sentinel-5p.ipynb

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   "source": [
    "# Sentinel 5P Example\n",
    "\n",
    "This examples computes daily minimum, maximum and/or smoothened mean NO2 values for a given year and spatial extent from Sentinel-5P data.\n",
    "\n",
    "Upfront, you need to install the following libraries by executing `pip install openeo pandas plotly`:\n",
    "\n",
    "- openeo\n",
    "- pandas\n",
    "- plotly\n",
    "\n",
    "## Connect to openEO Platform"
   ]
  },
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   "id": "7b92357f-622e-4c02-83f0-9c71bc063f09",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Authenticated using refresh token.\n"
     ]
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     "data": {
      "text/plain": [
       "<Connection to 'https://openeocloud.vito.be/openeo/1.0.0/' with OidcBearerAuth>"
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     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import required packages\n",
    "import openeo\n",
    "from openeo.processes import process\n",
    "\n",
    "# Connect to the back-end and log in\n",
    "connection = openeo.connect(\"openeo.cloud\")\n",
    "connection.authenticate_oidc()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "206aa2d6-6e5f-48de-9465-3407ec949608",
   "metadata": {},
   "source": [
    "## Details about the Sentinel-5P collection"
   ]
  },
  {
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   "execution_count": 65,
   "id": "09e3dfb3-c5bf-4708-8b56-ce6529a7be61",
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   "outputs": [
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       "{'assets': {'metadata_iso_19139': {'href': 'https://services.terrascope.be/collectioncatalogue/srv/api/records/urn:eop:VITO:TERRASCOPE_S5P_L3_NO2_TD_V1/formatters/xml?approved=true',\n",
       "   'roles': ['metadata', 'iso-19139'],\n",
       "   'title': 'ISO 19139 metadata',\n",
       "   'type': 'application/vnd.iso.19139+xml'},\n",
       "  'thumbnail': {'href': 'https://services.terrascope.be/collectioncatalogue/images/quicklooks/S5P_NO2_TD.png',\n",
       "   'roles': ['thumbnail'],\n",
       "   'title': 'Thumbnail',\n",
       "   'type': 'image/png'}},\n",
       " 'cube:dimensions': {'bands': {'type': 'bands', 'values': ['NO2']},\n",
       "  't': {'extent': ['2019-01-01T00:00:00Z', None], 'type': 'temporal'},\n",
       "  'x': {'axis': 'x', 'extent': [-180, 180], 'step': 0.05, 'type': 'spatial'},\n",
       "  'y': {'axis': 'y', 'extent': [-89, 89], 'step': 0.05, 'type': 'spatial'}},\n",
       " 'description': 'Daily NO2 composites, based on Sentinel-5p data.',\n",
       " 'extent': {'spatial': {'bbox': [[-180, -89, 180, 89]]},\n",
       "  'temporal': {'interval': [['2019-01-01T00:00:00Z', None]]}},\n",
       " 'id': 'TERRASCOPE_S5P_L3_NO2_TD_V1',\n",
       " 'keywords': ['VITO',\n",
       "  'TERRASCOPE',\n",
       "  'COPERNICUS',\n",
       "  'ESA',\n",
       "  'Orthoimagery',\n",
       "  'SENTINEL-5P',\n",
       "  'TROPOMI',\n",
       "  'Level-3',\n",
       "  'Atmospheric Components',\n",
       "  'NO2',\n",
       "  'Day'],\n",
       " 'license': 'proprietary',\n",
       " 'links': [{'href': 'https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice',\n",
       "   'rel': 'license'},\n",
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       "   'rel': 'wmts',\n",
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       "   'rel': 'self'}],\n",
       " 'providers': [{'name': 'VITO, on behalf of the Belgian Science Policy Office (BELSPO).',\n",
       "   'roles': ['producer', 'licensor'],\n",
       "   'url': 'https://terrascope.be'}],\n",
       " 'stac_extensions': ['datacube',\n",
       "  'https://stac-extensions.github.io/eo/v1.0.0/schema.json'],\n",
       " 'stac_version': '0.9.0',\n",
       " 'summaries': {'constellation': ['Sentinel-5'],\n",
       "  'eo:bands': [{'gsd': 5600,\n",
       "    'name': 'NO2',\n",
       "    'offset': 0,\n",
       "    'scale': 5,\n",
       "    'type': 'int16',\n",
       "    'unit': 'µmol/m²'}],\n",
       "  'eo:gsd': [5600],\n",
       "  'instruments': ['TROPOMI'],\n",
       "  'platform': ['Sentinel-5P']},\n",
       " 'title': 'Sentinel-5P Daily NO2, full archive, by Terrascope.'}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "connection.describe_collection(\"TERRASCOPE_S5P_L3_NO2_TD_V1\")"
   ]
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   "cell_type": "markdown",
   "id": "5bc472a5-0c3d-4c07-82a8-9027cbe3c75e",
   "metadata": {},
   "source": [
    "## Configure the script\n",
    "\n",
    "- `year`: Set any year starting from 2019 for evaluation\n",
    "- `extent`: Specify the spatial extent / geometry. Examples are given for NRW (state) or Münster (city)\n",
    "- `moving_average_window`: The number of days for the moving average window (must be an odd number) - only applies if `method` is set to `mean`\n",
    "- `method`: Which spatial aggregation method to use (one of: `min`, `max` or `mean`) - you can run the script multiple times with different methods to show them all in a single chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3acf4f66-c1b6-4f7d-9dab-75968ea8a90e",
   "metadata": {},
   "outputs": [],
   "source": [
    "results = {}\n",
    "year = 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "7b4f73eb-c95b-4acf-a104-b073d7826b7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# NRW\n",
    "extent = {\n",
    "    \"type\": \"Polygon\",\n",
    "    \"coordinates\": [[\n",
    "        [9.02480809551023,50.520443333954574],\n",
    "        [5.885010577442016,50.520443333954574],\n",
    "        [5.885010577442016,52.14446135651707],\n",
    "        [9.024808095510227,52.14446135651707],\n",
    "        [9.02480809551023,50.520443333954574]\n",
    "    ]]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a7bff38e-1c2a-4703-9c68-2a299380358e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Münster\n",
    "extent = {\n",
    "    \"type\": \"Polygon\",\n",
    "    \"coordinates\": [[\n",
    "        [7.737228350528245,51.86687168604513],\n",
    "        [7.507741544165615,51.86687168604513],\n",
    "        [7.507741544165615,52.05013100121914],\n",
    "        [7.737228350528245,52.05013100121914],\n",
    "        [7.737228350528245,51.86687168604513]\n",
    "    ]]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d2cb91c9-2a4c-4c60-8c04-6a4461ea063f",
   "metadata": {},
   "outputs": [],
   "source": [
    "moving_average_window = 31\n",
    "method = \"max\" # mean or max or min"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab33d44a-776b-4818-b167-ca4a27cfd29d",
   "metadata": {},
   "source": [
    "## Run the process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "5bea957d-26d0-4c8f-9e51-05af5fb3cd39",
   "metadata": {},
   "outputs": [],
   "source": [
    "udf = openeo.UDF(\"\"\"\n",
    "from pandas import Series\n",
    "import numpy as np\n",
    "\n",
    "def apply_timeseries(series: Series, context: dict) -> Series:\n",
    "    return np.convolve(series, np.ones({n})/{n}, mode='same')\n",
    "\"\"\".format(n = moving_average_window))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "1ae4341b-ae55-49bd-8cc1-8bdf7927cf03",
   "metadata": {},
   "outputs": [
    {
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       "        <script type=\"application/json\">{\"id\": \"0230dc680e9d4c6793f1775e8659d962\", \"explicit-zoom\": true, \"height\": \"400px\", \"value\": {\"process_graph\": {\"loadcollection1\": {\"process_id\": \"load_collection\", \"arguments\": {\"id\": \"TERRASCOPE_S5P_L3_NO2_TD_V1\", \"spatial_extent\": {\"type\": \"Polygon\", \"coordinates\": [[[7.737228350528245, 51.86687168604513], [7.507741544165615, 51.86687168604513], [7.507741544165615, 52.05013100121914], [7.737228350528245, 52.05013100121914], [7.737228350528245, 51.86687168604513]]]}, \"temporal_extent\": [\"2019-01-01\", \"2019-12-31\"]}}, \"applydimension1\": {\"process_id\": \"apply_dimension\", \"arguments\": {\"data\": {\"from_node\": \"loadcollection1\"}, \"dimension\": \"t\", \"process\": {\"process_graph\": {\"arrayinterpolatelinear1\": {\"process_id\": \"array_interpolate_linear\", \"arguments\": {\"data\": {\"from_parameter\": \"data\"}}, \"result\": true}}}}}, \"aggregatespatial1\": {\"process_id\": \"aggregate_spatial\", \"arguments\": {\"data\": {\"from_node\": \"applydimension1\"}, \"geometries\": {\"type\": \"Polygon\", \"coordinates\": [[[7.737228350528245, 51.86687168604513], [7.507741544165615, 51.86687168604513], [7.507741544165615, 52.05013100121914], [7.737228350528245, 52.05013100121914], [7.737228350528245, 51.86687168604513]]]}, \"reducer\": {\"process_graph\": {\"max1\": {\"process_id\": \"max\", \"arguments\": {\"data\": {\"from_parameter\": \"data\"}}, \"result\": true}}}}}, \"saveresult1\": {\"process_id\": \"save_result\", \"arguments\": {\"data\": {\"from_node\": \"aggregatespatial1\"}, \"format\": \"JSON\", \"options\": {}}, \"result\": true}}}}</script>\n",
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    "datacube = connection.load_collection(\"TERRASCOPE_S5P_L3_NO2_TD_V1\", spatial_extent = extent, temporal_extent = [f\"{year}-01-01\", f\"{year}-12-31\"])\n",
    "datacube = datacube.apply_dimension(dimension = \"t\", process = \"array_interpolate_linear\")\n",
    "if method == \"mean\":\n",
    "    datacube = datacube.apply_dimension(dimension = \"t\", process = udf)\n",
    "datacube = datacube.aggregate_spatial(geometries = extent, reducer = method)\n",
    "datacube = datacube.save_result(format = \"JSON\")\n",
    "datacube"
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   "cell_type": "code",
   "execution_count": 76,
   "id": "daa23eef-a3ae-4157-a72f-a0fed183f4ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "results[method] = connection.execute(datacube)"
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  {
   "cell_type": "markdown",
   "id": "94d1319b-2c77-44d0-9113-8879642d98d9",
   "metadata": {},
   "source": [
    "## Prepare the data for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e3c6b2a1-1c60-4652-9baf-4c5466cc8ad5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27</th>\n",
       "      <td>9.486804</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-28</th>\n",
       "      <td>8.917889</td>\n",
       "      <td>14.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-29</th>\n",
       "      <td>8.545455</td>\n",
       "      <td>18.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>8.348974</td>\n",
       "      <td>21.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-31</th>\n",
       "      <td>8.123167</td>\n",
       "      <td>9.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>363 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                mean   min   max\n",
       "2019-01-01       NaN   NaN   NaN\n",
       "2019-01-02       NaN   0.0   2.0\n",
       "2019-01-03       NaN   0.0   2.0\n",
       "2019-01-04       NaN   0.0   2.0\n",
       "2019-01-05       NaN  -1.0   3.0\n",
       "...              ...   ...   ...\n",
       "2019-12-27  9.486804   4.0  14.0\n",
       "2019-12-28  8.917889  14.0  31.0\n",
       "2019-12-29  8.545455  18.0  28.0\n",
       "2019-12-30  8.348974  21.0  29.0\n",
       "2019-12-31  8.123167   9.0  15.0\n",
       "\n",
       "[363 rows x 3 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import DataFrame, Series\n",
    "from datetime import datetime\n",
    "\n",
    "values = {}\n",
    "for m, result in results.items():\n",
    "    dates = []\n",
    "    for k, v in result.items():\n",
    "        if m not in values:\n",
    "            values[m] = []\n",
    "        dates.append(datetime.strptime(k, \"%Y-%m-%dT%H:%M:%SZ\"))\n",
    "        values[m].append(v[0][0])\n",
    "\n",
    "df = DataFrame(values, dates, results.keys())\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22a27db3-a19b-4e7a-b3df-eac7fb084231",
   "metadata": {},
   "source": [
    "## Plot the data"
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   "execution_count": 78,
   "id": "36009b1f-7199-4500-b4d9-f0926d81298e",
   "metadata": {},
   "outputs": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "\n",
    "fig = px.line(df)\n",
    "fig.show()"
   ]
  }
 ],
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