ... 027 LE70470272020363EDC00 047 \n",
"2 [etm+] ... 027 LE70460272020356EDC00 046 \n",
"3 [oli, tirs] ... 027 LC80470272020355LGN00 047 \n",
"4 [oli, tirs] ... 027 LC80460272020348LGN00 046 \n",
"5 [etm+] ... 027 LE70470272020347EDC00 047 \n",
"6 [etm+] ... 027 LE70460272020340EDC00 046 \n",
"7 [oli, tirs] ... 027 LC80470272020339LGN00 047 \n",
"\n",
" landsat:wrs_type view:sun_azimuth landsat:correction view:sun_elevation \\\n",
"0 2 162.253231 L2SP 17.458298 \n",
"1 2 152.689113 L2SP 14.678880 \n",
"2 2 153.649177 L2SP 14.779612 \n",
"3 2 163.360118 L2SP 17.414441 \n",
"4 2 164.126188 L2SP 17.799744 \n",
"5 2 154.692691 L2SP 15.427422 \n",
"6 2 155.308739 L2SP 16.313570 \n",
"7 2 164.914060 L2SP 18.807230 \n",
"\n",
" landsat:cloud_cover_land landsat:collection_number \\\n",
"0 100.00 02 \n",
"1 32.00 02 \n",
"2 24.00 02 \n",
"3 100.00 02 \n",
"4 98.64 02 \n",
"5 12.00 02 \n",
"6 2.00 02 \n",
"7 1.90 02 \n",
"\n",
" landsat:collection_category \n",
"0 T2 \n",
"1 T1 \n",
"2 T2 \n",
"3 T2 \n",
"4 T2 \n",
"5 T1 \n",
"6 T1 \n",
"7 T1 \n",
"\n",
"[8 rows x 23 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import geopandas\n",
"\n",
"df = geopandas.GeoDataFrame.from_features(items.to_dict(), crs=\"epsg:4326\")\n",
"df"
]
},
{
"cell_type": "markdown",
"id": "e88bc7eb-fd2f-48b1-a3d7-dc96d8b7635c",
"metadata": {},
"source": [
"Some collections implement the `eo` extension, which we can use to sort the items by cloudiness. We'll grab an item with low cloudiness:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c60accc9-4126-4609-8a65-406719b5485b",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<Item id=LC08_L2SP_047027_20201204_02_T1>\n"
]
}
],
"source": [
"selected_item = min(items, key=lambda item: item.properties[\"eo:cloud_cover\"])\n",
"print(selected_item)"
]
},
{
"cell_type": "markdown",
"id": "9be696c5-c9b9-4dcb-a876-a645d384fa83",
"metadata": {},
"source": [
"Each STAC item has one or more [Assets](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md#asset-object), which include links to the actual files."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f507b45d-5985-4f10-9850-c1b3947c9908",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃<span style=\"font-weight: bold\"> Asset Key </span>┃<span style=\"font-weight: bold\"> Description </span>┃\n",
"┡━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ qa │ Surface Temperature Quality Assessment Band │\n",
"│ ang │ Angle Coefficients File │\n",
"│ red │ Red Band │\n",
"│ blue │ Blue Band │\n",
"│ drad │ Downwelled Radiance Band │\n",
"│ emis │ Emissivity Band │\n",
"│ emsd │ Emissivity Standard Deviation Band │\n",
"│ trad │ Thermal Radiance Band │\n",
"│ urad │ Upwelled Radiance Band │\n",
"│ atran │ Atmospheric Transmittance Band │\n",
"│ cdist │ Cloud Distance Band │\n",
"│ green │ Green Band │\n",
"│ nir08 │ Near Infrared Band 0.8 │\n",
"│ lwir11 │ Surface Temperature Band │\n",
"│ swir16 │ Short-wave Infrared Band 1.6 │\n",
"│ swir22 │ Short-wave Infrared Band 2.2 │\n",
"│ coastal │ Coastal/Aerosol Band │\n",
"│ mtl.txt │ Product Metadata File (txt) │\n",
"│ mtl.xml │ Product Metadata File (xml) │\n",
"│ mtl.json │ Product Metadata File (json) │\n",
"│ qa_pixel │ Pixel Quality Assessment Band │\n",
"│ qa_radsat │ Radiometric Saturation and Terrain Occlusion Quality Assessment Band │\n",
"│ qa_aerosol │ Aerosol Quality Assessment Band │\n",
"│ tilejson │ TileJSON with default rendering │\n",
"│ rendered_preview │ Rendered preview │\n",
"└──────────────────┴──────────────────────────────────────────────────────────────────────┘\n",
"</pre>\n"
],
"text/plain": [
"┏━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
"┃\u001b[1m \u001b[0m\u001b[1mAsset Key \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mDescription \u001b[0m\u001b[1m \u001b[0m┃\n",
"┡━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
"│ qa │ Surface Temperature Quality Assessment Band │\n",
"│ ang │ Angle Coefficients File │\n",
"│ red │ Red Band │\n",
"│ blue │ Blue Band │\n",
"│ drad │ Downwelled Radiance Band │\n",
"│ emis │ Emissivity Band │\n",
"│ emsd │ Emissivity Standard Deviation Band │\n",
"│ trad │ Thermal Radiance Band │\n",
"│ urad │ Upwelled Radiance Band │\n",
"│ atran │ Atmospheric Transmittance Band │\n",
"│ cdist │ Cloud Distance Band │\n",
"│ green │ Green Band │\n",
"│ nir08 │ Near Infrared Band 0.8 │\n",
"│ lwir11 │ Surface Temperature Band │\n",
"│ swir16 │ Short-wave Infrared Band 1.6 │\n",
"│ swir22 │ Short-wave Infrared Band 2.2 │\n",
"│ coastal │ Coastal/Aerosol Band │\n",
"│ mtl.txt │ Product Metadata File (txt) │\n",
"│ mtl.xml │ Product Metadata File (xml) │\n",
"│ mtl.json │ Product Metadata File (json) │\n",
"│ qa_pixel │ Pixel Quality Assessment Band │\n",
"│ qa_radsat │ Radiometric Saturation and Terrain Occlusion Quality Assessment Band │\n",
"│ qa_aerosol │ Aerosol Quality Assessment Band │\n",
"│ tilejson │ TileJSON with default rendering │\n",
"│ rendered_preview │ Rendered preview │\n",
"└──────────────────┴──────────────────────────────────────────────────────────────────────┘\n"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import rich.table\n",
"\n",
"table = rich.table.Table(\"Asset Key\", \"Description\")\n",
"for asset_key, asset in selected_item.assets.items():\n",
" table.add_row(asset_key, asset.title)\n",
"\n",
"table"
]
},
{
"cell_type": "markdown",
"id": "fe4053e4-6c1e-46b7-947d-79811a2877e4",
"metadata": {},
"source": [
"Here, we'll inspect the `rendered_preview` asset."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f175aa10-8f97-4ea3-8fda-c6fe4469baea",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"{'href': 'https://planetarycomputer.microsoft.com/api/data/v1/item/preview.png?collection=landsat-c2-l2&item=LC08_L2SP_047027_20201204_02_T1&assets=red&assets=green&assets=blue&color_formula=gamma+RGB+2.7%2C+saturation+1.5%2C+sigmoidal+RGB+15+0.55&format=png',\n",
" 'type': 'image/png',\n",
" 'title': 'Rendered preview',\n",
" 'rel': 'preview',\n",
" 'roles': ['overview']}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"selected_item.assets[\"rendered_preview\"].to_dict()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "50535606-4891-40d4-9e52-7d0056583681",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"https://planetarycomputer.microsoft.com/api/data/v1/item/preview.png?collection=landsat-c2-l2&item=LC08_L2SP_047027_20201204_02_T1&assets=red&assets=green&assets=blue&color_formula=gamma+RGB+2.7%2C+saturation+1.5%2C+sigmoidal+RGB+15+0.55&format=png\" width=\"500\"/>"
],
"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from IPython.display import Image\n",
"\n",
"Image(url=selected_item.assets[\"rendered_preview\"].href, width=500)"
]
},
{
"cell_type": "markdown",
"id": "5abf7884-00c5-49fd-b27f-a57a23c00af5",
"metadata": {},
"source": [
"That `rendered_preview` asset is generated dynamically from the raw data using the Planetary Computer's [data API](http://planetarycomputer.microsoft.com/api/data/v1/). We can access the raw data, stored as Cloud Optimzied GeoTIFFs in Azure Blob Storage, using one of the other assets.\n",
"\n",
"The actual data assets are in *private* [Azure Blob Storage containers](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction#containers). If forget to pass `modifier=planetary_computer.sign_inplace` or manually sign the item, then you'll get a 404 when trying to access the asset.\n",
"\n",
"That's why we included the `modifier=planetary_computer.sign_inplace` when we created the `pystac_client.Client` earlier. With that, the results returned by pystac-client are automatically signed, so that a token granting access to the file is included in the URL."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "d43f7c1b-4463-4802-a112-aa466d6791c8",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'https://landsateuwest.blob.core.windows.net/landsat-c2/level-2/standard/oli-tirs/2020/047/027/LC08_L2SP_047027_20201204_20210313_02_T1/LC08_L2SP_047027_20201204_20210313_02_T1_SR_B2.TIF?st=2023-11-06T12%3A35%3A44Z&se=2023-11-14T12%3A35%3A44Z&sp=rl&sv'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"selected_item.assets[\"blue\"].href[:250]"
]
},
{
"cell_type": "markdown",
"id": "3779e4dc-72d0-4a77-8d83-b49f67e16a5c",
"metadata": {},
"source": [
" Everything after the `?` in that URL is a [SAS token](https://docs.microsoft.com/en-us/azure/storage/common/storage-sas-overview) grants access to the data. See https://planetarycomputer.microsoft.com/docs/concepts/sas/ for more on using tokens to access data."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "aefcb71d-31ba-4211-b7a5-7966c13304ef",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"200"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import requests\n",
"\n",
"requests.head(selected_item.assets[\"blue\"].href).status_code"
]
},
{
"cell_type": "markdown",
"id": "65686af5-414e-4482-af27-fb680b179930",
"metadata": {},
"source": [
"The `200` status code indicates that we were able to successfully access the data using the \"signed\" URL with the SAS token included."
]
},
{
"cell_type": "markdown",
"id": "29feea6d-400d-4573-82ba-835918adb701",
"metadata": {},
"source": [
"We can load up that single COG using libraries like [rioxarray](https://corteva.github.io/rioxarray/html/rioxarray.html) or [rasterio](https://rasterio.readthedocs.io/en/latest/)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "58481f78-40b5-486d-bf36-ab333206bdb4",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/quickstarts-reading-stac.ipynb/11.png\"/>"
],
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# import xarray as xr\n",
"import rioxarray\n",
"\n",
"ds = rioxarray.open_rasterio(\n",
" selected_item.assets[\"blue\"].href, overview_level=4\n",
").squeeze()\n",
"img = ds.plot(cmap=\"Blues\", add_colorbar=False)\n",
"img.axes.set_axis_off();"
]
},
{
"cell_type": "markdown",
"id": "d3c08641-fbb6-425f-a3ca-975ca1d425a4",
"metadata": {},
"source": [
"If you wish to work with multiple STAC items as a datacube, you can use libraries like [stackstac](https://stackstac.readthedocs.io/) or [odc-stac](https://odc-stac.readthedocs.io/en/latest/index.html)."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "d4a749a5-4a05-405b-8c81-9c10317c9e33",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/srv/conda/envs/notebook/lib/python3.11/site-packages/stackstac/prepare.py:363: 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"
]
},
{
"data": {
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":root {\n",
" --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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" --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
" --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
" --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
" --xr-background-color: var(--jp-layout-color0, white);\n",
" --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
" --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
"}\n",
"\n",
"html[theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
" --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
" --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
" --xr-border-color: #1F1F1F;\n",
" --xr-disabled-color: #515151;\n",
" --xr-background-color: #111111;\n",
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".xr-wrap {\n",
" display: block !important;\n",
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"\n",
".xr-text-repr-fallback {\n",
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" display: none;\n",
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"\n",
".xr-header {\n",
" padding-top: 6px;\n",
" padding-bottom: 6px;\n",
" margin-bottom: 4px;\n",
" border-bottom: solid 1px var(--xr-border-color);\n",
"}\n",
"\n",
".xr-header > div,\n",
".xr-header > ul {\n",
" display: inline;\n",
" margin-top: 0;\n",
" margin-bottom: 0;\n",
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"\n",
".xr-obj-type,\n",
".xr-array-name {\n",
" margin-left: 2px;\n",
" margin-right: 10px;\n",
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"\n",
".xr-obj-type {\n",
" color: var(--xr-font-color2);\n",
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"\n",
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
" grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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"\n",
".xr-section-item {\n",
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".xr-section-item input {\n",
" display: none;\n",
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".xr-section-item input + label {\n",
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"\n",
".xr-var-item > div,\n",
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" background-color: var(--xr-background-color-row-even);\n",
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" grid-column: 1;\n",
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"\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",
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"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
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"\n",
".xr-index-preview {\n",
" grid-column: 2 / 5;\n",
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"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
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".xr-var-name:hover,\n",
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"\n",
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"\n",
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"\n",
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"</style><pre class='xr-text-repr-fallback'><xarray.DataArray 'stackstac-58124a3f2aeb9e86fe45c8d5489954e7' (time: 8,\n",
" band: 22,\n",
" y: 7972,\n",
" x: 12372)>\n",
"dask.array<fetch_raster_window, shape=(8, 22, 7972, 12372), dtype=float64, chunksize=(1, 1, 1024, 1024), chunktype=numpy.ndarray>\n",
"Coordinates: (12/31)\n",
" * time (time) datetime64[ns] 2020-12-04T19:02:11.19...\n",
" id (time) <U31 'LC08_L2SP_047027_20201204_02_T1...\n",
" * band (band) <U13 'qa' 'red' ... 'atmos_opacity'\n",
" * x (x) float64 3.339e+05 3.339e+05 ... 7.05e+05\n",
" * y (y) float64 5.374e+06 5.374e+06 ... 5.135e+06\n",
" landsat:wrs_type <U1 '2'\n",
" ... ...\n",
" title (band) object 'Surface Temperature Quality A...\n",
" classification:bitfields (band) object None None ... None\n",
" common_name (band) object None None None ... None None\n",
" center_wavelength (band) object None None None ... None None\n",
" full_width_half_max (band) object None None None ... 2.05 None None\n",
" epsg int64 32610\n",
"Attributes:\n",
" spec: RasterSpec(epsg=32610, bounds=(333870.0, 5135070.0, 705030.0...\n",
" crs: epsg:32610\n",
" transform: | 30.00, 0.00, 333870.00|\\n| 0.00,-30.00, 5374230.00|\\n| 0.0...\n",
" resolution: 30.0</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-58124a3f2aeb9e86fe45c8d5489954e7'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 8</li><li><span class='xr-has-index'>band</span>: 22</li><li><span class='xr-has-index'>y</span>: 7972</li><li><span class='xr-has-index'>x</span>: 12372</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a6c4a2d8-7a65-46a3-ab68-35d921034b97' class='xr-array-in' type='checkbox' checked><label for='section-a6c4a2d8-7a65-46a3-ab68-35d921034b97' 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<chunksize=(1, 1, 1024, 1024), meta=np.ndarray></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> 129.33 GiB </td>\n",
" <td> 8.00 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (8, 22, 7972, 12372) </td>\n",
" <td> (1, 1, 1024, 1024) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 18304 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",
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"</table></div></div></li><li class='xr-section-item'><input id='section-645aa64e-5139-4d94-8c8c-de949b5e5806' class='xr-section-summary-in' type='checkbox' ><label for='section-645aa64e-5139-4d94-8c8c-de949b5e5806' class='xr-section-summary' >Coordinates: <span>(31)</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'>2020-12-04T19:02:11.194486 ... 2...</div><input id='attrs-0ed9baf8-d459-47cf-98ae-8ab945c081e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0ed9baf8-d459-47cf-98ae-8ab945c081e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4a85017-6a52-478a-96a1-15dd1203ed1b' class='xr-var-data-in' type='checkbox'><label for='data-a4a85017-6a52-478a-96a1-15dd1203ed1b' 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(['2020-12-04T19:02:11.194486000', '2020-12-05T18:16:03.755599000',\n",
" '2020-12-12T18:21:42.991249000', '2020-12-13T18:56:00.096447000',\n",
" '2020-12-20T19:02:09.878796000', '2020-12-21T18:14:50.812768000',\n",
" '2020-12-28T18:20:32.609164000', '2020-12-29T18:55:56.738265000'],\n",
" dtype='datetime64[ns]')</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'><U31</div><div class='xr-var-preview xr-preview'>'LC08_L2SP_047027_20201204_02_T1...</div><input id='attrs-737f241f-32e5-4034-aad3-7b693bcd4392' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-737f241f-32e5-4034-aad3-7b693bcd4392' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-853737c7-61ac-4ad8-a6c5-2e38e29e44f2' class='xr-var-data-in' type='checkbox'><label for='data-853737c7-61ac-4ad8-a6c5-2e38e29e44f2' 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(['LC08_L2SP_047027_20201204_02_T1',\n",
" 'LE07_L2SP_046027_20201205_02_T1',\n",
" 'LE07_L2SP_047027_20201212_02_T1',\n",
" 'LC08_L2SP_046027_20201213_02_T2',\n",
" 'LC08_L2SP_047027_20201220_02_T2',\n",
" 'LE07_L2SP_046027_20201221_02_T2',\n",
" 'LE07_L2SP_047027_20201228_02_T1',\n",
" 'LC08_L2SP_046027_20201229_02_T2'], dtype='<U31')</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'><U13</div><div class='xr-var-preview xr-preview'>'qa' 'red' ... 'atmos_opacity'</div><input id='attrs-8391380c-751b-421d-b65f-1edf7c223771' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8391380c-751b-421d-b65f-1edf7c223771' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eeed7af9-283a-4b7e-8db0-6cae601d0519' class='xr-var-data-in' type='checkbox'><label for='data-eeed7af9-283a-4b7e-8db0-6cae601d0519' 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(['qa', 'red', 'blue', 'drad', 'emis', 'emsd', 'trad', 'urad', 'atran',\n",
" 'cdist', 'green', 'nir08', 'lwir11', 'swir16', 'swir22', 'coastal',\n",
" 'qa_pixel', 'qa_radsat', 'qa_aerosol', 'lwir', 'cloud_qa',\n",
" 'atmos_opacity'], dtype='<U13')</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'>3.339e+05 3.339e+05 ... 7.05e+05</div><input id='attrs-e53c321f-75a3-4681-b30a-105f0577c4f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e53c321f-75a3-4681-b30a-105f0577c4f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6edfa0a-8255-4f23-8433-be044b428364' class='xr-var-data-in' type='checkbox'><label for='data-c6edfa0a-8255-4f23-8433-be044b428364' 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([333870., 333900., 333930., ..., 704940., 704970., 705000.])</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'>5.374e+06 5.374e+06 ... 5.135e+06</div><input id='attrs-7407d7c8-b678-4d92-b61b-9f22e23d9b88' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7407d7c8-b678-4d92-b61b-9f22e23d9b88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5136d43-a08b-4ba4-a6a4-6930b9a3ca8c' class='xr-var-data-in' type='checkbox'><label for='data-d5136d43-a08b-4ba4-a6a4-6930b9a3ca8c' 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([5374230., 5374200., 5374170., ..., 5135160., 5135130., 5135100.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landsat:wrs_type</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'><U1</div><div class='xr-var-preview xr-preview'>'2'</div><input id='attrs-90a6d6b2-d976-4410-b997-8539599bbe3d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-90a6d6b2-d976-4410-b997-8539599bbe3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0c1e06b-92d8-4297-9a12-7c79c15ce7a9' class='xr-var-data-in' type='checkbox'><label for='data-a0c1e06b-92d8-4297-9a12-7c79c15ce7a9' 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('2', dtype='<U1')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>platform</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'><U9</div><div class='xr-var-preview xr-preview'>'landsat-8' ... 'landsat-8'</div><input id='attrs-6333fca9-9f3b-408f-93f5-5082f2585f05' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6333fca9-9f3b-408f-93f5-5082f2585f05' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30e90ebe-4b63-45aa-ba93-11028476f55f' class='xr-var-data-in' type='checkbox'><label for='data-30e90ebe-4b63-45aa-ba93-11028476f55f' 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(['landsat-8', 'landsat-7', 'landsat-7', 'landsat-8', 'landsat-8',\n",
" 'landsat-7', 'landsat-7', 'landsat-8'], dtype='<U9')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landsat:wrs_row</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'><U3</div><div class='xr-var-preview xr-preview'>'027'</div><input id='attrs-3f036ddb-e87e-4c8d-91d8-acf8c04e0304' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3f036ddb-e87e-4c8d-91d8-acf8c04e0304' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cf6fafc-fde8-49b0-839c-a85c506d0cd2' class='xr-var-data-in' type='checkbox'><label for='data-8cf6fafc-fde8-49b0-839c-a85c506d0cd2' 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('027', dtype='<U3')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>created</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'><U27</div><div class='xr-var-preview xr-preview'>'2022-05-06T17:46:22.097338Z' .....</div><input id='attrs-8bc4f214-395b-4908-b540-50dddd9cae7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8bc4f214-395b-4908-b540-50dddd9cae7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e03d10c-2b3d-44d5-8cc4-cddba1ec3244' class='xr-var-data-in' type='checkbox'><label for='data-8e03d10c-2b3d-44d5-8cc4-cddba1ec3244' 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(['2022-05-06T17:46:22.097338Z', '2022-05-06T18:01:04.178839Z',\n",
" '2022-05-06T17:25:29.412798Z', '2022-05-06T18:04:16.935800Z',\n",
" '2022-05-06T17:46:22.246696Z', '2022-05-06T18:01:04.319403Z',\n",
" '2022-05-06T17:25:29.626986Z', '2022-05-06T18:04:17.126358Z'],\n",
" dtype='<U27')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>view:off_nadir</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-53aba504-b602-4188-9469-b68084c58ee5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53aba504-b602-4188-9469-b68084c58ee5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fec9a3d-9acb-4dbd-9e64-7b63e8ae1775' class='xr-var-data-in' type='checkbox'><label for='data-6fec9a3d-9acb-4dbd-9e64-7b63e8ae1775' 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)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landsat:wrs_path</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'><U3</div><div class='xr-var-preview xr-preview'>'047' '046' '047' ... '047' '046'</div><input id='attrs-f16293f3-9fa8-4a5f-b922-760716ab943f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f16293f3-9fa8-4a5f-b922-760716ab943f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2738914e-9dd1-4299-bec1-69cd08913cd6' class='xr-var-data-in' type='checkbox'><label for='data-2738914e-9dd1-4299-bec1-69cd08913cd6' 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(['047', '046', '047', '046', '047', '046', '047', '046'],\n",
" dtype='<U3')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landsat:scene_id</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'><U21</div><div class='xr-var-preview xr-preview'>'LC80470272020339LGN00' ... 'LC8...</div><input id='attrs-b67d8b75-6ecd-4292-9507-86e70fbf41d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b67d8b75-6ecd-4292-9507-86e70fbf41d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d86089b3-a144-4f72-901f-17f649b1af01' class='xr-var-data-in' type='checkbox'><label for='data-d86089b3-a144-4f72-901f-17f649b1af01' 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(['LC80470272020339LGN00', 'LE70460272020340EDC00',\n",
" 'LE70470272020347EDC00', 'LC80460272020348LGN00',\n",
" 'LC80470272020355LGN00', 'LE70460272020356EDC00',\n",
" 'LE70470272020363EDC00', 'LC80460272020364LGN00'], dtype='<U21')</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'>32610</div><input id='attrs-cbd617f0-b200-486e-9e9b-7c4338e58b8a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cbd617f0-b200-486e-9e9b-7c4338e58b8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-486d7a45-1f39-4f2e-94b3-d3daaa1904f3' class='xr-var-data-in' type='checkbox'><label for='data-486d7a45-1f39-4f2e-94b3-d3daaa1904f3' 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(32610)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landsat:correction</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'><U4</div><div class='xr-var-preview xr-preview'>'L2SP'</div><input id='attrs-b957f811-1327-49eb-b6bf-a0962051256b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b957f811-1327-49eb-b6bf-a0962051256b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-516a860c-edda-48d9-b84b-94eaf38e1bd9' class='xr-var-data-in' type='checkbox'><label for='data-516a860c-edda-48d9-b84b-94eaf38e1bd9' 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('L2SP', dtype='<U4')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>instruments</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>['oli', 'tirs'] ... ['oli', 'tirs']</div><input id='attrs-fa70dfed-91cc-4dad-97db-f557594a23ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa70dfed-91cc-4dad-97db-f557594a23ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50621efc-e2ba-4c06-9ab8-98d6f0efd21f' class='xr-var-data-in' type='checkbox'><label for='data-50621efc-e2ba-4c06-9ab8-98d6f0efd21f' 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([list(['oli', 'tirs']), list(['etm+']), list(['etm+']),\n",
" list(['oli', 'tirs']), list(['oli', 'tirs']), list(['etm+']),\n",
" list(['etm+']), list(['oli', 'tirs'])], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>view:sun_elevation</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>18.81 16.31 15.43 ... 14.68 17.46</div><input id='attrs-26fd2b8f-3ced-47c0-aac6-0b5b1512b296' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-26fd2b8f-3ced-47c0-aac6-0b5b1512b296' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40684fb1-11eb-441b-b624-5546a18edb0a' class='xr-var-data-in' type='checkbox'><label for='data-40684fb1-11eb-441b-b624-5546a18edb0a' 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([18.80722985, 16.31357033, 15.42742211, 17.7997438 , 17.41444102,\n",
" 14.77961157, 14.67887964, 17.45829837])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landsat:cloud_cover_land</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.9 2.0 12.0 ... 24.0 32.0 100.0</div><input id='attrs-2235777f-e773-4788-971c-e4610a5262cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2235777f-e773-4788-971c-e4610a5262cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dced4f67-385f-41cc-a5e4-256d2708ed47' class='xr-var-data-in' type='checkbox'><label for='data-dced4f67-385f-41cc-a5e4-256d2708ed47' 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([ 1.9 , 2. , 12. , 98.64, 100. , 24. , 32. , 100. ])</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'>object</div><div class='xr-var-preview xr-preview'>'Collection 2 Level-2 Quality As...</div><input id='attrs-d39287a8-0089-4807-8e49-4d9317b3e3ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d39287a8-0089-4807-8e49-4d9317b3e3ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84ed5589-7af4-40f1-a285-d1f1f879a35e' class='xr-var-data-in' type='checkbox'><label for='data-84ed5589-7af4-40f1-a285-d1f1f879a35e' 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(['Collection 2 Level-2 Quality Assessment Band (ST_QA) Surface Temperature Product',\n",
" None, None,\n",
" 'Collection 2 Level-2 Downwelled Radiance Band (ST_DRAD) Surface Temperature Product',\n",
" 'Collection 2 Level-2 Emissivity Band (ST_EMIS) Surface Temperature Product',\n",
" 'Collection 2 Level-2 Emissivity Standard Deviation Band (ST_EMSD) Surface Temperature Product',\n",
" 'Collection 2 Level-2 Thermal Radiance Band (ST_TRAD) Surface Temperature Product',\n",
" 'Collection 2 Level-2 Upwelled Radiance Band (ST_URAD) Surface Temperature Product',\n",
" 'Collection 2 Level-2 Atmospheric Transmittance Band (ST_ATRAN) Surface Temperature Product',\n",
" 'Collection 2 Level-2 Cloud Distance Band (ST_CDIST) Surface Temperature Product',\n",
" None, None,\n",
" 'Collection 2 Level-2 Thermal Infrared Band (ST_B10) Surface Temperature',\n",
" None,\n",
" 'Collection 2 Level-2 Short-wave Infrared Band 2.2 (SR_B7) Surface Reflectance',\n",
" 'Collection 2 Level-2 Coastal/Aerosol Band (SR_B1) Surface Reflectance',\n",
" 'Collection 2 Level-1 Pixel Quality Assessment Band (QA_PIXEL)',\n",
" None,\n",
" 'Collection 2 Level-2 Aerosol Quality Assessment Band (SR_QA_AEROSOL) Surface Reflectance Product',\n",
" 'Collection 2 Level-2 Thermal Infrared Band (ST_B6) Surface Temperature',\n",
" 'Collection 2 Level-2 Cloud Quality Assessment Band (SR_CLOUD_QA) Surface Reflectance Product',\n",
" 'Collection 2 Level-2 Atmospheric Opacity Band (SR_ATMOS_OPACITY) Surface Reflectance Product'],\n",
" dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>eo:cloud_cover</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.55 2.0 17.0 ... 25.0 31.0 100.0</div><input id='attrs-1c5e79ce-e8eb-45e7-aca6-0c2bc2acefa1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c5e79ce-e8eb-45e7-aca6-0c2bc2acefa1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aac8ea29-db81-4c2d-a506-563fb7b03385' class='xr-var-data-in' type='checkbox'><label for='data-aac8ea29-db81-4c2d-a506-563fb7b03385' 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([ 1.55, 2. , 17. , 98.73, 100. , 25. , 31. , 100. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landsat:collection_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'><U2</div><div class='xr-var-preview xr-preview'>'02'</div><input id='attrs-ed8ff065-bf12-44ff-b250-3d159fc52a0c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed8ff065-bf12-44ff-b250-3d159fc52a0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a2d8891-ea2c-41a1-94fb-adfd623eb5cd' class='xr-var-data-in' type='checkbox'><label for='data-3a2d8891-ea2c-41a1-94fb-adfd623eb5cd' 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('02', dtype='<U2')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>gsd</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None None None ... 60 None None</div><input id='attrs-87960bc7-d552-4a00-87f2-2b4c20a57aec' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-87960bc7-d552-4a00-87f2-2b4c20a57aec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75245acd-4ee1-4018-baa9-09e94ae9b4fd' class='xr-var-data-in' type='checkbox'><label for='data-75245acd-4ee1-4018-baa9-09e94ae9b4fd' 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([None, None, None, None, None, None, None, None, None, None, None,\n",
" None, 100, None, None, None, None, None, None, 60, None, None],\n",
" dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>landsat:collection_category</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'><U2</div><div class='xr-var-preview xr-preview'>'T1' 'T1' 'T1' ... 'T2' 'T1' 'T2'</div><input id='attrs-08264c8d-1e3f-4354-b7c5-a567d3fee2bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-08264c8d-1e3f-4354-b7c5-a567d3fee2bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fcf7487-9e24-4ada-95c4-2f86ac2c27f3' class='xr-var-data-in' type='checkbox'><label for='data-2fcf7487-9e24-4ada-95c4-2f86ac2c27f3' 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(['T1', 'T1', 'T1', 'T2', 'T2', 'T2', 'T1', 'T2'], dtype='<U2')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sci:doi</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'><U16</div><div class='xr-var-preview xr-preview'>'10.5066/P9OGBGM6' ... '10.5066/...</div><input id='attrs-b1e1f5a2-9e03-43f5-8fd3-815dbc1db17d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b1e1f5a2-9e03-43f5-8fd3-815dbc1db17d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9131606-7254-4545-9077-ce3fe0e03681' class='xr-var-data-in' type='checkbox'><label for='data-c9131606-7254-4545-9077-ce3fe0e03681' 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(['10.5066/P9OGBGM6', '10.5066/P9C7I13B', '10.5066/P9C7I13B',\n",
" '10.5066/P9OGBGM6', '10.5066/P9OGBGM6', '10.5066/P9C7I13B',\n",
" '10.5066/P9C7I13B', '10.5066/P9OGBGM6'], dtype='<U16')</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>view:sun_azimuth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>164.9 155.3 154.7 ... 152.7 162.3</div><input id='attrs-e6c4b22b-1f41-4fc1-ab97-84720e006fad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6c4b22b-1f41-4fc1-ab97-84720e006fad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1015250-25aa-41ef-9098-1283539a9e18' class='xr-var-data-in' type='checkbox'><label for='data-a1015250-25aa-41ef-9098-1283539a9e18' 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([164.91405951, 155.30873901, 154.692691 , 164.12618828,\n",
" 163.36011782, 153.64917744, 152.68911295, 162.25323052])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>raster:bands</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>{'unit': 'kelvin', 'scale': 0.01...</div><input id='attrs-f584b57f-be21-4c35-9516-411335d5533d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f584b57f-be21-4c35-9516-411335d5533d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86849486-cf56-4bd0-836f-dd76dfd7fbfc' class='xr-var-data-in' type='checkbox'><label for='data-86849486-cf56-4bd0-836f-dd76dfd7fbfc' 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([{'unit': 'kelvin', 'scale': 0.01, 'nodata': -9999, 'data_type': 'int16', 'spatial_resolution': 30},\n",
" {'scale': 2.75e-05, 'nodata': 0, 'offset': -0.2, 'data_type': 'uint16', 'spatial_resolution': 30},\n",
" {'scale': 2.75e-05, 'nodata': 0, 'offset': -0.2, 'data_type': 'uint16', 'spatial_resolution': 30},\n",
" {'unit': 'watt/steradian/square_meter/micrometer', 'scale': 0.001, 'nodata': -9999, 'data_type': 'int16', 'spatial_resolution': 30},\n",
" {'unit': 'emissivity coefficient', 'scale': 0.0001, 'nodata': -9999, 'data_type': 'int16', 'spatial_resolution': 30},\n",
" {'unit': 'emissivity coefficient', 'scale': 0.0001, 'nodata': -9999, 'data_type': 'int16', 'spatial_resolution': 30},\n",
" {'unit': 'watt/steradian/square_meter/micrometer', 'scale': 0.001, 'nodata': -9999, 'data_type': 'int16', 'spatial_resolution': 30},\n",
" {'unit': 'watt/steradian/square_meter/micrometer', 'scale': 0.001, 'nodata': -9999, 'data_type': 'int16', 'spatial_resolution': 30},\n",
" {'scale': 0.0001, 'nodata': -9999, 'data_type': 'int16', 'spatial_resolution': 30},\n",
" {'unit': 'kilometer', 'scale': 0.01, 'nodata': -9999, 'data_type': 'int16', 'spatial_resolution': 30},\n",
" {'scale': 2.75e-05, 'nodata': 0, 'offset': -0.2, 'data_type': 'uint16', 'spatial_resolution': 30},\n",
" {'scale': 2.75e-05, 'nodata': 0, 'offset': -0.2, 'data_type': 'uint16', 'spatial_resolution': 30},\n",
" {'unit': 'kelvin', 'scale': 0.00341802, 'nodata': 0, 'offset': 149.0, 'data_type': 'uint16', 'spatial_resolution': 30},\n",
" {'scale': 2.75e-05, 'nodata': 0, 'offset': -0.2, 'data_type': 'uint16', 'spatial_resolution': 30},\n",
" {'scale': 2.75e-05, 'nodata': 0, 'offset': -0.2, 'data_type': 'uint16', 'spatial_resolution': 30},\n",
" {'scale': 2.75e-05, 'nodata': 0, 'offset': -0.2, 'data_type': 'uint16', 'spatial_resolution': 30},\n",
" {'unit': 'bit index', 'nodata': 1, 'data_type': 'uint16', 'spatial_resolution': 30},\n",
" {'unit': 'bit index', 'data_type': 'uint16', 'spatial_resolution': 30},\n",
" {'unit': 'bit index', 'nodata': 1, 'data_type': 'uint8', 'spatial_resolution': 30},\n",
" {'unit': 'kelvin', 'scale': 0.00341802, 'nodata': 0, 'offset': 149.0, 'data_type': 'uint16', 'spatial_resolution': 30},\n",
" {'unit': 'bit index', 'data_type': 'uint8', 'spatial_resolution': 30},\n",
" {'scale': 0.001, 'nodata': -9999, 'data_type': 'int16', 'spatial_resolution': 30}],\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'>object</div><div class='xr-var-preview xr-preview'>'Surface Temperature Quality Ass...</div><input id='attrs-0289e784-8ca3-49e6-9f61-0c764b932bb8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0289e784-8ca3-49e6-9f61-0c764b932bb8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbc54bc3-1e39-4d19-a6b8-245c4ca447b0' class='xr-var-data-in' type='checkbox'><label for='data-bbc54bc3-1e39-4d19-a6b8-245c4ca447b0' 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(['Surface Temperature Quality Assessment Band', 'Red Band',\n",
" 'Blue Band', 'Downwelled Radiance Band', 'Emissivity Band',\n",
" 'Emissivity Standard Deviation Band', 'Thermal Radiance Band',\n",
" 'Upwelled Radiance Band', 'Atmospheric Transmittance Band',\n",
" 'Cloud Distance Band', 'Green Band', 'Near Infrared Band 0.8',\n",
" 'Surface Temperature Band', 'Short-wave Infrared Band 1.6',\n",
" 'Short-wave Infrared Band 2.2', 'Coastal/Aerosol Band',\n",
" 'Pixel Quality Assessment Band', None,\n",
" 'Aerosol Quality Assessment Band', 'Surface Temperature Band',\n",
" 'Cloud Quality Assessment Band', 'Atmospheric Opacity Band'],\n",
" dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>classification:bitfields</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None None ... None</div><input id='attrs-99cfb177-2829-484c-8526-83c7579b222e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99cfb177-2829-484c-8526-83c7579b222e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4120a6fd-92c6-4b84-99c8-7a70928fdb87' class='xr-var-data-in' type='checkbox'><label for='data-4120a6fd-92c6-4b84-99c8-7a70928fdb87' 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([None, None, None, None, None, None, None, None, None, None, None,\n",
" None, None, None, None, None, None, None,\n",
" list([{'name': 'fill', 'length': 1, 'offset': 0, 'classes': [{'name': 'not_fill', 'value': 0, 'description': 'Pixel is not fill'}, {'name': 'fill', 'value': 1, 'description': 'Pixel is fill'}], 'description': 'Image or fill data'}, {'name': 'retrieval', 'length': 1, 'offset': 1, 'classes': [{'name': 'not_valid', 'value': 0, 'description': 'Pixel retrieval is not valid'}, {'name': 'valid', 'value': 1, 'description': 'Pixel retrieval is valid'}], 'description': 'Valid aerosol retrieval'}, {'name': 'water', 'length': 1, 'offset': 2, 'classes': [{'name': 'not_water', 'value': 0, 'description': 'Pixel is not water'}, {'name': 'water', 'value': 1, 'description': 'Pixel is water'}], 'description': 'Water mask'}, {'name': 'interpolated', 'length': 1, 'offset': 5, 'classes': [{'name': 'not_interpolated', 'value': 0, 'description': 'Pixel is not interpolated aerosol'}, {'name': 'interpolated', 'value': 1, 'description': 'Pixel is interpolated aerosol'}], 'description': 'Aerosol interpolation'}, {'name': 'level', 'length': 2, 'offset': 6, 'classes': [{'name': 'climatology', 'value': 0, 'description': 'No aerosol correction applied'}, {'name': 'low', 'value': 1, 'description': 'Low aerosol level'}, {'name': 'medium', 'value': 2, 'description': 'Medium aerosol level'}, {'name': 'high', 'value': 3, 'description': 'High aerosol level'}], 'description': 'Aerosol level'}]),\n",
" None,\n",
" list([{'name': 'ddv', 'length': 1, 'offset': 0, 'classes': [{'name': 'not_ddv', 'value': 0, 'description': 'Pixel has no DDV'}, {'name': 'ddv', 'value': 1, 'description': 'Pixel has DDV'}], 'description': 'Dense Dark Vegetation (DDV)'}, {'name': 'cloud', 'length': 1, 'offset': 1, 'classes': [{'name': 'not_cloud', 'value': 0, 'description': 'Pixel has no cloud'}, {'name': 'cloud', 'value': 1, 'description': 'Pixel has cloud'}], 'description': 'Cloud mask'}, {'name': 'cloud_shadow', 'length': 1, 'offset': 2, 'classes': [{'name': 'not_shadow', 'value': 0, 'description': 'Pixel has no cloud shadow'}, {'name': 'shadow', 'value': 1, 'description': 'Pixel has cloud shadow'}], 'description': 'Cloud shadow mask'}, {'name': 'cloud_adjacent', 'length': 1, 'offset': 3, 'classes': [{'name': 'not_adjacent', 'value': 0, 'description': 'Pixel is not adjacent to cloud'}, {'name': 'adjacent', 'value': 1, 'description': 'Pixel is adjacent to cloud'}], 'description': 'Cloud adjacency'}, {'name': 'snow', 'length': 1, 'offset': 4, 'classes': [{'name': 'not_snow', 'value': 0, 'description': 'Pixel is not snow'}, {'name': 'shadow', 'value': 1, 'description': 'Pixel is snow'}], 'description': 'Snow mask'}, {'name': 'water', 'length': 1, 'offset': 5, 'classes': [{'name': 'not_water', 'value': 0, 'description': 'Pixel is not water'}, {'name': 'water', 'value': 1, 'description': 'Pixel is water'}], 'description': 'Water mask'}]),\n",
" None], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>common_name</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None None None ... 'lwir' None None</div><input id='attrs-5f5462fd-6a16-4b03-a8e6-a4d5418ee8e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f5462fd-6a16-4b03-a8e6-a4d5418ee8e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6802e76-b46e-42ea-b0ac-aae987314625' class='xr-var-data-in' type='checkbox'><label for='data-e6802e76-b46e-42ea-b0ac-aae987314625' 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([None, None, None, None, None, None, None, None, None, None, None,\n",
" None, 'lwir11', None, None, 'coastal', None, None, None, 'lwir',\n",
" None, None], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>center_wavelength</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None None None ... 11.34 None None</div><input id='attrs-e9001e09-c880-4b8e-ac04-d95d79299b10' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9001e09-c880-4b8e-ac04-d95d79299b10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da59b34a-86f0-4662-9068-ae3eba7d41f4' class='xr-var-data-in' type='checkbox'><label for='data-da59b34a-86f0-4662-9068-ae3eba7d41f4' 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([None, None, None, None, None, None, None, None, None, None, None,\n",
" None, 10.9, None, None, 0.44, None, None, None, 11.34, None, None],\n",
" dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>full_width_half_max</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>None None None ... 2.05 None None</div><input id='attrs-28d373c9-746f-45ff-8106-682c4434e7c9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-28d373c9-746f-45ff-8106-682c4434e7c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-905f3c71-2787-4434-8636-59b98b18153f' class='xr-var-data-in' type='checkbox'><label for='data-905f3c71-2787-4434-8636-59b98b18153f' 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([None, None, None, None, None, None, None, None, None, None, None,\n",
" None, 0.59, None, None, 0.02, None, None, None, 2.05, None, None],\n",
" dtype=object)</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'>32610</div><input id='attrs-b2c42c1c-d6ca-4998-b4ac-2929b4dbc284' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2c42c1c-d6ca-4998-b4ac-2929b4dbc284' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9319ad55-f044-489d-9c49-65e74bc7bfc1' class='xr-var-data-in' type='checkbox'><label for='data-9319ad55-f044-489d-9c49-65e74bc7bfc1' 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(32610)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2b88dc49-a537-4277-8f9b-4cf542cafb6d' class='xr-section-summary-in' type='checkbox' ><label for='section-2b88dc49-a537-4277-8f9b-4cf542cafb6d' 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-17a83457-8503-4292-b8e2-ae9863bdd3c3' class='xr-index-data-in' type='checkbox'/><label for='index-17a83457-8503-4292-b8e2-ae9863bdd3c3' 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(['2020-12-04 19:02:11.194486', '2020-12-05 18:16:03.755599',\n",
" '2020-12-12 18:21:42.991249', '2020-12-13 18:56:00.096447',\n",
" '2020-12-20 19:02:09.878796', '2020-12-21 18:14:50.812768',\n",
" '2020-12-28 18:20:32.609164', '2020-12-29 18:55:56.738265'],\n",
" dtype='datetime64[ns]', name='time', 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-5559199f-470f-47c9-9cf1-b6de0b130577' class='xr-index-data-in' type='checkbox'/><label for='index-5559199f-470f-47c9-9cf1-b6de0b130577' 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(['qa', 'red', 'blue', 'drad', 'emis', 'emsd', 'trad', 'urad', 'atran',\n",
" 'cdist', 'green', 'nir08', 'lwir11', 'swir16', 'swir22', 'coastal',\n",
" 'qa_pixel', 'qa_radsat', 'qa_aerosol', 'lwir', 'cloud_qa',\n",
" 'atmos_opacity'],\n",
" dtype='object', name='band'))</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-61d2f8f8-3312-4b8b-8d57-5bec27b1ca70' class='xr-index-data-in' type='checkbox'/><label for='index-61d2f8f8-3312-4b8b-8d57-5bec27b1ca70' 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([333870.0, 333900.0, 333930.0, 333960.0, 333990.0, 334020.0, 334050.0,\n",
" 334080.0, 334110.0, 334140.0,\n",
" ...\n",
" 704730.0, 704760.0, 704790.0, 704820.0, 704850.0, 704880.0, 704910.0,\n",
" 704940.0, 704970.0, 705000.0],\n",
" dtype='float64', name='x', length=12372))</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-6ebe9aee-2905-4344-9e01-f33b47fd1f27' class='xr-index-data-in' type='checkbox'/><label for='index-6ebe9aee-2905-4344-9e01-f33b47fd1f27' 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([5374230.0, 5374200.0, 5374170.0, 5374140.0, 5374110.0, 5374080.0,\n",
" 5374050.0, 5374020.0, 5373990.0, 5373960.0,\n",
" ...\n",
" 5135370.0, 5135340.0, 5135310.0, 5135280.0, 5135250.0, 5135220.0,\n",
" 5135190.0, 5135160.0, 5135130.0, 5135100.0],\n",
" dtype='float64', name='y', length=7972))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1eb3d4f1-f620-425a-93ec-857a28aa8c59' class='xr-section-summary-in' type='checkbox' checked><label for='section-1eb3d4f1-f620-425a-93ec-857a28aa8c59' 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=32610, bounds=(333870.0, 5135070.0, 705030.0, 5374230.0), resolutions_xy=(30.0, 30.0))</dd><dt><span>crs :</span></dt><dd>epsg:32610</dd><dt><span>transform :</span></dt><dd>| 30.00, 0.00, 333870.00|\n",
"| 0.00,-30.00, 5374230.00|\n",
"| 0.00, 0.00, 1.00|</dd><dt><span>resolution :</span></dt><dd>30.0</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'stackstac-58124a3f2aeb9e86fe45c8d5489954e7' (time: 8,\n",
" band: 22,\n",
" y: 7972,\n",
" x: 12372)>\n",
"dask.array<fetch_raster_window, shape=(8, 22, 7972, 12372), dtype=float64, chunksize=(1, 1, 1024, 1024), chunktype=numpy.ndarray>\n",
"Coordinates: (12/31)\n",
" * time (time) datetime64[ns] 2020-12-04T19:02:11.19...\n",
" id (time) <U31 'LC08_L2SP_047027_20201204_02_T1...\n",
" * band (band) <U13 'qa' 'red' ... 'atmos_opacity'\n",
" * x (x) float64 3.339e+05 3.339e+05 ... 7.05e+05\n",
" * y (y) float64 5.374e+06 5.374e+06 ... 5.135e+06\n",
" landsat:wrs_type <U1 '2'\n",
" ... ...\n",
" title (band) object 'Surface Temperature Quality A...\n",
" classification:bitfields (band) object None None ... None\n",
" common_name (band) object None None None ... None None\n",
" center_wavelength (band) object None None None ... None None\n",
" full_width_half_max (band) object None None None ... 2.05 None None\n",
" epsg int64 32610\n",
"Attributes:\n",
" spec: RasterSpec(epsg=32610, bounds=(333870.0, 5135070.0, 705030.0...\n",
" crs: epsg:32610\n",
" transform: | 30.00, 0.00, 333870.00|\\n| 0.00,-30.00, 5374230.00|\\n| 0.0...\n",
" resolution: 30.0"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import stackstac\n",
"\n",
"ds = stackstac.stack(items)\n",
"ds"
]
},
{
"cell_type": "markdown",
"id": "fc96240e-edb9-4e6d-9c70-645d91813a7f",
"metadata": {},
"source": [
"### Searching on additional properties\n",
"\n",
"Previously, we searched for items by space and time. Because the Planetary Computer's STAC API supports the [query](https://github.com/radiantearth/stac-api-spec/blob/master/fragments/query/README.md) parameter, you can search on additional properties on the STAC item.\n",
"\n",
"For example, collections like `sentinel-2-l2a` and `landsat-c2-l2` both implement the [`eo` STAC extension](https://github.com/stac-extensions/eo) and include an `eo:cloud_cover` property. Use `query={\"eo:cloud_cover\": {\"lt\": 20}}` to return only items that are less than 20% cloudy."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "18c2f286-654a-479a-9ae0-0f819250b575",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/srv/conda/envs/notebook/lib/python3.11/site-packages/pystac_client/item_search.py:841: FutureWarning: get_all_items() is deprecated, use item_collection() instead.\n",
" warnings.warn(\n"
]
}
],
"source": [
"time_range = \"2020-12-01/2020-12-31\"\n",
"bbox = [-122.2751, 47.5469, -121.9613, 47.7458]\n",
"\n",
"search = catalog.search(\n",
" collections=[\"sentinel-2-l2a\"],\n",
" bbox=bbox,\n",
" datetime=time_range,\n",
" query={\"eo:cloud_cover\": {\"lt\": 20}},\n",
")\n",
"items = search.get_all_items()"
]
},
{
"cell_type": "markdown",
"id": "fda18a4d-7421-420b-a918-c45ce43833a9",
"metadata": {},
"source": [
"Other common uses of the `query` parameter is to filter a collection down to items of a specific type, For example, the [GOES-CMI](https://planetarycomputer.microsoft.com/dataset/goes-cmi) collection includes images from various when the satellite is in various modes, which produces images of either the Full Disk of the earth, the continental United States, or a mesoscale. You can use `goes:image-type` to filter down to just the ones you want."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "d2db005e-9732-43e2-89c1-3133a6fda2d1",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"search = catalog.search(\n",
" collections=[\"goes-cmi\"],\n",
" bbox=[-67.2729, 25.6000, -61.7999, 27.5423],\n",
" datetime=[\"2018-09-11T13:00:00Z\", \"2018-09-11T15:40:00Z\"],\n",
" query={\"goes:image-type\": {\"eq\": \"MESOSCALE\"}},\n",
")"
]
},
{
"cell_type": "markdown",
"id": "10cb5d51-7d01-4d8b-ad14-1c3df6cea258",
"metadata": {},
"source": [
"### Analyzing STAC Metadata\n",
"\n",
"STAC items are proper GeoJSON Features, and so can be treated as a kind of data on their own."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "0283b780-d1c8-4715-bcc0-0e533110020f",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/quickstarts-reading-stac.ipynb/15.png\"/>"
],
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import contextily\n",
"\n",
"search = catalog.search(\n",
" collections=[\"sentinel-2-l2a\"],\n",
" bbox=[-124.2751, 45.5469, -110.9613, 47.7458],\n",
" datetime=\"2020-12-26/2020-12-31\",\n",
")\n",
"items = search.item_collection()\n",
"\n",
"df = geopandas.GeoDataFrame.from_features(items.to_dict(), crs=\"epsg:4326\")\n",
"\n",
"ax = df[[\"geometry\", \"datetime\", \"s2:mgrs_tile\", \"eo:cloud_cover\"]].plot(\n",
" facecolor=\"none\", figsize=(12, 6)\n",
")\n",
"contextily.add_basemap(\n",
" ax, crs=df.crs.to_string(), source=contextily.providers.Esri.NatGeoWorldMap\n",
");"
]
},
{
"cell_type": "markdown",
"id": "debaa186-f987-49c0-a476-98835207fd1f",
"metadata": {},
"source": [
"Or we can plot cloudiness of a region over time."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "548b344f-4c5e-41a7-bc27-9fe5d3bf3845",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/srv/conda/envs/notebook/lib/python3.11/site-packages/pystac_client/item_search.py:841: FutureWarning: get_all_items() is deprecated, use item_collection() instead.\n",
" warnings.warn(\n"
]
},
{
"data": {
"text/html": [
"<img src=\"https://ai4edatasetspublicassets.blob.core.windows.net/assets/notebook-output/quickstarts-reading-stac.ipynb/16.png\"/>"
],
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"\n",
"search = catalog.search(\n",
" collections=[\"sentinel-2-l2a\"],\n",
" bbox=[-124.2751, 45.5469, -123.9613, 45.7458],\n",
" datetime=\"2020-01-01/2020-12-31\",\n",
")\n",
"items = search.get_all_items()\n",
"df = geopandas.GeoDataFrame.from_features(items.to_dict())\n",
"df[\"datetime\"] = pd.to_datetime(df[\"datetime\"])\n",
"\n",
"ts = df.set_index(\"datetime\").sort_index()[\"eo:cloud_cover\"].rolling(7).mean()\n",
"ts.plot(title=\"eo:cloud-cover (7-scene rolling average)\");"
]
},
{
"cell_type": "markdown",
"id": "13669344-a6c2-4684-ba78-69b7907e37e1",
"metadata": {},
"source": [
"### Working with STAC Catalogs and Collections\n",
"\n",
"Our `catalog` is a [STAC Catalog](https://github.com/radiantearth/stac-spec/blob/master/catalog-spec/catalog-spec.md) that we can crawl or search. The Catalog contains [STAC Collections](https://github.com/radiantearth/stac-spec/blob/master/collection-spec/collection-spec.md) for each dataset we have indexed (which is not the yet the entirety of data hosted by the Planetary Computer).\n",
"\n",
"Collections have information about the [STAC Items](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md) they contain. For instance, here we look at the [Bands](https://github.com/stac-extensions/eo#band-object) available for [Landsat 8 Collection 2 Level 2](https://planetarycomputer.microsoft.com/dataset/landsat-c2-l2) data:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "8da11d26-3658-4b9e-b790-f84a2aded3e8",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>common_name</th>\n",
" <th>description</th>\n",
" <th>center_wavelength</th>\n",
" <th>full_width_half_max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>TM_B1</td>\n",
" <td>blue</td>\n",
" <td>Visible blue (Thematic Mapper)</td>\n",
" <td>0.49</td>\n",
" <td>0.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>TM_B2</td>\n",
" <td>green</td>\n",
" <td>Visible green (Thematic Mapper)</td>\n",
" <td>0.56</td>\n",
" <td>0.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>TM_B3</td>\n",
" <td>red</td>\n",
" <td>Visible red (Thematic Mapper)</td>\n",
" <td>0.66</td>\n",
" <td>0.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>TM_B4</td>\n",
" <td>nir08</td>\n",
" <td>Near infrared (Thematic Mapper)</td>\n",
" <td>0.83</td>\n",
" <td>0.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>TM_B5</td>\n",
" <td>swir16</td>\n",
" <td>Short-wave infrared (Thematic Mapper)</td>\n",
" <td>1.65</td>\n",
" <td>0.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>TM_B6</td>\n",
" <td>lwir</td>\n",
" <td>Long-wave infrared (Thematic Mapper)</td>\n",
" <td>11.45</td>\n",
" <td>2.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>TM_B7</td>\n",
" <td>swir22</td>\n",
" <td>Short-wave infrared (Thematic Mapper)</td>\n",
" <td>2.22</td>\n",
" <td>0.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>ETM_B1</td>\n",
" <td>blue</td>\n",
" <td>Visible blue (Enhanced Thematic Mapper Plus)</td>\n",
" <td>0.48</td>\n",
" <td>0.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>ETM_B2</td>\n",
" <td>green</td>\n",
" <td>Visible green (Enhanced Thematic Mapper Plus)</td>\n",
" <td>0.56</td>\n",
" <td>0.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>ETM_B3</td>\n",
" <td>red</td>\n",
" <td>Visible red (Enhanced Thematic Mapper Plus)</td>\n",
" <td>0.66</td>\n",
" <td>0.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>ETM_B4</td>\n",
" <td>nir08</td>\n",
" <td>Near infrared (Enhanced Thematic Mapper Plus)</td>\n",
" <td>0.84</td>\n",
" <td>0.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>ETM_B5</td>\n",
" <td>swir16</td>\n",
" <td>Short-wave infrared (Enhanced Thematic Mapper ...</td>\n",
" <td>1.65</td>\n",
" <td>0.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>ETM_B6</td>\n",
" <td>lwir</td>\n",
" <td>Long-wave infrared (Enhanced Thematic Mapper P...</td>\n",
" <td>11.34</td>\n",
" <td>2.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>ETM_B7</td>\n",
" <td>swir22</td>\n",
" <td>Short-wave infrared (Enhanced Thematic Mapper ...</td>\n",
" <td>2.20</td>\n",
" <td>0.28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>OLI_B1</td>\n",
" <td>coastal</td>\n",
" <td>Coastal/Aerosol (Operational Land Imager)</td>\n",
" <td>0.44</td>\n",
" <td>0.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>OLI_B2</td>\n",
" <td>blue</td>\n",
" <td>Visible blue (Operational Land Imager)</td>\n",
" <td>0.48</td>\n",
" <td>0.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>OLI_B3</td>\n",
" <td>green</td>\n",
" <td>Visible green (Operational Land Imager)</td>\n",
" <td>0.56</td>\n",
" <td>0.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>OLI_B4</td>\n",
" <td>red</td>\n",
" <td>Visible red (Operational Land Imager)</td>\n",
" <td>0.65</td>\n",
" <td>0.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>OLI_B5</td>\n",
" <td>nir08</td>\n",
" <td>Near infrared (Operational Land Imager)</td>\n",
" <td>0.87</td>\n",
" <td>0.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>OLI_B6</td>\n",
" <td>swir16</td>\n",
" <td>Short-wave infrared (Operational Land Imager)</td>\n",
" <td>1.61</td>\n",
" <td>0.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>OLI_B7</td>\n",
" <td>swir22</td>\n",
" <td>Short-wave infrared (Operational Land Imager)</td>\n",
" <td>2.20</td>\n",
" <td>0.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>TIRS_B10</td>\n",
" <td>lwir11</td>\n",
" <td>Long-wave infrared (Thermal Infrared Sensor)</td>\n",
" <td>10.90</td>\n",
" <td>0.59</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name common_name description \\\n",
"0 TM_B1 blue Visible blue (Thematic Mapper) \n",
"1 TM_B2 green Visible green (Thematic Mapper) \n",
"2 TM_B3 red Visible red (Thematic Mapper) \n",
"3 TM_B4 nir08 Near infrared (Thematic Mapper) \n",
"4 TM_B5 swir16 Short-wave infrared (Thematic Mapper) \n",
"5 TM_B6 lwir Long-wave infrared (Thematic Mapper) \n",
"6 TM_B7 swir22 Short-wave infrared (Thematic Mapper) \n",
"7 ETM_B1 blue Visible blue (Enhanced Thematic Mapper Plus) \n",
"8 ETM_B2 green Visible green (Enhanced Thematic Mapper Plus) \n",
"9 ETM_B3 red Visible red (Enhanced Thematic Mapper Plus) \n",
"10 ETM_B4 nir08 Near infrared (Enhanced Thematic Mapper Plus) \n",
"11 ETM_B5 swir16 Short-wave infrared (Enhanced Thematic Mapper ... \n",
"12 ETM_B6 lwir Long-wave infrared (Enhanced Thematic Mapper P... \n",
"13 ETM_B7 swir22 Short-wave infrared (Enhanced Thematic Mapper ... \n",
"14 OLI_B1 coastal Coastal/Aerosol (Operational Land Imager) \n",
"15 OLI_B2 blue Visible blue (Operational Land Imager) \n",
"16 OLI_B3 green Visible green (Operational Land Imager) \n",
"17 OLI_B4 red Visible red (Operational Land Imager) \n",
"18 OLI_B5 nir08 Near infrared (Operational Land Imager) \n",
"19 OLI_B6 swir16 Short-wave infrared (Operational Land Imager) \n",
"20 OLI_B7 swir22 Short-wave infrared (Operational Land Imager) \n",
"21 TIRS_B10 lwir11 Long-wave infrared (Thermal Infrared Sensor) \n",
"\n",
" center_wavelength full_width_half_max \n",
"0 0.49 0.07 \n",
"1 0.56 0.08 \n",
"2 0.66 0.06 \n",
"3 0.83 0.14 \n",
"4 1.65 0.20 \n",
"5 11.45 2.10 \n",
"6 2.22 0.27 \n",
"7 0.48 0.07 \n",
"8 0.56 0.08 \n",
"9 0.66 0.06 \n",
"10 0.84 0.13 \n",
"11 1.65 0.20 \n",
"12 11.34 2.05 \n",
"13 2.20 0.28 \n",
"14 0.44 0.02 \n",
"15 0.48 0.06 \n",
"16 0.56 0.06 \n",
"17 0.65 0.04 \n",
"18 0.87 0.03 \n",
"19 1.61 0.09 \n",
"20 2.20 0.19 \n",
"21 10.90 0.59 "
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"\n",
"landsat = catalog.get_collection(\"landsat-c2-l2\")\n",
"\n",
"pd.DataFrame(landsat.summaries.get_list(\"eo:bands\"))"
]
},
{
"cell_type": "markdown",
"id": "4ae651c0",
"metadata": {},
"source": [
"We can see what [Assets](https://github.com/radiantearth/stac-spec/blob/master/item-spec/item-spec.md#asset-object) are available on our item with:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "4bb2b985-f18d-4161-9331-b4b259814f34",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>title</th>\n",
" <th>description</th>\n",
" <th>gsd</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>qa</th>\n",
" <td>Surface Temperature Quality Assessment Band</td>\n",
" <td>Collection 2 Level-2 Quality Assessment Band (...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ang</th>\n",
" <td>Angle Coefficients File</td>\n",
" <td>Collection 2 Level-1 Angle Coefficients File</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>red</th>\n",
" <td>Red Band</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>blue</th>\n",
" <td>Blue Band</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>drad</th>\n",
" <td>Downwelled Radiance Band</td>\n",
" <td>Collection 2 Level-2 Downwelled Radiance Band ...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>emis</th>\n",
" <td>Emissivity Band</td>\n",
" <td>Collection 2 Level-2 Emissivity Band (ST_EMIS)...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>emsd</th>\n",
" <td>Emissivity Standard Deviation Band</td>\n",
" <td>Collection 2 Level-2 Emissivity Standard Devia...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lwir</th>\n",
" <td>Surface Temperature Band</td>\n",
" <td>Collection 2 Level-2 Thermal Infrared Band (ST...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>trad</th>\n",
" <td>Thermal Radiance Band</td>\n",
" <td>Collection 2 Level-2 Thermal Radiance Band (ST...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>urad</th>\n",
" <td>Upwelled Radiance Band</td>\n",
" <td>Collection 2 Level-2 Upwelled Radiance Band (S...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>atran</th>\n",
" <td>Atmospheric Transmittance Band</td>\n",
" <td>Collection 2 Level-2 Atmospheric Transmittance...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cdist</th>\n",
" <td>Cloud Distance Band</td>\n",
" <td>Collection 2 Level-2 Cloud Distance Band (ST_C...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>green</th>\n",
" <td>Green Band</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>nir08</th>\n",
" <td>Near Infrared Band 0.8</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>lwir11</th>\n",
" <td>Surface Temperature Band</td>\n",
" <td>Collection 2 Level-2 Thermal Infrared Band (ST...</td>\n",
" <td>100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>swir16</th>\n",
" <td>Short-wave Infrared Band 1.6</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>swir22</th>\n",
" <td>Short-wave Infrared Band 2.2</td>\n",
" <td>Collection 2 Level-2 Short-wave Infrared Band ...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>coastal</th>\n",
" <td>Coastal/Aerosol Band</td>\n",
" <td>Collection 2 Level-2 Coastal/Aerosol Band (SR_...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mtl.txt</th>\n",
" <td>Product Metadata File (txt)</td>\n",
" <td>Collection 2 Level-2 Product Metadata File (txt)</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mtl.xml</th>\n",
" <td>Product Metadata File (xml)</td>\n",
" <td>Collection 2 Level-2 Product Metadata File (xml)</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>cloud_qa</th>\n",
" <td>Cloud Quality Assessment Band</td>\n",
" <td>Collection 2 Level-2 Cloud Quality Assessment ...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mtl.json</th>\n",
" <td>Product Metadata File (json)</td>\n",
" <td>Collection 2 Level-2 Product Metadata File (json)</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>qa_pixel</th>\n",
" <td>Pixel Quality Assessment Band</td>\n",
" <td>Collection 2 Level-1 Pixel Quality Assessment ...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>atmos_opacity</th>\n",
" <td>Atmospheric Opacity Band</td>\n",
" <td>Collection 2 Level-2 Atmospheric Opacity Band ...</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" title \\\n",
"qa Surface Temperature Quality Assessment Band \n",
"ang Angle Coefficients File \n",
"red Red Band \n",
"blue Blue Band \n",
"drad Downwelled Radiance Band \n",
"emis Emissivity Band \n",
"emsd Emissivity Standard Deviation Band \n",
"lwir Surface Temperature Band \n",
"trad Thermal Radiance Band \n",
"urad Upwelled Radiance Band \n",
"atran Atmospheric Transmittance Band \n",
"cdist Cloud Distance Band \n",
"green Green Band \n",
"nir08 Near Infrared Band 0.8 \n",
"lwir11 Surface Temperature Band \n",
"swir16 Short-wave Infrared Band 1.6 \n",
"swir22 Short-wave Infrared Band 2.2 \n",
"coastal Coastal/Aerosol Band \n",
"mtl.txt Product Metadata File (txt) \n",
"mtl.xml Product Metadata File (xml) \n",
"cloud_qa Cloud Quality Assessment Band \n",
"mtl.json Product Metadata File (json) \n",
"qa_pixel Pixel Quality Assessment Band \n",
"qa_radsat NaN \n",
"qa_aerosol Aerosol Quality Assessment Band \n",
"atmos_opacity Atmospheric Opacity Band \n",
"\n",
" description gsd \n",
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"red NaN NaN \n",
"blue NaN NaN \n",
"drad Collection 2 Level-2 Downwelled Radiance Band ... NaN \n",
"emis Collection 2 Level-2 Emissivity Band (ST_EMIS)... NaN \n",
"emsd Collection 2 Level-2 Emissivity Standard Devia... NaN \n",
"lwir Collection 2 Level-2 Thermal Infrared Band (ST... NaN \n",
"trad Collection 2 Level-2 Thermal Radiance Band (ST... NaN \n",
"urad Collection 2 Level-2 Upwelled Radiance Band (S... NaN \n",
"atran Collection 2 Level-2 Atmospheric Transmittance... NaN \n",
"cdist Collection 2 Level-2 Cloud Distance Band (ST_C... NaN \n",
"green NaN NaN \n",
"nir08 NaN NaN \n",
"lwir11 Collection 2 Level-2 Thermal Infrared Band (ST... 100.0 \n",
"swir16 NaN NaN \n",
"swir22 Collection 2 Level-2 Short-wave Infrared Band ... NaN \n",
"coastal Collection 2 Level-2 Coastal/Aerosol Band (SR_... NaN \n",
"mtl.txt Collection 2 Level-2 Product Metadata File (txt) NaN \n",
"mtl.xml Collection 2 Level-2 Product Metadata File (xml) NaN \n",
"cloud_qa Collection 2 Level-2 Cloud Quality Assessment ... NaN \n",
"mtl.json Collection 2 Level-2 Product Metadata File (json) NaN \n",
"qa_pixel Collection 2 Level-1 Pixel Quality Assessment ... NaN \n",
"qa_radsat NaN NaN \n",
"qa_aerosol Collection 2 Level-2 Aerosol Quality Assessmen... NaN \n",
"atmos_opacity Collection 2 Level-2 Atmospheric Opacity Band ... NaN "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame.from_dict(landsat.extra_fields[\"item_assets\"], orient=\"index\")[\n",
" [\"title\", \"description\", \"gsd\"]\n",
"]"
]
},
{
"cell_type": "markdown",
"id": "d951c06c-8d3a-47e7-8067-47fe4e9d4ccf",
"metadata": {},
"source": [
"Some collections, like [Daymet](https://planetarycomputer.microsoft.com/dataset/daymet-daily-na) include collection-level assets. You can use the `.assets` property to access those assets."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "c2ffb4ee-2714-4bf0-8445-5d90fcc2520c",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<CollectionClient id=daymet-daily-na>\n"
]
}
],
"source": [
"collection = catalog.get_collection(\"daymet-daily-na\")\n",
"print(collection)"
]
},
{
"cell_type": "markdown",
"id": "520e7e3f-b189-4ce3-b5f1-11211e474d1b",
"metadata": {},
"source": [
"Just like assets on items, these assets include links to data in Azure Blob Storage."
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "08b95491-3994-4cc5-8b24-ba113874835f",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<Asset href=abfs://daymet-zarr/daily/na.zarr>\n"
]
}
],
"source": [
"asset = collection.assets[\"zarr-abfs\"]\n",
"print(asset)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "58b7f436-d739-40d8-93bc-631a2295709a",
"metadata": {
"tags": []
},
"outputs": [
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".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'><xarray.Dataset>\n",
"Dimensions: (time: 14965, y: 8075, x: 7814, nv: 2)\n",
"Coordinates:\n",
" lat (y, x) float32 dask.array<chunksize=(284, 584), meta=np.ndarray>\n",
" lon (y, x) float32 dask.array<chunksize=(284, 584), meta=np.ndarray>\n",
" * time (time) datetime64[ns] 1980-01-01T12:00:00 ... 20...\n",
" * x (x) float32 -4.56e+06 -4.559e+06 ... 3.253e+06\n",
" * y (y) float32 4.984e+06 4.983e+06 ... -3.09e+06\n",
"Dimensions without coordinates: nv\n",
"Data variables:\n",
" dayl (time, y, x) float32 dask.array<chunksize=(365, 284, 584), meta=np.ndarray>\n",
" lambert_conformal_conic int16 ...\n",
" prcp (time, y, x) float32 dask.array<chunksize=(365, 284, 584), meta=np.ndarray>\n",
" srad (time, y, x) float32 dask.array<chunksize=(365, 284, 584), meta=np.ndarray>\n",
" swe (time, y, x) float32 dask.array<chunksize=(365, 284, 584), meta=np.ndarray>\n",
" time_bnds (time, nv) datetime64[ns] dask.array<chunksize=(365, 2), meta=np.ndarray>\n",
" tmax (time, y, x) float32 dask.array<chunksize=(365, 284, 584), meta=np.ndarray>\n",
" tmin (time, y, x) float32 dask.array<chunksize=(365, 284, 584), meta=np.ndarray>\n",
" vp (time, y, x) float32 dask.array<chunksize=(365, 284, 584), meta=np.ndarray>\n",
" yearday (time) int16 dask.array<chunksize=(365,), meta=np.ndarray>\n",
"Attributes:\n",
" Conventions: CF-1.6\n",
" Version_data: Daymet Data Version 4.0\n",
" Version_software: Daymet Software Version 4.0\n",
" citation: Please see http://daymet.ornl.gov/ for current Daymet ...\n",
" references: Please see http://daymet.ornl.gov/ for current informa...\n",
" source: Daymet Software Version 4.0\n",
" start_year: 1980</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d03e5485-a17c-4be3-9122-5551c91ea5c8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d03e5485-a17c-4be3-9122-5551c91ea5c8' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 14965</li><li><span class='xr-has-index'>y</span>: 8075</li><li><span class='xr-has-index'>x</span>: 7814</li><li><span>nv</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-04445400-8530-4c7a-946f-39b19fcf62b8' class='xr-section-summary-in' type='checkbox' checked><label for='section-04445400-8530-4c7a-946f-39b19fcf62b8' class='xr-section-summary' >Coordinates: <span>(5)</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>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(284, 584), meta=np.ndarray></div><input id='attrs-c41c2a26-9007-4716-b704-6a7dbebf43c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c41c2a26-9007-4716-b704-6a7dbebf43c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcf1fd74-102d-45c1-b804-b371ac2782f2' class='xr-var-data-in' type='checkbox'><label for='data-fcf1fd74-102d-45c1-b804-b371ac2782f2' 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'><dt><span>long_name :</span></dt><dd>latitude coordinate</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-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> 240.70 MiB </td>\n",
" <td> 647.88 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (8075, 7814) </td>\n",
" <td> (284, 584) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 406 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"166\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"116\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"4\" x2=\"116\" y2=\"4\" />\n",
" <line x1=\"0\" y1=\"12\" x2=\"116\" y2=\"12\" />\n",
" <line x1=\"0\" y1=\"16\" x2=\"116\" y2=\"16\" />\n",
" <line x1=\"0\" y1=\"25\" x2=\"116\" y2=\"25\" />\n",
" <line x1=\"0\" y1=\"29\" x2=\"116\" y2=\"29\" />\n",
" <line x1=\"0\" y1=\"37\" x2=\"116\" y2=\"37\" />\n",
" <line x1=\"0\" y1=\"42\" x2=\"116\" y2=\"42\" />\n",
" <line x1=\"0\" y1=\"50\" x2=\"116\" y2=\"50\" />\n",
" <line x1=\"0\" y1=\"54\" x2=\"116\" y2=\"54\" />\n",
" <line x1=\"0\" y1=\"63\" x2=\"116\" y2=\"63\" />\n",
" <line x1=\"0\" y1=\"67\" x2=\"116\" y2=\"67\" />\n",
" <line x1=\"0\" y1=\"75\" x2=\"116\" y2=\"75\" />\n",
" <line x1=\"0\" y1=\"80\" x2=\"116\" y2=\"80\" />\n",
" <line x1=\"0\" y1=\"88\" x2=\"116\" y2=\"88\" />\n",
" <line x1=\"0\" y1=\"92\" x2=\"116\" y2=\"92\" />\n",
" <line x1=\"0\" y1=\"101\" x2=\"116\" y2=\"101\" />\n",
" <line x1=\"0\" y1=\"105\" x2=\"116\" y2=\"105\" />\n",
" <line x1=\"0\" y1=\"113\" x2=\"116\" y2=\"113\" />\n",
" <line x1=\"0\" y1=\"120\" x2=\"116\" y2=\"120\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
" <line x1=\"8\" y1=\"0\" x2=\"8\" y2=\"120\" />\n",
" <line x1=\"17\" y1=\"0\" x2=\"17\" y2=\"120\" />\n",
" <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"120\" />\n",
" <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"120\" />\n",
" <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"120\" />\n",
" <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"120\" />\n",
" <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
" <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"120\" />\n",
" <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"120\" />\n",
" <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"120\" />\n",
" <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"120\" />\n",
" <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"120\" />\n",
" <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"120\" />\n",
" <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"120\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.0,0.0 116.12136222910216,0.0 116.12136222910216,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"58.060681\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7814</text>\n",
" <text x=\"136.121362\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,136.121362,60.000000)\">8075</text>\n",
"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(284, 584), meta=np.ndarray></div><input id='attrs-7c0f7816-cc94-457c-9004-00989ed576f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c0f7816-cc94-457c-9004-00989ed576f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d895bab-8359-4d7a-833e-7498d69e4bbf' class='xr-var-data-in' type='checkbox'><label for='data-2d895bab-8359-4d7a-833e-7498d69e4bbf' 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'><dt><span>long_name :</span></dt><dd>longitude coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-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> 240.70 MiB </td>\n",
" <td> 647.88 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (8075, 7814) </td>\n",
" <td> (284, 584) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 406 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"166\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"116\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"4\" x2=\"116\" y2=\"4\" />\n",
" <line x1=\"0\" y1=\"12\" x2=\"116\" y2=\"12\" />\n",
" <line x1=\"0\" y1=\"16\" x2=\"116\" y2=\"16\" />\n",
" <line x1=\"0\" y1=\"25\" x2=\"116\" y2=\"25\" />\n",
" <line x1=\"0\" y1=\"29\" x2=\"116\" y2=\"29\" />\n",
" <line x1=\"0\" y1=\"37\" x2=\"116\" y2=\"37\" />\n",
" <line x1=\"0\" y1=\"42\" x2=\"116\" y2=\"42\" />\n",
" <line x1=\"0\" y1=\"50\" x2=\"116\" y2=\"50\" />\n",
" <line x1=\"0\" y1=\"54\" x2=\"116\" y2=\"54\" />\n",
" <line x1=\"0\" y1=\"63\" x2=\"116\" y2=\"63\" />\n",
" <line x1=\"0\" y1=\"67\" x2=\"116\" y2=\"67\" />\n",
" <line x1=\"0\" y1=\"75\" x2=\"116\" y2=\"75\" />\n",
" <line x1=\"0\" y1=\"80\" x2=\"116\" y2=\"80\" />\n",
" <line x1=\"0\" y1=\"88\" x2=\"116\" y2=\"88\" />\n",
" <line x1=\"0\" y1=\"92\" x2=\"116\" y2=\"92\" />\n",
" <line x1=\"0\" y1=\"101\" x2=\"116\" y2=\"101\" />\n",
" <line x1=\"0\" y1=\"105\" x2=\"116\" y2=\"105\" />\n",
" <line x1=\"0\" y1=\"113\" x2=\"116\" y2=\"113\" />\n",
" <line x1=\"0\" y1=\"120\" x2=\"116\" y2=\"120\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
" <line x1=\"8\" y1=\"0\" x2=\"8\" y2=\"120\" />\n",
" <line x1=\"17\" y1=\"0\" x2=\"17\" y2=\"120\" />\n",
" <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"120\" />\n",
" <line x1=\"34\" y1=\"0\" x2=\"34\" y2=\"120\" />\n",
" <line x1=\"43\" y1=\"0\" x2=\"43\" y2=\"120\" />\n",
" <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"120\" />\n",
" <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" />\n",
" <line x1=\"69\" y1=\"0\" x2=\"69\" y2=\"120\" />\n",
" <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"120\" />\n",
" <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"120\" />\n",
" <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"120\" />\n",
" <line x1=\"104\" y1=\"0\" x2=\"104\" y2=\"120\" />\n",
" <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"120\" />\n",
" <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"120\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.0,0.0 116.12136222910216,0.0 116.12136222910216,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"58.060681\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >7814</text>\n",
" <text x=\"136.121362\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,136.121362,60.000000)\">8075</text>\n",
"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li><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'>1980-01-01T12:00:00 ... 2020-12-...</div><input id='attrs-be6df066-c182-413e-ba89-817718218336' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be6df066-c182-413e-ba89-817718218336' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7351acaa-a600-4484-8278-f44fa1a55168' class='xr-var-data-in' type='checkbox'><label for='data-7351acaa-a600-4484-8278-f44fa1a55168' 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'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>24-hour day based on local time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(['1980-01-01T12:00:00.000000000', '1980-01-02T12:00:00.000000000',\n",
" '1980-01-03T12:00:00.000000000', ..., '2020-12-28T12:00:00.000000000',\n",
" '2020-12-29T12:00:00.000000000', '2020-12-30T12:00:00.000000000'],\n",
" dtype='datetime64[ns]')</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'>float32</div><div class='xr-var-preview xr-preview'>-4.56e+06 -4.559e+06 ... 3.253e+06</div><input id='attrs-7cac9fb9-a843-4790-93d0-8525457a596c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cac9fb9-a843-4790-93d0-8525457a596c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1a706dd-b363-45a5-a239-a90e8b2f53b6' class='xr-var-data-in' type='checkbox'><label for='data-c1a706dd-b363-45a5-a239-a90e8b2f53b6' 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'><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-4560250., -4559250., -4558250., ..., 3250750., 3251750., 3252750.],\n",
" dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>4.984e+06 4.983e+06 ... -3.09e+06</div><input id='attrs-c583269f-fae2-435e-9476-af13703c53c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c583269f-fae2-435e-9476-af13703c53c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0295b8a1-4101-44ae-b195-4f2dbef324f5' class='xr-var-data-in' type='checkbox'><label for='data-0295b8a1-4101-44ae-b195-4f2dbef324f5' 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'><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([ 4984000., 4983000., 4982000., ..., -3088000., -3089000., -3090000.],\n",
" dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7318d30d-36e6-4048-9123-753b0c93b684' class='xr-section-summary-in' type='checkbox' checked><label for='section-7318d30d-36e6-4048-9123-753b0c93b684' class='xr-section-summary' >Data variables: <span>(10)</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>dayl</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(365, 284, 584), meta=np.ndarray></div><input id='attrs-4ee2c729-fe30-4ba6-917b-a2eede836e61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ee2c729-fe30-4ba6-917b-a2eede836e61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d430170a-830d-411e-a368-5ccd52fbfad0' class='xr-var-data-in' type='checkbox'><label for='data-d430170a-830d-411e-a368-5ccd52fbfad0' 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'><dt><span>cell_methods :</span></dt><dd>area: mean</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>long_name :</span></dt><dd>daylength</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-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> 3.44 TiB </td>\n",
" <td> 230.93 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (14965, 8075, 7814) </td>\n",
" <td> (365, 284, 584) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 16646 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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" </tbody>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lambert_conformal_conic</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-72c69296-6d2c-49ff-80ed-096200d2d8ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72c69296-6d2c-49ff-80ed-096200d2d8ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-085b7b6c-2b8f-4081-a94a-b5b9fe0c936f' class='xr-var-data-in' type='checkbox'><label for='data-085b7b6c-2b8f-4081-a94a-b5b9fe0c936f' 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'><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>grid_mapping_name :</span></dt><dd>lambert_conformal_conic</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>42.5</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-100.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>standard_parallel :</span></dt><dd>[25.0, 60.0]</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int16]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prcp</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(365, 284, 584), meta=np.ndarray></div><input id='attrs-71dea9fb-aa9b-4c13-8e50-e9c49ffd36ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71dea9fb-aa9b-4c13-8e50-e9c49ffd36ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eb00352-7adc-421f-b037-2efccbe84430' class='xr-var-data-in' type='checkbox'><label for='data-5eb00352-7adc-421f-b037-2efccbe84430' 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'><dt><span>cell_methods :</span></dt><dd>area: mean time: sum</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>long_name :</span></dt><dd>daily total precipitation</dd><dt><span>units :</span></dt><dd>mm/day</dd></dl></div><div class='xr-var-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> 3.44 TiB </td>\n",
" <td> 230.93 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (14965, 8075, 7814) </td>\n",
" <td> (365, 284, 584) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 16646 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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" \n",
" <tr>\n",
" <th> Bytes </th>\n",
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" \n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nv)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(365, 2), meta=np.ndarray></div><input id='attrs-1ff05bc7-131d-44e2-9dd5-240678cf2f4c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1ff05bc7-131d-44e2-9dd5-240678cf2f4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-037d029c-9f15-4115-8eab-86b9ac65b41f' class='xr-var-data-in' type='checkbox'><label for='data-037d029c-9f15-4115-8eab-86b9ac65b41f' 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'><table>\n",
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" <th> Chunk </th>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tmax</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(365, 284, 584), meta=np.ndarray></div><input id='attrs-197e75d0-2b35-4a72-acd9-784052f325ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-197e75d0-2b35-4a72-acd9-784052f325ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae5d5454-3946-4dae-ad80-c2a578bfa2a3' class='xr-var-data-in' type='checkbox'><label for='data-ae5d5454-3946-4dae-ad80-c2a578bfa2a3' 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'><dt><span>cell_methods :</span></dt><dd>area: mean time: maximum</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>long_name :</span></dt><dd>daily maximum temperature</dd><dt><span>units :</span></dt><dd>degrees C</dd></dl></div><div class='xr-var-data'><table>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tmin</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(365, 284, 584), meta=np.ndarray></div><input id='attrs-29b855c2-e1b1-4a84-b742-a68b29ffe8f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29b855c2-e1b1-4a84-b742-a68b29ffe8f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73cbebf6-bebf-4b7a-90fc-9e9fa0cdc08a' class='xr-var-data-in' type='checkbox'><label for='data-73cbebf6-bebf-4b7a-90fc-9e9fa0cdc08a' 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'><dt><span>cell_methods :</span></dt><dd>area: mean time: minimum</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>long_name :</span></dt><dd>daily minimum temperature</dd><dt><span>units :</span></dt><dd>degrees C</dd></dl></div><div class='xr-var-data'><table>\n",
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" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 3.44 TiB </td>\n",
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" \n",
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" <td> (14965, 8075, 7814) </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vp</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(365, 284, 584), meta=np.ndarray></div><input id='attrs-e28002f8-472e-44cc-8d9e-9da235cd544e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e28002f8-472e-44cc-8d9e-9da235cd544e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28b73d99-2cdd-4dde-857d-aa408b377cbd' class='xr-var-data-in' type='checkbox'><label for='data-28b73d99-2cdd-4dde-857d-aa408b377cbd' 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'><dt><span>cell_methods :</span></dt><dd>area: mean time: mean</dd><dt><span>grid_mapping :</span></dt><dd>lambert_conformal_conic</dd><dt><span>long_name :</span></dt><dd>daily average vapor pressure</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-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> 3.44 TiB </td>\n",
" <td> 230.93 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (14965, 8075, 7814) </td>\n",
" <td> (365, 284, 584) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 16646 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>yearday</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(365,), meta=np.ndarray></div><input id='attrs-a1a1060e-f016-4d20-953a-04fe773f8ad6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1a1060e-f016-4d20-953a-04fe773f8ad6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07a2e937-ca03-42c5-b45d-67f7d8c63410' class='xr-var-data-in' type='checkbox'><label for='data-07a2e937-ca03-42c5-b45d-67f7d8c63410' 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'><dt><span>long_name :</span></dt><dd>day of year (DOY) starting with day 1 on Januaray 1st</dd></dl></div><div class='xr-var-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> 29.23 kiB </td>\n",
" <td> 730 B </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (14965,) </td>\n",
" <td> (365,) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 41 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> int16 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
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" <td>\n",
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"\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-30fcc6c2-466b-4ce7-8691-740d789e894e' class='xr-section-summary-in' type='checkbox' ><label for='section-30fcc6c2-466b-4ce7-8691-740d789e894e' class='xr-section-summary' >Indexes: <span>(3)</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-45b63bb8-c9c2-430b-91d9-a7c276ef3ab6' class='xr-index-data-in' type='checkbox'/><label for='index-45b63bb8-c9c2-430b-91d9-a7c276ef3ab6' 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(['1980-01-01 12:00:00', '1980-01-02 12:00:00',\n",
" '1980-01-03 12:00:00', '1980-01-04 12:00:00',\n",
" '1980-01-05 12:00:00', '1980-01-06 12:00:00',\n",
" '1980-01-07 12:00:00', '1980-01-08 12:00:00',\n",
" '1980-01-09 12:00:00', '1980-01-10 12:00:00',\n",
" ...\n",
" '2020-12-21 12:00:00', '2020-12-22 12:00:00',\n",
" '2020-12-23 12:00:00', '2020-12-24 12:00:00',\n",
" '2020-12-25 12:00:00', '2020-12-26 12:00:00',\n",
" '2020-12-27 12:00:00', '2020-12-28 12:00:00',\n",
" '2020-12-29 12:00:00', '2020-12-30 12:00:00'],\n",
" dtype='datetime64[ns]', name='time', length=14965, freq=None))</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-a5ed7bcb-f6b2-4ded-9fe7-4b6693d1d578' class='xr-index-data-in' type='checkbox'/><label for='index-a5ed7bcb-f6b2-4ded-9fe7-4b6693d1d578' 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([-4560250.0, -4559250.0, -4558250.0, -4557250.0, -4556250.0, -4555250.0,\n",
" -4554250.0, -4553250.0, -4552250.0, -4551250.0,\n",
" ...\n",
" 3243750.0, 3244750.0, 3245750.0, 3246750.0, 3247750.0, 3248750.0,\n",
" 3249750.0, 3250750.0, 3251750.0, 3252750.0],\n",
" dtype='float32', name='x', length=7814))</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-e688368c-59b4-4da1-908f-909acb75b68f' class='xr-index-data-in' type='checkbox'/><label for='index-e688368c-59b4-4da1-908f-909acb75b68f' 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([ 4984000.0, 4983000.0, 4982000.0, 4981000.0, 4980000.0, 4979000.0,\n",
" 4978000.0, 4977000.0, 4976000.0, 4975000.0,\n",
" ...\n",
" -3081000.0, -3082000.0, -3083000.0, -3084000.0, -3085000.0, -3086000.0,\n",
" -3087000.0, -3088000.0, -3089000.0, -3090000.0],\n",
" dtype='float32', name='y', length=8075))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4dce297b-5fca-40a8-bbc1-0e2561a693eb' class='xr-section-summary-in' type='checkbox' checked><label for='section-4dce297b-5fca-40a8-bbc1-0e2561a693eb' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>Version_data :</span></dt><dd>Daymet Data Version 4.0</dd><dt><span>Version_software :</span></dt><dd>Daymet Software Version 4.0</dd><dt><span>citation :</span></dt><dd>Please see http://daymet.ornl.gov/ for current Daymet data citation information</dd><dt><span>references :</span></dt><dd>Please see http://daymet.ornl.gov/ for current information on Daymet references</dd><dt><span>source :</span></dt><dd>Daymet Software Version 4.0</dd><dt><span>start_year :</span></dt><dd>1980</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (time: 14965, y: 8075, x: 7814, nv: 2)\n",
"Coordinates:\n",
" lat (y, x) float32 dask.array<chunksize=(284, 584), meta=np.ndarray>\n",
" lon (y, x) float32 dask.array<chunksize=(284, 584), meta=np.ndarray>\n",
" * time (time) datetime64[ns] 1980-01-01T12:00:00 ... 20...\n",
" * x (x) float32 -4.56e+06 -4.559e+06 ... 3.253e+06\n",
" * y (y) float32 4.984e+06 4.983e+06 ... -3.09e+06\n",
"Dimensions without coordinates: nv\n",
"Data variables:\n",
" dayl (time, y, x) float32 dask.array<chunksize=(365, 284, 584), meta=np.ndarray>\n",
" lambert_conformal_conic int16 ...\n",
" prcp (time, y, x) float32 dask.array<chunksize=(365, 284, 584), meta=np.ndarray>\n",
" srad (time, y, x) float32 dask.array<chunksize=(365, 284, 584), meta=np.ndarray>\n",
" swe (time, y, x) float32 dask.array<chunksize=(365, 284, 584), meta=np.ndarray>\n",
" time_bnds (time, nv) datetime64[ns] dask.array<chunksize=(365, 2), meta=np.ndarray>\n",
" tmax (time, y, x) float32 dask.array<chunksize=(365, 284, 584), meta=np.ndarray>\n",
" tmin (time, y, x) float32 dask.array<chunksize=(365, 284, 584), meta=np.ndarray>\n",
" vp (time, y, x) float32 dask.array<chunksize=(365, 284, 584), meta=np.ndarray>\n",
" yearday (time) int16 dask.array<chunksize=(365,), meta=np.ndarray>\n",
"Attributes:\n",
" Conventions: CF-1.6\n",
" Version_data: Daymet Data Version 4.0\n",
" Version_software: Daymet Software Version 4.0\n",
" citation: Please see http://daymet.ornl.gov/ for current Daymet ...\n",
" references: Please see http://daymet.ornl.gov/ for current informa...\n",
" source: Daymet Software Version 4.0\n",
" start_year: 1980"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import xarray as xr\n",
"\n",
"ds = xr.open_zarr(\n",
" asset.href,\n",
" **asset.extra_fields[\"xarray:open_kwargs\"],\n",
" storage_options=asset.extra_fields[\"xarray:storage_options\"],\n",
")\n",
"ds"
]
},
{
"cell_type": "markdown",
"id": "59c4ec09-548c-42d2-805e-16b1d7875448",
"metadata": {},
"source": [
"### Manually signing assets\n",
"\n",
"Earlier on, when we created our `pystac_client.Client`, we specified `modifier=planetary_computer.sign_inplace`. That `modifier` will automatically \"sign\" the STAC metadata, so that the assets can be accessed.\n",
"\n",
"Alternatively, you can manually sign the items."
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "825926df-eb00-420b-9cad-fe238a77c163",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"200"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pystac\n",
"\n",
"item = pystac.read_file(selected_item.get_self_href())\n",
"signed_item = planetary_computer.sign(item) # these assets can be accessed\n",
"requests.head(signed_item.assets[\"blue\"].href).status_code"
]
},
{
"cell_type": "markdown",
"id": "9eb85e6d-daf5-46ad-a95c-0b542a1e6aa7",
"metadata": {},
"source": [
"Internally, that `planetary_computer.sign` method is making a request to the Planetary Computer's [SAS API](http://planetarycomputer.microsoft.com/api/sas/v1/docs) to get a signed HREF for each asset. You could do that manually yourself."
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "d0759d76-453a-48a0-adac-a72d66e63dd2",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"200"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"collection = item.get_collection()\n",
"storage_account = collection.extra_fields[\"msft:storage_account\"]\n",
"container = collection.extra_fields[\"msft:container\"]\n",
"\n",
"response = requests.get(\n",
" f\"https://planetarycomputer.microsoft.com/api/sas/v1/token/{collection.id}\"\n",
")\n",
"\n",
"signed_url = item.assets[\"blue\"].href + \"?\" + response.json()[\"token\"]\n",
"\n",
"requests.head(signed_url).status_code"
]
},
{
"cell_type": "markdown",
"id": "6ea8409b-7438-4483-818f-149cafaf10df",
"metadata": {},
"source": [
"See https://planetarycomputer.microsoft.com/docs/concepts/sas/ for more on how to manually sign assets."
]
}
],
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"kernelspec": {
"display_name": "Python 3 (ipykernel)",
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