2d_missing.ipynb

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   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b5bef27a-bd20-42e7-8c79-858ef24b7ec0",
   "metadata": {},
   "outputs": [],
   "source": [
    "destination_dir = os.path.join(\"2d\")\n",
    "os.makedirs(destination_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "29e64690-916e-43a3-a629-bad96139e93d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_latlong(footprints):\n",
    "    centroid = footprints.geometry.to_crs(\"EPSG:4326\").centroid\n",
    "    footprints['latitude'] = centroid.y\n",
    "    footprints['longitude'] = centroid.x\n",
    "    return pd.DataFrame(footprints.drop(columns=\"geometry\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6f857099-1293-49d2-b998-6827b89f05f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_dir = os.path.join(\"data\")\n",
    "footprints = gpd.read_file(os.path.join(base_dir, \"footprints.geojson\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b8552507-ab29-40c6-9b13-501a2ab17251",
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    "footprints"
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   "cell_type": "code",
   "execution_count": 6,
   "id": "d1fd16c4-74a8-4fed-9a84-80f2e3119824",
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      "/home/thomas/miniconda3/envs/thermal/lib/python3.6/site-packages/ipykernel_launcher.py:2: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
      "\n",
      "  \n"
     ]
    }
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   "source": [
    "footprints_ll = add_latlong(footprints)"
   ]
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   "execution_count": 7,
   "id": "8e8e46c4-1ba3-4319-9b59-fa7e76569b3f",
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   "execution_count": 8,
   "id": "a274a4f4-87a9-41a3-b81c-4413e9a18eae",
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   "source": [
    "data_dir = os.path.join(base_dir, \"satellite_unique\")\n",
    "\n",
    "elevation_path = os.path.join(data_dir, \"elevation.csv\")\n",
    "era5_land_path = os.path.join(data_dir, \"era5_land.csv\")\n",
    "era5_path = os.path.join(data_dir, \"era5.csv\")\n",
    "lst_path = os.path.join(data_dir, \"lst.csv\")\n",
    "sentinel_2A_path = os.path.join(data_dir, \"sentinel_2A.csv\")\n",
    "sentinel_1C_path = os.path.join(data_dir, \"sentinel_1C.csv\")\n",
    "landsat_path = os.path.join(data_dir, \"landsat8.csv\")\n",
    "viirs_path = os.path.join(data_dir, \"viirs.csv\")"
   ]
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   "execution_count": 9,
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       "9246  1008280059  1015659  27520  40.744560 -73.989475          1\n",
       "9247  2029410001  2114529  5830f  40.840495 -73.897780          1\n",
       "9248  4098750001  4211112  2ec2a  40.717143 -73.794127          1\n",
       "9249  2048190015  2062829  2bf8f  40.887201 -73.864459          1\n",
       "\n",
       "[9250 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elevation = pd.read_csv(elevation_path)\n",
    "\n",
    "elevation.loc[:,\"bbl\"] = elevation.loc[:,\"bbl\"].astype(str)\n",
    "elevation.loc[:,\"bin\"] = elevation.loc[:,\"bin\"].astype(str)\n",
    "\n",
    "elevation_counts = elevation.groupby(['bbl','bin']).count().reset_index()\n",
    "\n",
    "elevation_null = pd.merge(footprints_ll, elevation_counts, left_on=[\"bbl\",\"bin\"], right_on=[\"bbl\",\"bin\"])\n",
    "elevation_null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8074ba4e-bd6c-4be1-ab59-272550f7b451",
   "metadata": {},
   "outputs": [],
   "source": [
    "# elevation_null.to_file(os.path.join(destination_dir, \"elevation_counts.geojson\"), driver=\"GeoJSON\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "da9851fc-ee2d-46ba-afb9-45464cbbdfe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bbl</th>\n",
       "      <th>bin</th>\n",
       "      <th>date</th>\n",
       "      <th>mean_2m_air_temperature</th>\n",
       "      <th>minimum_2m_air_temperature</th>\n",
       "      <th>maximum_2m_air_temperature</th>\n",
       "      <th>dewpoint_2m_temperature</th>\n",
       "      <th>total_precipitation</th>\n",
       "      <th>surface_pressure</th>\n",
       "      <th>mean_sea_level_pressure</th>\n",
       "      <th>u_component_of_wind_10m</th>\n",
       "      <th>v_component_of_wind_10m</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1007640007</td>\n",
       "      <td>1013712</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>271.787140</td>\n",
       "      <td>255.525620</td>\n",
       "      <td>289.121735</td>\n",
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       "      <td>0.078149</td>\n",
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       "      <td>102253.250000</td>\n",
       "      <td>1.851955</td>\n",
       "      <td>-0.512473</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3050830044</td>\n",
       "      <td>3116615</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>271.838043</td>\n",
       "      <td>254.907501</td>\n",
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       "      <td>266.663757</td>\n",
       "      <td>0.073549</td>\n",
       "      <td>102007.164062</td>\n",
       "      <td>102241.953125</td>\n",
       "      <td>1.721707</td>\n",
       "      <td>-0.435242</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2033180015</td>\n",
       "      <td>2017499</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>271.396332</td>\n",
       "      <td>255.562424</td>\n",
       "      <td>288.285980</td>\n",
       "      <td>265.629211</td>\n",
       "      <td>0.079027</td>\n",
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       "      <td>102234.062500</td>\n",
       "      <td>1.668592</td>\n",
       "      <td>-0.493059</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          bbl      bin       date  mean_2m_air_temperature  \\\n",
       "0  1007640007  1013712 2018-01-01               271.787140   \n",
       "1  3050830044  3116615 2018-01-01               271.838043   \n",
       "2  2033180015  2017499 2018-01-01               271.396332   \n",
       "\n",
       "   minimum_2m_air_temperature  maximum_2m_air_temperature  \\\n",
       "0                  255.525620                  289.121735   \n",
       "1                  254.907501                  288.015198   \n",
       "2                  255.562424                  288.285980   \n",
       "\n",
       "   dewpoint_2m_temperature  total_precipitation  surface_pressure  \\\n",
       "0               266.019714             0.078149     101327.625000   \n",
       "1               266.663757             0.073549     102007.164062   \n",
       "2               265.629211             0.079027     101002.234375   \n",
       "\n",
       "   mean_sea_level_pressure  u_component_of_wind_10m  v_component_of_wind_10m  \\\n",
       "0            102253.250000                 1.851955                -0.512473   \n",
       "1            102241.953125                 1.721707                -0.435242   \n",
       "2            102234.062500                 1.668592                -0.493059   \n",
       "\n",
       "   month  year  \n",
       "0      1  2018  \n",
       "1      1  2018  \n",
       "2      1  2018  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "era5 = pd.read_csv(era5_path)\n",
    "\n",
    "era5.loc[:,\"bbl\"] = era5.loc[:,\"bbl\"].astype(str)\n",
    "era5.loc[:,\"bin\"] = era5.loc[:,\"bin\"].astype(str)\n",
    "\n",
    "era5.loc[:,\"date\"] = pd.to_datetime(era5.date)\n",
    "era5.loc[:,\"month\"] = era5.date.dt.month\n",
    "era5.loc[:,\"year\"] = era5.date.dt.year\n",
    "era5.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8dd2f9e5-8d7d-4fb8-9b59-1dc88a6e7d1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>bbl</th>\n",
       "      <th>bin</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>mean_2m_air_temperature</th>\n",
       "      <th>minimum_2m_air_temperature</th>\n",
       "      <th>maximum_2m_air_temperature</th>\n",
       "      <th>dewpoint_2m_temperature</th>\n",
       "      <th>total_precipitation</th>\n",
       "      <th>surface_pressure</th>\n",
       "      <th>mean_sea_level_pressure</th>\n",
       "      <th>u_component_of_wind_10m</th>\n",
       "      <th>v_component_of_wind_10m</th>\n",
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       "  </thead>\n",
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       "      <td>1000020001</td>\n",
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       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>9245</th>\n",
       "      <td>5078410154</td>\n",
       "      <td>5087579</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>9246</th>\n",
       "      <td>5078940001</td>\n",
       "      <td>5088090</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>9247</th>\n",
       "      <td>5078980001</td>\n",
       "      <td>5107854</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>9248</th>\n",
       "      <td>5079710250</td>\n",
       "      <td>5107860</td>\n",
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       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>9249</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9250 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             bbl      bin  year  month  mean_2m_air_temperature  \\\n",
       "0     1000020001  1085792    30     30                       30   \n",
       "1     1000080039  1078968    30     30                       30   \n",
       "2     1000080051  1090472    30     30                       30   \n",
       "3     1000090001  1000018    30     30                       30   \n",
       "4     1000090014  1000020    30     30                       30   \n",
       "...          ...      ...   ...    ...                      ...   \n",
       "9245  5078410154  5087579    30     30                       30   \n",
       "9246  5078940001  5088090    30     30                       30   \n",
       "9247  5078980001  5107854    30     30                       30   \n",
       "9248  5079710250  5107860    30     30                       30   \n",
       "9249  5079910100  5088569    30     30                       30   \n",
       "\n",
       "      minimum_2m_air_temperature  maximum_2m_air_temperature  \\\n",
       "0                             30                          30   \n",
       "1                             30                          30   \n",
       "2                             30                          30   \n",
       "3                             30                          30   \n",
       "4                             30                          30   \n",
       "...                          ...                         ...   \n",
       "9245                          30                          30   \n",
       "9246                          30                          30   \n",
       "9247                          30                          30   \n",
       "9248                          30                          30   \n",
       "9249                          30                          30   \n",
       "\n",
       "      dewpoint_2m_temperature  total_precipitation  surface_pressure  \\\n",
       "0                          30                   30                30   \n",
       "1                          30                   30                30   \n",
       "2                          30                   30                30   \n",
       "3                          30                   30                30   \n",
       "4                          30                   30                30   \n",
       "...                       ...                  ...               ...   \n",
       "9245                       30                   30                30   \n",
       "9246                       30                   30                30   \n",
       "9247                       30                   30                30   \n",
       "9248                       30                   30                30   \n",
       "9249                       30                   30                30   \n",
       "\n",
       "      mean_sea_level_pressure  u_component_of_wind_10m  \\\n",
       "0                          30                       30   \n",
       "1                          30                       30   \n",
       "2                          30                       30   \n",
       "3                          30                       30   \n",
       "4                          30                       30   \n",
       "...                       ...                      ...   \n",
       "9245                       30                       30   \n",
       "9246                       30                       30   \n",
       "9247                       30                       30   \n",
       "9248                       30                       30   \n",
       "9249                       30                       30   \n",
       "\n",
       "      v_component_of_wind_10m  \n",
       "0                          30  \n",
       "1                          30  \n",
       "2                          30  \n",
       "3                          30  \n",
       "4                          30  \n",
       "...                       ...  \n",
       "9245                       30  \n",
       "9246                       30  \n",
       "9247                       30  \n",
       "9248                       30  \n",
       "9249                       30  \n",
       "\n",
       "[9250 rows x 13 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_era5 = era5.groupby(['bbl','bin','year','month']).mean().reset_index()\n",
    "era5_counts = monthly_era5.groupby([\"bbl\",\"bin\"]).count().reset_index()\n",
    "era5_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bbedb987-6633-47e1-a13c-f0fcc3075631",
   "metadata": {},
   "outputs": [],
   "source": [
    "era5_footprintscount = pd.merge(footprints_ll, era5_counts, left_on=[\"bbl\",\"bin\"], right_on=[\"bbl\",\"bin\"])\n",
    "era5_footprintscount.to_csv(os.path.join(destination_dir, \"era5_counts.csv\"))\n",
    "# era5_footprintscount.to_file(os.path.join(destination_dir, \"era5_counts.geojson\"), driver=\"GeoJSON\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e130f34-ff72-4141-9bf0-46baf7b7e8e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a85c4b22-2e43-4c26-b6cf-15004845ca16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# era5_land = pd.read_csv(era5_land_path)\n",
    "\n",
    "# era5_land.loc[:,\"bbl\"] = era5_land.loc[:,\"bbl\"].astype(str)\n",
    "# era5_land.loc[:,\"bin\"] = era5_land.loc[:,\"bin\"].astype(str)\n",
    "\n",
    "# era5_land.loc[:,\"date\"] = pd.to_datetime(era5_land.date)\n",
    "# era5_land.loc[:,\"month\"] = era5_land.date.dt.month\n",
    "# era5_land.loc[:,\"year\"] = era5_land.date.dt.year\n",
    "# era5_land.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3ccc42ca-09a8-490a-98f7-21374b011f53",
   "metadata": {},
   "outputs": [],
   "source": [
    "# monthly_era5land = era5_land.groupby(['bbl','bin','year','month']).mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1969434b-2ac6-4b3f-b6be-7b9ded2771ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# era5_land_counts = monthly_era5land.groupby([\"bbl\",\"bin\"]).count().reset_index()[[\"bbl\",\"bin\",\"temperature_2m\"]]\n",
    "# era5_land_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f33d5d54-48a3-449e-b772-5855900826a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# era5_land_footprintscount = pd.merge(footprints, era5_land_counts, left_on=[\"bbl\",\"bin\"], right_on=[\"bbl\",\"bin\"])\n",
    "# era5_land_footprintscount.to_file(os.path.join(destination_dir, \"era5_land_counts.geojson\"), driver=\"GeoJSON\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0f1c0532-d1f7-40f3-be4f-458de0ac1416",
   "metadata": {},
   "outputs": [],
   "source": [
    "# era5_land_footprintscount.temperature_2m.hist()\n",
    "# plt.title(\"Frequency of temperature availability -era5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "954f27a6-21c2-4e15-9dad-673cb7dd8b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Now thinking about LST\n",
    "# lst = pd.read_csv(lst_path)\n",
    "\n",
    "# lst.loc[:,\"bbl\"] = lst.loc[:,\"bbl\"].astype(str)\n",
    "# lst.loc[:,\"bin\"] = lst.loc[:,\"bin\"].astype(str)\n",
    "\n",
    "# lst.loc[:,\"date\"] = pd.to_datetime(lst.date)\n",
    "# lst.loc[:,\"month\"] = lst.date.dt.month\n",
    "# lst.loc[:,\"year\"] = lst.date.dt.year\n",
    "\n",
    "# lst_monthly = lst.groupby([\"bbl\",\"bin\",\"year\",\"month\"], dropna=False).mean().reset_index()\n",
    "\n",
    "# # lst_null = pd.merge(footprints, lst_monthly, left_on=[\"bbl\",\"bin\"], right_on=[\"bbl\",\"bin\"])\n",
    "# # lst_nullterms = lst_null[lst_null.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9e22b72d-93da-47ea-8b1f-8df8ecf729aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lst_monthly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a181111f-bc22-46ca-aa39-ed82b9379756",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lst_counts = lst_monthly.groupby([\"bbl\",\"bin\"], dropna=False).count().reset_index().drop(columns=['year','month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "750d86d9-038e-4c46-ac23-61d47dc5ad42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lst_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0e80b0c0-9dc7-4651-93bb-f5fd74bfa089",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lst_nullcounts = pd.merge(footprints, lst_counts, left_on=[\"bbl\",\"bin\"], right_on=[\"bbl\",\"bin\"])\n",
    "# lst_nullcounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "900939cf-3973-4075-818f-b57893e14eb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lst_nullcounts.to_file(os.path.join(destination_dir, \"lst_counts.geojson\"), driver=\"GeoJSON\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cedecc07-da8a-47ed-8bc5-18b219c6fa48",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>bbl</th>\n",
       "      <th>bin</th>\n",
       "      <th>date</th>\n",
       "      <th>avg_rad</th>\n",
       "      <th>cf_cvg</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1007640007</td>\n",
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       "      <td>2018-01-01</td>\n",
       "      <td>108.335547</td>\n",
       "      <td>11.346875</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
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       "      <th>1</th>\n",
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       "      <td>14.137729</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2033180015</td>\n",
       "      <td>2017499</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>88.671643</td>\n",
       "      <td>12.030175</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3074040001</td>\n",
       "      <td>3202112</td>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>40.342716</td>\n",
       "      <td>14.078403</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
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       "    <tr>\n",
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       "      <td>2018-01-01</td>\n",
       "      <td>50.992780</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <th>332995</th>\n",
       "      <td>3017130007</td>\n",
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       "      <td>115.349383</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
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       "    <tr>\n",
       "      <th>332996</th>\n",
       "      <td>1008280059</td>\n",
       "      <td>1015659</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>117.372896</td>\n",
       "      <td>8.627148</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332997</th>\n",
       "      <td>2029410001</td>\n",
       "      <td>2114529</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>121.778479</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332998</th>\n",
       "      <td>4098750001</td>\n",
       "      <td>4211112</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>68.953659</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
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       "    <tr>\n",
       "      <th>332999</th>\n",
       "      <td>2048190015</td>\n",
       "      <td>2062829</td>\n",
       "      <td>2020-12-01</td>\n",
       "      <td>44.071499</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>333000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               bbl      bin       date     avg_rad     cf_cvg  month  year\n",
       "0       1007640007  1013712 2018-01-01  108.335547  11.346875      1  2018\n",
       "1       3050830044  3116615 2018-01-01   53.679238  14.137729      1  2018\n",
       "2       2033180015  2017499 2018-01-01   88.671643  12.030175      1  2018\n",
       "3       3074040001  3202112 2018-01-01   40.342716  14.078403      1  2018\n",
       "4       3050580057  3116089 2018-01-01   50.992780  14.000000      1  2018\n",
       "...            ...      ...        ...         ...        ...    ...   ...\n",
       "332995  3017130007  3421590 2020-12-01  115.349383   9.000000     12  2020\n",
       "332996  1008280059  1015659 2020-12-01  117.372896   8.627148     12  2020\n",
       "332997  2029410001  2114529 2020-12-01  121.778479   9.000000     12  2020\n",
       "332998  4098750001  4211112 2020-12-01   68.953659  10.000000     12  2020\n",
       "332999  2048190015  2062829 2020-12-01   44.071499   9.000000     12  2020\n",
       "\n",
       "[333000 rows x 7 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viirs = pd.read_csv(viirs_path)\n",
    "\n",
    "viirs.loc[:,\"bbl\"] = viirs.loc[:,\"bbl\"].astype(str)\n",
    "viirs.loc[:,\"bin\"] = viirs.loc[:,\"bin\"].astype(str)\n",
    "\n",
    "viirs.loc[:,\"date\"] = pd.to_datetime(viirs.date)\n",
    "viirs.loc[:,\"month\"] = viirs.date.dt.month\n",
    "viirs.loc[:,\"year\"] = viirs.date.dt.year\n",
    "\n",
    "viirs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0672e4b5-fb8e-4307-bc89-6b22e5c5bce5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viirs.groupby(['bbl','bin','date']).size().reset_index()[0].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c32bbbe1-6b68-43a5-bbc0-f9fd1dbd8559",
   "metadata": {},
   "outputs": [
    {
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       "      <th>avg_rad</th>\n",
       "      <th>cf_cvg</th>\n",
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       "      <th>9248</th>\n",
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       "      <td>36</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>9250 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             bbl      bin  date  avg_rad  cf_cvg  month  year\n",
       "0     1000020001  1085792    36       36      36     36    36\n",
       "1     1000080039  1078968    36       36      36     36    36\n",
       "2     1000080051  1090472    36       36      36     36    36\n",
       "3     1000090001  1000018    36       36      36     36    36\n",
       "4     1000090014  1000020    36       36      36     36    36\n",
       "...          ...      ...   ...      ...     ...    ...   ...\n",
       "9245  5078410154  5087579    36       36      36     36    36\n",
       "9246  5078940001  5088090    36       36      36     36    36\n",
       "9247  5078980001  5107854    36       36      36     36    36\n",
       "9248  5079710250  5107860    36       36      36     36    36\n",
       "9249  5079910100  5088569    36       36      36     36    36\n",
       "\n",
       "[9250 rows x 7 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viirs_counts = viirs.groupby(['bbl','bin']).count().reset_index()\n",
    "viirs_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d5066e1b-d624-4b11-8b83-6599c12cb28b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([36])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viirs_counts[\"avg_rad\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "27061a7b-6a02-4667-86de-433e87321dce",
   "metadata": {},
   "outputs": [],
   "source": [
    "viirs_footcounts = pd.merge(footprints_ll, viirs_counts, left_on=[\"bbl\",\"bin\"], right_on=[\"bbl\",\"bin\"])\n",
    "viirs_footcounts.to_csv(os.path.join(destination_dir, \"viirs_counts.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d5fdf5db-92a3-4135-8b9f-7df08dd62124",
   "metadata": {},
   "outputs": [
    {
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       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2033180015</td>\n",
       "      <td>2017499</td>\n",
       "      <td>2018-01-15 15:33:44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          bbl      bin                date  ST_B10  ST_QA  ST_ATRAN  ST_CDIST  \\\n",
       "0  1007640007  1013712 2018-01-15 15:33:44     NaN    NaN       NaN       NaN   \n",
       "1  3050830044  3116615 2018-01-15 15:33:44     NaN    NaN       NaN       NaN   \n",
       "2  2033180015  2017499 2018-01-15 15:33:44     NaN    NaN       NaN       NaN   \n",
       "\n",
       "   ST_DRAD  ST_EMIS  ST_EMSD  ...  SR_B1  SR_B2  SR_B3  SR_B4  SR_B5  SR_B6  \\\n",
       "0      NaN      NaN      NaN  ...    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "1      NaN      NaN      NaN  ...    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "2      NaN      NaN      NaN  ...    NaN    NaN    NaN    NaN    NaN    NaN   \n",
       "\n",
       "   SR_B7  NDVI  month  year  \n",
       "0    NaN   NaN      1  2018  \n",
       "1    NaN   NaN      1  2018  \n",
       "2    NaN   NaN      1  2018  \n",
       "\n",
       "[3 rows x 22 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landsat = pd.read_csv(landsat_path)\n",
    "\n",
    "landsat.loc[:,\"bbl\"] = landsat.loc[:,\"bbl\"].astype(str)\n",
    "landsat.loc[:,\"bin\"] = landsat.loc[:,\"bin\"].astype(str)\n",
    "\n",
    "landsat.loc[:,\"date\"] = pd.to_datetime(landsat.date)\n",
    "landsat.loc[:,\"month\"] = landsat.date.dt.month\n",
    "landsat.loc[:,\"year\"] = landsat.date.dt.year\n",
    "\n",
    "landsat.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7c33197c-db1f-4f98-a3a3-33caca1b47ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landsat.groupby(['bbl','bin','date']).size().reset_index()[0].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "398d10ad-8969-4c7e-9c70-22f6f265496f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "landsat.groupby(['year','month']).median()['NDVI'].plot(kind=\"bar\", figsize=(20,6), title=\"Landsat Monthly NDVI\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0009a9e7-5792-47f3-ac29-7d366694bc18",
   "metadata": {},
   "outputs": [],
   "source": [
    "landsat_monthly = landsat.groupby(['bbl','bin','year','month']).mean().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0a0ebbc3-8bee-471d-838b-579809315dd6",
   "metadata": {},
   "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>bbl</th>\n",
       "      <th>bin</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>NDVI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000020001</td>\n",
       "      <td>1085792</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000020001</td>\n",
       "      <td>1085792</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000020001</td>\n",
       "      <td>1085792</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000020001</td>\n",
       "      <td>1085792</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000020001</td>\n",
       "      <td>1085792</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323745</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323746</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323747</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323748</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323749</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>323750 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               bbl      bin  year  month  NDVI\n",
       "0       1000020001  1085792  2018      1     1\n",
       "1       1000020001  1085792  2018      3     1\n",
       "2       1000020001  1085792  2018      4     1\n",
       "3       1000020001  1085792  2018      5     0\n",
       "4       1000020001  1085792  2018      6     1\n",
       "...            ...      ...   ...    ...   ...\n",
       "323745  5079910100  5088569  2020      8     0\n",
       "323746  5079910100  5088569  2020      9     1\n",
       "323747  5079910100  5088569  2020     10     1\n",
       "323748  5079910100  5088569  2020     11     1\n",
       "323749  5079910100  5088569  2020     12     1\n",
       "\n",
       "[323750 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landsat_monthly_bool = (landsat.groupby(['bbl','bin','year','month']).count() > 0).reset_index()[['bbl','bin','year','month','NDVI']]\n",
    "landsat_monthly_bool['NDVI'] = landsat_monthly_bool['NDVI'].astype(int)\n",
    "landsat_monthly_bool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b0bd5b4d-49db-4354-90d6-a6a3969dce47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8366486486486486"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landsat_monthly_bool['NDVI'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "404426c4-feb0-4f59-90bc-cff2a1c0c08c",
   "metadata": {},
   "outputs": [],
   "source": [
    "landsat_footcounts_monthly = pd.merge(footprints_ll, landsat_monthly_bool, left_on=[\"bbl\",\"bin\"], right_on=[\"bbl\",\"bin\"])\n",
    "landsat_footcounts_monthly.to_csv(os.path.join(destination_dir, \"landsat_counts_monthly.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "35ac836f-a837-49fd-ad15-a7f634e5dcd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bbl</th>\n",
       "      <th>bin</th>\n",
       "      <th>date</th>\n",
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       "      <td>166</td>\n",
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       "    <tr>\n",
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       "      <td>59</td>\n",
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       "      <td>59</td>\n",
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       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
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       "    <tr>\n",
       "      <th>9249</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>166</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>...</td>\n",
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       "      <td>56</td>\n",
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       "      <td>56</td>\n",
       "      <td>56</td>\n",
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       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9250 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             bbl      bin  date  ST_B10  ST_QA  ST_ATRAN  ST_CDIST  ST_DRAD  \\\n",
       "0     1000020001  1085792   166      48     48        48        48       48   \n",
       "1     1000080039  1078968   166      45     45        45        45       45   \n",
       "2     1000080051  1090472   166      45     45        45        45       45   \n",
       "3     1000090001  1000018   166      47     47        47        47       47   \n",
       "4     1000090014  1000020   166      48     48        48        48       48   \n",
       "...          ...      ...   ...     ...    ...       ...       ...      ...   \n",
       "9245  5078410154  5087579   166      60     60        60        60       60   \n",
       "9246  5078940001  5088090   166      61     61        61        61       61   \n",
       "9247  5078980001  5107854   166      61     61        61        61       61   \n",
       "9248  5079710250  5107860   166      59     59        59        59       59   \n",
       "9249  5079910100  5088569   166      56     56        56        56       56   \n",
       "\n",
       "      ST_EMIS  ST_EMSD  ...  SR_B1  SR_B2  SR_B3  SR_B4  SR_B5  SR_B6  SR_B7  \\\n",
       "0          48       48  ...     48     48     48     48     48     48     48   \n",
       "1          45       45  ...     45     45     45     45     45     45     45   \n",
       "2          45       45  ...     45     45     45     45     45     45     45   \n",
       "3          47       47  ...     47     47     47     47     47     47     47   \n",
       "4          48       48  ...     48     48     48     48     48     48     48   \n",
       "...       ...      ...  ...    ...    ...    ...    ...    ...    ...    ...   \n",
       "9245       60       60  ...     60     60     60     60     60     60     60   \n",
       "9246       61       61  ...     61     61     61     61     61     61     61   \n",
       "9247       61       61  ...     61     61     61     61     61     61     61   \n",
       "9248       59       59  ...     59     59     59     59     59     59     59   \n",
       "9249       56       56  ...     56     56     56     56     56     56     56   \n",
       "\n",
       "      NDVI  month  year  \n",
       "0       48    166   166  \n",
       "1       45    166   166  \n",
       "2       45    166   166  \n",
       "3       47    166   166  \n",
       "4       48    166   166  \n",
       "...    ...    ...   ...  \n",
       "9245    60    166   166  \n",
       "9246    61    166   166  \n",
       "9247    61    166   166  \n",
       "9248    59    166   166  \n",
       "9249    56    166   166  \n",
       "\n",
       "[9250 rows x 22 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landsat_counts = landsat.groupby(['bbl','bin']).count().reset_index()\n",
    "landsat_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b8e2ce76-29df-422c-8eaa-03c09b45ed0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Histogram of Samples per building')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(landsat_counts[\"ST_B10\"].unique(), bins=15)\n",
    "plt.xlim(0,100)\n",
    "plt.title(\"Histogram of Samples per building\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2273f561-b5ff-4786-9a30-7bdf8bb93fea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "landsat[[\"date\",\"ST_B10\"]].groupby([landsat[\"date\"].dt.year, landsat[\"date\"].dt.month]).count()[\"ST_B10\"].plot(kind=\"bar\", figsize=(15,8), title=\"Landsat Monthly Capture\")\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.xlabel(\"Date of Capture\")\n",
    "plt.ylabel(\"Count of Instances\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "4bda8bf7-6fe3-46ce-a0cf-73a704f1bad9",
   "metadata": {},
   "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>bbl</th>\n",
       "      <th>bin</th>\n",
       "      <th>date</th>\n",
       "      <th>ST_B10</th>\n",
       "      <th>ST_QA</th>\n",
       "      <th>ST_ATRAN</th>\n",
       "      <th>ST_CDIST</th>\n",
       "      <th>ST_DRAD</th>\n",
       "      <th>ST_EMIS</th>\n",
       "      <th>ST_EMSD</th>\n",
       "      <th>...</th>\n",
       "      <th>SR_B2</th>\n",
       "      <th>SR_B3</th>\n",
       "      <th>SR_B4</th>\n",
       "      <th>SR_B5</th>\n",
       "      <th>SR_B6</th>\n",
       "      <th>SR_B7</th>\n",
       "      <th>NDVI</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>percent_found</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000020001</td>\n",
       "      <td>1085792</td>\n",
       "      <td>166</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>0.289157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000080039</td>\n",
       "      <td>1078968</td>\n",
       "      <td>166</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>0.271084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000080051</td>\n",
       "      <td>1090472</td>\n",
       "      <td>166</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>0.271084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000090001</td>\n",
       "      <td>1000018</td>\n",
       "      <td>166</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>...</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>0.283133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000090014</td>\n",
       "      <td>1000020</td>\n",
       "      <td>166</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>...</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>0.289157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9245</th>\n",
       "      <td>5078410154</td>\n",
       "      <td>5087579</td>\n",
       "      <td>166</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>0.361446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9246</th>\n",
       "      <td>5078940001</td>\n",
       "      <td>5088090</td>\n",
       "      <td>166</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>0.367470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9247</th>\n",
       "      <td>5078980001</td>\n",
       "      <td>5107854</td>\n",
       "      <td>166</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>0.367470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9248</th>\n",
       "      <td>5079710250</td>\n",
       "      <td>5107860</td>\n",
       "      <td>166</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>...</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>59</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>0.355422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9249</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>166</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>...</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>166</td>\n",
       "      <td>166</td>\n",
       "      <td>0.337349</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9250 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             bbl      bin  date  ST_B10  ST_QA  ST_ATRAN  ST_CDIST  ST_DRAD  \\\n",
       "0     1000020001  1085792   166      48     48        48        48       48   \n",
       "1     1000080039  1078968   166      45     45        45        45       45   \n",
       "2     1000080051  1090472   166      45     45        45        45       45   \n",
       "3     1000090001  1000018   166      47     47        47        47       47   \n",
       "4     1000090014  1000020   166      48     48        48        48       48   \n",
       "...          ...      ...   ...     ...    ...       ...       ...      ...   \n",
       "9245  5078410154  5087579   166      60     60        60        60       60   \n",
       "9246  5078940001  5088090   166      61     61        61        61       61   \n",
       "9247  5078980001  5107854   166      61     61        61        61       61   \n",
       "9248  5079710250  5107860   166      59     59        59        59       59   \n",
       "9249  5079910100  5088569   166      56     56        56        56       56   \n",
       "\n",
       "      ST_EMIS  ST_EMSD  ...  SR_B2  SR_B3  SR_B4  SR_B5  SR_B6  SR_B7  NDVI  \\\n",
       "0          48       48  ...     48     48     48     48     48     48    48   \n",
       "1          45       45  ...     45     45     45     45     45     45    45   \n",
       "2          45       45  ...     45     45     45     45     45     45    45   \n",
       "3          47       47  ...     47     47     47     47     47     47    47   \n",
       "4          48       48  ...     48     48     48     48     48     48    48   \n",
       "...       ...      ...  ...    ...    ...    ...    ...    ...    ...   ...   \n",
       "9245       60       60  ...     60     60     60     60     60     60    60   \n",
       "9246       61       61  ...     61     61     61     61     61     61    61   \n",
       "9247       61       61  ...     61     61     61     61     61     61    61   \n",
       "9248       59       59  ...     59     59     59     59     59     59    59   \n",
       "9249       56       56  ...     56     56     56     56     56     56    56   \n",
       "\n",
       "      month  year  percent_found  \n",
       "0       166   166       0.289157  \n",
       "1       166   166       0.271084  \n",
       "2       166   166       0.271084  \n",
       "3       166   166       0.283133  \n",
       "4       166   166       0.289157  \n",
       "...     ...   ...            ...  \n",
       "9245    166   166       0.361446  \n",
       "9246    166   166       0.367470  \n",
       "9247    166   166       0.367470  \n",
       "9248    166   166       0.355422  \n",
       "9249    166   166       0.337349  \n",
       "\n",
       "[9250 rows x 23 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landsat_counts['percent_found'] = landsat_counts['ST_B10'] / landsat_counts['month']\n",
    "landsat_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6b27962f-0dfb-46d0-b983-ea8fd566cb1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "landsat_footcounts = pd.merge(footprints_ll, landsat_counts, left_on=[\"bbl\",\"bin\"], right_on=[\"bbl\",\"bin\"])\n",
    "landsat_footcounts.to_csv(os.path.join(destination_dir, \"landsat_counts.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "020f544d-bc78-45d1-aa23-c8a749a74c38",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "f2096c4c-614c-4492-8fe7-6765a56edde5",
   "metadata": {},
   "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>bbl</th>\n",
       "      <th>bin</th>\n",
       "      <th>date</th>\n",
       "      <th>B1</th>\n",
       "      <th>B2</th>\n",
       "      <th>B3</th>\n",
       "      <th>B4</th>\n",
       "      <th>B5</th>\n",
       "      <th>B6</th>\n",
       "      <th>B7</th>\n",
       "      <th>B8</th>\n",
       "      <th>B8A</th>\n",
       "      <th>B9</th>\n",
       "      <th>B11</th>\n",
       "      <th>B12</th>\n",
       "      <th>NDVI</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1007640007</td>\n",
       "      <td>1013712</td>\n",
       "      <td>2018-01-01 16:01:22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3050830044</td>\n",
       "      <td>3116615</td>\n",
       "      <td>2018-01-01 16:01:22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2033180015</td>\n",
       "      <td>2017499</td>\n",
       "      <td>2018-01-01 16:01:22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          bbl      bin                date  B1  B2  B3  B4  B5  B6  B7  B8  \\\n",
       "0  1007640007  1013712 2018-01-01 16:01:22 NaN NaN NaN NaN NaN NaN NaN NaN   \n",
       "1  3050830044  3116615 2018-01-01 16:01:22 NaN NaN NaN NaN NaN NaN NaN NaN   \n",
       "2  2033180015  2017499 2018-01-01 16:01:22 NaN NaN NaN NaN NaN NaN NaN NaN   \n",
       "\n",
       "   B8A  B9  B11  B12  NDVI  month  year  \n",
       "0  NaN NaN  NaN  NaN   NaN      1  2018  \n",
       "1  NaN NaN  NaN  NaN   NaN      1  2018  \n",
       "2  NaN NaN  NaN  NaN   NaN      1  2018  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_1C = pd.read_csv(sentinel_1C_path)\n",
    "\n",
    "sentinel_1C.loc[:,\"bbl\"] = sentinel_1C.loc[:,\"bbl\"].astype(str)\n",
    "sentinel_1C.loc[:,\"bin\"] = sentinel_1C.loc[:,\"bin\"].astype(str)\n",
    "\n",
    "sentinel_1C.loc[:,\"date\"] = pd.to_datetime(sentinel_1C.date)\n",
    "sentinel_1C.loc[:,\"month\"] = sentinel_1C.date.dt.month\n",
    "sentinel_1C.loc[:,\"year\"] = sentinel_1C.date.dt.year\n",
    "\n",
    "sentinel_1C.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b312d87e-4b16-4fe4-a582-1680ca53bfbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sentinel_1C[[\"date\",\"B1\"]].groupby([sentinel_1C[\"date\"].dt.month]).count()[\"B1\"].plot(kind=\"bar\", figsize=(10,4), title=\"sentinel_1C Monthly Capture\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "d8873849-f847-4f69-aa15-4ca172d8333f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b1 = sentinel_1C[[\"date\",\"NDVI\"]]\n",
    "b1 = b1.dropna().reset_index().drop(columns={\"index\"})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,6))\n",
    "ax.plot(b1.groupby('date').mean(), '.')\n",
    "plt.title(\"NDVI Temporal Trend - Sentinel 1C\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7989436d-0a4e-4712-894b-117dea574a1b",
   "metadata": {},
   "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>date</th>\n",
       "      <th>NDVI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01 16:01:22</td>\n",
       "      <td>0.029604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-01 16:01:22</td>\n",
       "      <td>0.046086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-01 16:01:22</td>\n",
       "      <td>0.057304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-01 16:01:22</td>\n",
       "      <td>0.041464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-01 16:01:22</td>\n",
       "      <td>0.053054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239397</th>\n",
       "      <td>2020-12-28 15:51:45</td>\n",
       "      <td>-0.011546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239398</th>\n",
       "      <td>2020-12-28 15:51:45</td>\n",
       "      <td>0.027745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239399</th>\n",
       "      <td>2020-12-28 15:51:45</td>\n",
       "      <td>-0.021700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239400</th>\n",
       "      <td>2020-12-28 15:51:45</td>\n",
       "      <td>-0.004148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2239401</th>\n",
       "      <td>2020-12-28 15:51:45</td>\n",
       "      <td>-0.025070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2239402 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       date      NDVI\n",
       "0       2018-01-01 16:01:22  0.029604\n",
       "1       2018-01-01 16:01:22  0.046086\n",
       "2       2018-01-01 16:01:22  0.057304\n",
       "3       2018-01-01 16:01:22  0.041464\n",
       "4       2018-01-01 16:01:22  0.053054\n",
       "...                     ...       ...\n",
       "2239397 2020-12-28 15:51:45 -0.011546\n",
       "2239398 2020-12-28 15:51:45  0.027745\n",
       "2239399 2020-12-28 15:51:45 -0.021700\n",
       "2239400 2020-12-28 15:51:45 -0.004148\n",
       "2239401 2020-12-28 15:51:45 -0.025070\n",
       "\n",
       "[2239402 rows x 2 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bf35305e-382a-42dd-bb33-dfd7fcc5211d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAD4CAYAAAAZ1BptAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAUp0lEQVR4nO3dbYyd5Z3f8e+vdkjYbBMgrEaRTWuqWLtyYLObjIBVqmoELZgQxbxIIhAtJkWxqpBdtqLKmu0L1CRIoLZLgpREsoKLiaIQyqbFWsiyFmGU9gUECGkIsJQpgcUWD7sxD+ukgU7674tzeTn1zGXjc4aZ8cz3Ix3Nff/v67rvay6N5zf3wzlOVSFJ0nz+3lIPQJK0fBkSkqQuQ0KS1GVISJK6DAlJUtfapR7AQjv55JNrw4YNI/X9+c9/zjvf+c6FHdAxzjmZn/Myl3My17E0Jw899NDfVNVvHFpfcSGxYcMGHnzwwZH6Tk9PMzU1tbADOsY5J/NzXuZyTuY6luYkyTPz1Y94uSnJziQvJvnJUO3fJ/nLJD9O8l+SnDC07eokM0meSHLeUH1zq80k2T5UPzXJ/a3+7STHtfrb2/pM275htG9dkjSqN3NP4mZg8yG1PcBpVfXbwP8ErgZIsgm4CHh/6/PVJGuSrAG+ApwPbAIubm0BrgduqKr3AS8Bl7f65cBLrX5DaydJWkRHDImq+j6w/5DaX1TVbFu9D1jflrcAt1bVa1X1U2AGOKO9Zqrqqap6HbgV2JIkwNnA7a3/LuDCoX3tasu3A+e09pKkRbIQTzf9S+C7bXkd8OzQtr2t1qu/B3h5KHAO1v+/fbXtr7T2kqRFMtaN6yT/FpgFvrkwwxl5HNuAbQATExNMT0+PtJ8DBw6M3Helck7m57zM5ZzMtRLmZOSQSHIZ8FHgnHrjUwL3AacMNVvfanTqPwNOSLK2nS0Mtz+4r71J1gLvbu3nqKodwA6AycnJGvVpgmPpSYTF4pzMz3mZyzmZayXMyUiXm5JsBj4HfKyqfjG0aTdwUXsy6VRgI/AD4AFgY3uS6TgGN7d3t3C5F/h4678VuGNoX1vb8seB75UfWStJi+qIZxJJvgVMAScn2Qtcw+BpprcDe9q95Puq6l9V1aNJbgMeY3AZ6oqq+lXbz2eBu4E1wM6qerQd4o+AW5N8EXgYuKnVbwK+kWSGwY3zixbg+5UkHYUjhkRVXTxP+aZ5agfbXwtcO0/9LuCueepPMXj66dD6L4FPHGl8kqS3zop7x/WxasP2O0fu+/R1FyzgSCTpDX7AnySpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUdMSSS7EzyYpKfDNVOSrInyZPt64mtniQ3JplJ8uMkHxzqs7W1fzLJ1qH6h5I80vrcmCSHO4YkafG8mTOJm4HNh9S2A/dU1UbgnrYOcD6wsb22AV+DwS984BrgTOAM4JqhX/pfAz491G/zEY4hSVokRwyJqvo+sP+Q8hZgV1veBVw4VL+lBu4DTkjyXuA8YE9V7a+ql4A9wOa27V1VdV9VFXDLIfua7xiSpEWydsR+E1X1XFt+Hphoy+uAZ4fa7W21w9X3zlM/3DHmSLKNwZkLExMTTE9PH+W3M3DgwIGR+47rqtNnR+77Vo55KedkOXNe5nJO5loJczJqSPydqqoktRCDGfUYVbUD2AEwOTlZU1NTIx1nenqaUfuO67Ltd47c9+lLphZuIIdYyjlZzpyXuZyTuVbCnIz6dNML7VIR7euLrb4POGWo3fpWO1x9/Tz1wx1DkrRIRg2J3cDBJ5S2AncM1S9tTzmdBbzSLhndDZyb5MR2w/pc4O627dUkZ7Wnmi49ZF/zHUOStEiOeLkpybeAKeDkJHsZPKV0HXBbksuBZ4BPtuZ3AR8BZoBfAJ8CqKr9Sb4APNDafb6qDt4M/wyDJ6iOB77bXhzmGJKkRXLEkKiqizubzpmnbQFXdPazE9g5T/1B4LR56j+b7xiSpMXjO64lSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpa6yQSPKvkzya5CdJvpXkHUlOTXJ/kpkk305yXGv79rY+07ZvGNrP1a3+RJLzhuqbW20myfZxxipJOnojh0SSdcAfAJNVdRqwBrgIuB64oareB7wEXN66XA681Oo3tHYk2dT6vR/YDHw1yZoka4CvAOcDm4CLW1tJ0iIZ93LTWuD4JGuBXwOeA84Gbm/bdwEXtuUtbZ22/ZwkafVbq+q1qvopMAOc0V4zVfVUVb0O3NraSpIWydpRO1bVviT/Afgr4H8DfwE8BLxcVbOt2V5gXVteBzzb+s4meQV4T6vfN7Tr4T7PHlI/c76xJNkGbAOYmJhgenp6pO/pwIEDI/cd11Wnzx65UcdbOealnJPlzHmZyzmZayXMycghkeREBn/Znwq8DPxnBpeLFl1V7QB2AExOTtbU1NRI+5menmbUvuO6bPudI/d9+pKphRvIIZZyTpYz52Uu52SulTAn41xu+qfAT6vqr6vq/wDfAT4MnNAuPwGsB/a15X3AKQBt+7uBnw3XD+nTq0uSFsk4IfFXwFlJfq3dWzgHeAy4F/h4a7MVuKMt727rtO3fq6pq9Yva00+nAhuBHwAPABvb01LHMbi5vXuM8UqSjtI49yTuT3I78ENgFniYwSWfO4Fbk3yx1W5qXW4CvpFkBtjP4Jc+VfVoktsYBMwscEVV/QogyWeBuxk8ObWzqh4ddbySpKM3ckgAVNU1wDWHlJ9i8GTSoW1/CXyis59rgWvnqd8F3DXOGCVJo/Md15KkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldYz0CqzdsGONjNSRpufJMQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6horJJKckOT2JH+Z5PEkv5fkpCR7kjzZvp7Y2ibJjUlmkvw4yQeH9rO1tX8yydah+oeSPNL63Jgk44xXknR0xj2T+DLw51X1W8AHgMeB7cA9VbURuKetA5wPbGyvbcDXAJKcBFwDnAmcAVxzMFham08P9ds85nglSUdh5JBI8m7gnwA3AVTV61X1MrAF2NWa7QIubMtbgFtq4D7ghCTvBc4D9lTV/qp6CdgDbG7b3lVV91VVAbcM7UuStAjWjtH3VOCvgf+U5APAQ8CVwERVPdfaPA9MtOV1wLND/fe22uHqe+epz5FkG4OzEyYmJpienh7pGzpw4MDIfa86fXakfgth1DG/GePMyUrmvMzlnMy1EuZknJBYC3wQ+P2quj/Jl3nj0hIAVVVJapwBvhlVtQPYATA5OVlTU1Mj7Wd6eppR+162/c6R+i2IR34+ctenr7vgsNvHmZOVzHmZyzmZayXMyTj3JPYCe6vq/rZ+O4PQeKFdKqJ9fbFt3wecMtR/fasdrr5+nrokaZGMHBJV9TzwbJLfbKVzgMeA3cDBJ5S2Ane05d3Ape0pp7OAV9plqbuBc5Oc2G5Ynwvc3ba9muSs9lTTpUP7kiQtgnEuNwH8PvDNJMcBTwGfYhA8tyW5HHgG+GRrexfwEWAG+EVrS1XtT/IF4IHW7vNVtb8tfwa4GTge+G57SZIWyVghUVU/Aibn2XTOPG0LuKKzn53AznnqDwKnjTNGSdLofMe1JKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdY0dEknWJHk4yZ+19VOT3J9kJsm3kxzX6m9v6zNt+4ahfVzd6k8kOW+ovrnVZpJsH3eskqSjsxBnElcCjw+tXw/cUFXvA14CLm/1y4GXWv2G1o4km4CLgPcDm4GvtuBZA3wFOB/YBFzc2kqSFslYIZFkPXAB8PW2HuBs4PbWZBdwYVve0tZp289p7bcAt1bVa1X1U2AGOKO9Zqrqqap6Hbi1tZUkLZK1Y/b/EvA54O+39fcAL1fVbFvfC6xry+uAZwGqajbJK639OuC+oX0O93n2kPqZ8w0iyTZgG8DExATT09MjfTMHDhwYue9Vp88eudEydKTvd5w5Wcmcl7mck7lWwpyMHBJJPgq8WFUPJZlasBGNoKp2ADsAJicna2pqtOFMT08zat/Ltt85Ur+l9vQlU4fdPs6crGTOy1zOyVwrYU7GOZP4MPCxJB8B3gG8C/gycEKSte1sYj2wr7XfB5wC7E2yFng38LOh+kHDfXp1SdIiGPmeRFVdXVXrq2oDgxvP36uqS4B7gY+3ZluBO9ry7rZO2/69qqpWv6g9/XQqsBH4AfAAsLE9LXVcO8buUccrSTp6496TmM8fAbcm+SLwMHBTq98EfCPJDLCfwS99qurRJLcBjwGzwBVV9SuAJJ8F7gbWADur6tG3YLyr2oYjXCa76vTZ7qW0p6+74K0YkqRlZEFCoqqmgem2/BSDJ5MObfNL4BOd/tcC185Tvwu4ayHGKEk6er7jWpLUZUhIkroMCUlSlyEhSeoyJCRJXYaEJKnLkJAkdRkSkqQuQ0KS1GVISJK6DAlJUpchIUnqMiQkSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVKXISFJ6jIkJEldhoQkqcuQkCR1GRKSpC5DQpLUZUhIkrpGDokkpyS5N8ljSR5NcmWrn5RkT5In29cTWz1Jbkwyk+THST44tK+trf2TSbYO1T+U5JHW58YkGeeblSQdnXHOJGaBq6pqE3AWcEWSTcB24J6q2gjc09YBzgc2ttc24GswCBXgGuBM4AzgmoPB0tp8eqjf5jHGK0k6SiOHRFU9V1U/bMt/CzwOrAO2ALtas13AhW15C3BLDdwHnJDkvcB5wJ6q2l9VLwF7gM1t27uq6r6qKuCWoX1JkhbB2oXYSZINwO8C9wMTVfVc2/Q8MNGW1wHPDnXb22qHq++dpz7f8bcxODthYmKC6enpkb6PAwcOjNz3qtNnR+q33E0c3//eRp2rlWCcn5WVyjmZayXMydghkeTXgT8F/rCqXh2+bVBVlaTGPcaRVNUOYAfA5ORkTU1NjbSf6elpRu172fY7R+q33F11+iz/8ZH5f0yevmRqcQezjIzzs7JSOSdzrYQ5GevppiRvYxAQ36yq77TyC+1SEe3ri62+DzhlqPv6Vjtcff08dUnSIhnn6aYANwGPV9WfDG3aDRx8QmkrcMdQ/dL2lNNZwCvtstTdwLlJTmw3rM8F7m7bXk1yVjvWpUP7kiQtgnEuN30Y+BfAI0l+1Gp/DFwH3JbkcuAZ4JNt213AR4AZ4BfApwCqan+SLwAPtHafr6r9bfkzwM3A8cB320uStEhGDomq+u9A730L58zTvoArOvvaCeycp/4gcNqoY5Qkjcd3XEuSugwJSVKXISFJ6jIkJEldhoQkqWtBPpZDq9OGMd5l/vR1FyzgSCS9VTyTkCR1GRKSpC5DQpLUZUhIkroMCUlSl083DXlk3ysr9v+FkKRReCYhSeoyJCRJXV5u0pIY54144JvxpMXimYQkqcuQkCR1GRKSpC5DQpLUZUhIkrp8uknHJD+mXFocnklIkro8k9Cq41mI9OZ5JiFJ6jIkJEldhoQkqcuQkCR1Lfsb10k2A18G1gBfr6rrlnhIWsUO3vS+6vTZo/6/R7zprWPRsj6TSLIG+ApwPrAJuDjJpqUdlSStHsv9TOIMYKaqngJIciuwBXhsSUcljcCPR9exaLmHxDrg2aH1vcCZhzZKsg3Y1lYPJHlixOOdDPzNiH1XpD9wTua1FPOS6xfzaCPxZ2WuY2lO/uF8xeUeEm9KVe0Adoy7nyQPVtXkAgxpxXBO5ue8zOWczLUS5mRZ35MA9gGnDK2vbzVJ0iJY7iHxALAxyalJjgMuAnYv8ZgkadVY1pebqmo2yWeBuxk8Aruzqh59Cw859iWrFcg5mZ/zMpdzMtcxPyepqqUegyRpmVrul5skSUvIkJAkdRkSTZLNSZ5IMpNk+1KPZykk2ZnkxSQ/GaqdlGRPkifb1xOXcoyLLckpSe5N8liSR5Nc2eqrdl6SvCPJD5L8jzYn/67VT01yf/s39O32sMmqkmRNkoeT/FlbP+bnxJDAj/8YcjOw+ZDaduCeqtoI3NPWV5NZ4Kqq2gScBVzRfjZW87y8BpxdVR8AfgfYnOQs4Hrghqp6H/AScPnSDXHJXAk8PrR+zM+JITHwdx//UVWvAwc//mNVqarvA/sPKW8BdrXlXcCFizmmpVZVz1XVD9vy3zL4BbCOVTwvNXCgrb6tvQo4G7i91VfVnAAkWQ9cAHy9rYcVMCeGxMB8H/+xbonGstxMVNVzbfl5YGIpB7OUkmwAfhe4n1U+L+2yyo+AF4E9wP8CXq6q2dZkNf4b+hLwOeD/tvX3sALmxJDQm1aD56VX5TPTSX4d+FPgD6vq1eFtq3FequpXVfU7DD4F4Qzgt5Z2REsryUeBF6vqoaUey0Jb1m+mW0R+/EffC0neW1XPJXkvg78cV5Ukb2MQEN+squ+08qqfF4CqejnJvcDvASckWdv+cl5t/4Y+DHwsyUeAdwDvYvD/4Bzzc+KZxIAf/9G3G9jalrcCdyzhWBZdu658E/B4Vf3J0KZVOy9JfiPJCW35eOCfMbhXcy/w8dZsVc1JVV1dVeuragOD3x/fq6pLWAFz4juum/YXwJd44+M/rl3aES2+JN8Cphh8vPELwDXAfwVuA/4B8Azwyao69Ob2ipXkHwP/DXiEN641/zGD+xKrcl6S/DaDm7BrGPyheVtVfT7JP2Lw0MdJwMPAP6+q15ZupEsjyRTwb6rqoythTgwJSVKXl5skSV2GhCSpy5CQJHUZEpKkLkNCktRlSEiSugwJSVLX/wO7fOOymqp8RQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sentinel_1C.groupby(['bbl','bin','year','month']).count()['NDVI'].hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f2d9c9c4-0521-4042-879d-201503e200e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "s1c_counts = sentinel_1C.groupby(['bbl','bin','year','month']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "2281683f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9250"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# so somehow everything is included here\n",
    "len((sentinel_1C['bbl'] + sentinel_1C['bin']).unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bcd34d43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "333000"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len((sentinel_1C['bbl'] + sentinel_1C['bin']).unique()) * 3 * 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "009e08f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[40, 44, 48, 50, 52, 56]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(s1c_counts.loc[:,'date'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c4ef8e04-4da4-4fb2-9dec-bf903ab4185f",
   "metadata": {},
   "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>bbl</th>\n",
       "      <th>bin</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>1C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000020001</td>\n",
       "      <td>1085792</td>\n",
       "      <td>2018</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000020001</td>\n",
       "      <td>1085792</td>\n",
       "      <td>2018</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000020001</td>\n",
       "      <td>1085792</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000020001</td>\n",
       "      <td>1085792</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000020001</td>\n",
       "      <td>1085792</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332995</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332996</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332997</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332998</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332999</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>333000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               bbl      bin  year  month  1C\n",
       "0       1000020001  1085792  2018      1   1\n",
       "1       1000020001  1085792  2018      2   1\n",
       "2       1000020001  1085792  2018      3   1\n",
       "3       1000020001  1085792  2018      4   1\n",
       "4       1000020001  1085792  2018      5   1\n",
       "...            ...      ...   ...    ...  ..\n",
       "332995  5079910100  5088569  2020      8   1\n",
       "332996  5079910100  5088569  2020      9   1\n",
       "332997  5079910100  5088569  2020     10   1\n",
       "332998  5079910100  5088569  2020     11   1\n",
       "332999  5079910100  5088569  2020     12   1\n",
       "\n",
       "[333000 rows x 5 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_1C_monthly_bool = (s1c_counts > 0).reset_index()[['bbl','bin','year','month','NDVI']]\n",
    "sentinel_1C_monthly_bool['NDVI'] = sentinel_1C_monthly_bool['NDVI'].astype(int)\n",
    "sentinel_1C_monthly_bool = sentinel_1C_monthly_bool.rename(columns={\"NDVI\":\"1C\"})\n",
    "sentinel_1C_monthly_bool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "2f631fb9-57b4-4733-bf09-063ee8522a9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999981981981982"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_1C_monthly_bool['1C'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "56b1a48e-1a24-401a-bf0b-b4493bb3630b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentinel_1C_footcounts_monthly = pd.merge(footprints_ll, sentinel_1C_monthly_bool, left_on=[\"bbl\",\"bin\"], right_on=[\"bbl\",\"bin\"])\n",
    "sentinel_1C_footcounts_monthly.to_csv(os.path.join(destination_dir, \"sentinel_1C_counts_monthly.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4bf5b9ed-ec74-4e18-b1c9-684142488095",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e1f7c7c5-d7aa-4da5-a117-ab318e264a3e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4f881504-501c-494c-9ade-b51c5be49d05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bbl</th>\n",
       "      <th>bin</th>\n",
       "      <th>date</th>\n",
       "      <th>B1</th>\n",
       "      <th>B2</th>\n",
       "      <th>B3</th>\n",
       "      <th>B4</th>\n",
       "      <th>B5</th>\n",
       "      <th>B6</th>\n",
       "      <th>B7</th>\n",
       "      <th>B8</th>\n",
       "      <th>B8A</th>\n",
       "      <th>B9</th>\n",
       "      <th>B11</th>\n",
       "      <th>B12</th>\n",
       "      <th>NDVI</th>\n",
       "      <th>month</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1007640007</td>\n",
       "      <td>1013712</td>\n",
       "      <td>2018-12-14 15:51:57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3050830044</td>\n",
       "      <td>3116615</td>\n",
       "      <td>2018-12-14 15:51:57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2033180015</td>\n",
       "      <td>2017499</td>\n",
       "      <td>2018-12-14 15:51:57</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          bbl      bin                date  B1  B2  B3  B4  B5  B6  B7  B8  \\\n",
       "0  1007640007  1013712 2018-12-14 15:51:57 NaN NaN NaN NaN NaN NaN NaN NaN   \n",
       "1  3050830044  3116615 2018-12-14 15:51:57 NaN NaN NaN NaN NaN NaN NaN NaN   \n",
       "2  2033180015  2017499 2018-12-14 15:51:57 NaN NaN NaN NaN NaN NaN NaN NaN   \n",
       "\n",
       "   B8A  B9  B11  B12  NDVI  month  year  \n",
       "0  NaN NaN  NaN  NaN   NaN     12  2018  \n",
       "1  NaN NaN  NaN  NaN   NaN     12  2018  \n",
       "2  NaN NaN  NaN  NaN   NaN     12  2018  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_2A = pd.read_csv(sentinel_2A_path)\n",
    "\n",
    "sentinel_2A.loc[:,\"bbl\"] = sentinel_2A.loc[:,\"bbl\"].astype(str)\n",
    "sentinel_2A.loc[:,\"bin\"] = sentinel_2A.loc[:,\"bin\"].astype(str)\n",
    "\n",
    "sentinel_2A.loc[:,\"date\"] = pd.to_datetime(sentinel_2A.date)\n",
    "sentinel_2A.loc[:,\"month\"] = sentinel_2A.date.dt.month\n",
    "sentinel_2A.loc[:,\"year\"] = sentinel_2A.date.dt.year\n",
    "\n",
    "sentinel_2A.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b6cd690e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "333000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len((sentinel_2A['bbl'] + sentinel_2A['bin']).unique()) * 3 * 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "2c358c42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "231250"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s2a_counts = sentinel_2A.groupby(['bbl','bin','year','month']).count()\n",
    "len(s2a_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d3bf1016-f7f2-41ac-a4b0-6e816c80b6a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000020001</td>\n",
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       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000020001</td>\n",
       "      <td>1085792</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000020001</td>\n",
       "      <td>1085792</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000020001</td>\n",
       "      <td>1085792</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000020001</td>\n",
       "      <td>1085792</td>\n",
       "      <td>2019</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231245</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231246</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>2020</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231247</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>2020</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231248</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>2020</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231249</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>231250 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               bbl      bin  year  month  2A\n",
       "0       1000020001  1085792  2018     12   1\n",
       "1       1000020001  1085792  2019      1   1\n",
       "2       1000020001  1085792  2019      2   0\n",
       "3       1000020001  1085792  2019      3   1\n",
       "4       1000020001  1085792  2019      4   1\n",
       "...            ...      ...   ...    ...  ..\n",
       "231245  5079910100  5088569  2020      8   1\n",
       "231246  5079910100  5088569  2020      9   1\n",
       "231247  5079910100  5088569  2020     10   1\n",
       "231248  5079910100  5088569  2020     11   0\n",
       "231249  5079910100  5088569  2020     12   0\n",
       "\n",
       "[231250 rows x 5 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_2A_monthly_bool = (sentinel_2A.groupby(['bbl','bin','year','month']).count() > 0).reset_index()[['bbl','bin','year','month','NDVI']]\n",
    "sentinel_2A_monthly_bool['NDVI'] = sentinel_2A_monthly_bool['NDVI'].astype(int)\n",
    "sentinel_2A_monthly_bool = sentinel_2A_monthly_bool.rename(columns={\"NDVI\":\"2A\"})\n",
    "sentinel_2A_monthly_bool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "ed586f3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentinel_total_bool = sentinel_1C_monthly_bool.merge(\n",
    "    sentinel_2A_monthly_bool, \n",
    "    left_on=[\"bbl\",\"bin\",\"year\",\"month\"],\n",
    "    right_on=[\"bbl\",\"bin\",\"year\",\"month\"],\n",
    "    how=\"left\"\n",
    ").fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5798c632",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000020001</td>\n",
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       "      <td>2018</td>\n",
       "      <td>5</td>\n",
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       "      <td>2020</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
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       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
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       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>332998</th>\n",
       "      <td>5079910100</td>\n",
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       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>332999</th>\n",
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       "      <td>5088569</td>\n",
       "      <td>2020</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
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       "  </tbody>\n",
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       "<p>333000 rows × 6 columns</p>\n",
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      ],
      "text/plain": [
       "               bbl      bin  year  month  1C   2A\n",
       "0       1000020001  1085792  2018      1   1  0.0\n",
       "1       1000020001  1085792  2018      2   1  0.0\n",
       "2       1000020001  1085792  2018      3   1  0.0\n",
       "3       1000020001  1085792  2018      4   1  0.0\n",
       "4       1000020001  1085792  2018      5   1  0.0\n",
       "...            ...      ...   ...    ...  ..  ...\n",
       "332995  5079910100  5088569  2020      8   1  1.0\n",
       "332996  5079910100  5088569  2020      9   1  1.0\n",
       "332997  5079910100  5088569  2020     10   1  1.0\n",
       "332998  5079910100  5088569  2020     11   1  0.0\n",
       "332999  5079910100  5088569  2020     12   1  0.0\n",
       "\n",
       "[333000 rows x 6 columns]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_total_bool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1396d715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.999981981981982"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_total_bool['1C'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "202672cf-516a-44f2-a7ce-583b557edf41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.17928528528528528"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_total_bool['2A'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e33ed873-e537-4b47-96a9-48e4e7a69e11",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentinel_footcounts_monthly = pd.merge(footprints_ll, sentinel_total_bool, left_on=[\"bbl\",\"bin\"], right_on=[\"bbl\",\"bin\"])\n",
    "sentinel_footcounts_monthly.to_csv(os.path.join(destination_dir, \"sentinel_counts_monthly.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29ddb6e5-3a64-4902-9455-d4173dc42cae",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "28e8542f-d681-484f-bc13-ec99f5f22fea",
   "metadata": {},
   "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></th>\n",
       "      <th>B1</th>\n",
       "      <th>B2</th>\n",
       "      <th>B3</th>\n",
       "      <th>B4</th>\n",
       "      <th>B5</th>\n",
       "      <th>B6</th>\n",
       "      <th>B7</th>\n",
       "      <th>B8</th>\n",
       "      <th>B8A</th>\n",
       "      <th>B9</th>\n",
       "      <th>B11</th>\n",
       "      <th>B12</th>\n",
       "      <th>NDVI</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <th>12</th>\n",
       "      <td>366.890296</td>\n",
       "      <td>474.257977</td>\n",
       "      <td>669.791987</td>\n",
       "      <td>713.175710</td>\n",
       "      <td>1105.460265</td>\n",
       "      <td>1788.623335</td>\n",
       "      <td>2014.399389</td>\n",
       "      <td>2271.544527</td>\n",
       "      <td>2263.221912</td>\n",
       "      <td>2145.778932</td>\n",
       "      <td>1871.699570</td>\n",
       "      <td>1274.378281</td>\n",
       "      <td>0.517861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2019</th>\n",
       "      <th>1</th>\n",
       "      <td>283.669683</td>\n",
       "      <td>400.008886</td>\n",
       "      <td>617.465586</td>\n",
       "      <td>689.473221</td>\n",
       "      <td>1047.557866</td>\n",
       "      <td>1690.233044</td>\n",
       "      <td>1911.793309</td>\n",
       "      <td>2184.571110</td>\n",
       "      <td>2172.754187</td>\n",
       "      <td>2067.139284</td>\n",
       "      <td>1909.465115</td>\n",
       "      <td>1337.823091</td>\n",
       "      <td>0.519557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>128.061669</td>\n",
       "      <td>311.512021</td>\n",
       "      <td>564.544378</td>\n",
       "      <td>694.306952</td>\n",
       "      <td>1098.241998</td>\n",
       "      <td>1725.436658</td>\n",
       "      <td>1967.199268</td>\n",
       "      <td>2342.863524</td>\n",
       "      <td>2327.722801</td>\n",
       "      <td>2229.458386</td>\n",
       "      <td>2135.347196</td>\n",
       "      <td>1474.833077</td>\n",
       "      <td>0.550262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>457.725369</td>\n",
       "      <td>546.913049</td>\n",
       "      <td>791.604955</td>\n",
       "      <td>916.170830</td>\n",
       "      <td>1341.641245</td>\n",
       "      <td>2052.821389</td>\n",
       "      <td>2310.310244</td>\n",
       "      <td>2617.022706</td>\n",
       "      <td>2601.749515</td>\n",
       "      <td>2428.161018</td>\n",
       "      <td>2306.914060</td>\n",
       "      <td>1608.154181</td>\n",
       "      <td>0.479863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>587.736324</td>\n",
       "      <td>560.587508</td>\n",
       "      <td>808.992238</td>\n",
       "      <td>802.469543</td>\n",
       "      <td>1233.485397</td>\n",
       "      <td>1991.858313</td>\n",
       "      <td>2208.541357</td>\n",
       "      <td>2349.175411</td>\n",
       "      <td>2384.913770</td>\n",
       "      <td>2282.142064</td>\n",
       "      <td>1914.652529</td>\n",
       "      <td>1347.244218</td>\n",
       "      <td>0.490103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>799.206412</td>\n",
       "      <td>780.180970</td>\n",
       "      <td>987.765898</td>\n",
       "      <td>898.966353</td>\n",
       "      <td>1305.302635</td>\n",
       "      <td>2209.444291</td>\n",
       "      <td>2466.214536</td>\n",
       "      <td>2546.433208</td>\n",
       "      <td>2573.410041</td>\n",
       "      <td>2534.696143</td>\n",
       "      <td>1848.974430</td>\n",
       "      <td>1368.227850</td>\n",
       "      <td>0.484578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>885.754437</td>\n",
       "      <td>840.141623</td>\n",
       "      <td>1022.934260</td>\n",
       "      <td>957.348050</td>\n",
       "      <td>1327.254342</td>\n",
       "      <td>2404.851597</td>\n",
       "      <td>2770.517927</td>\n",
       "      <td>2820.544952</td>\n",
       "      <td>2894.564955</td>\n",
       "      <td>2844.862453</td>\n",
       "      <td>2037.530319</td>\n",
       "      <td>1468.383425</td>\n",
       "      <td>0.495806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>879.833092</td>\n",
       "      <td>833.048621</td>\n",
       "      <td>984.920866</td>\n",
       "      <td>914.361023</td>\n",
       "      <td>1256.584740</td>\n",
       "      <td>2238.497112</td>\n",
       "      <td>2615.106194</td>\n",
       "      <td>2592.702103</td>\n",
       "      <td>2757.936723</td>\n",
       "      <td>2673.779212</td>\n",
       "      <td>1952.010271</td>\n",
       "      <td>1374.325680</td>\n",
       "      <td>0.481857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>768.250755</td>\n",
       "      <td>727.865706</td>\n",
       "      <td>890.727666</td>\n",
       "      <td>836.190329</td>\n",
       "      <td>1154.991798</td>\n",
       "      <td>2069.986948</td>\n",
       "      <td>2390.644347</td>\n",
       "      <td>2443.112033</td>\n",
       "      <td>2524.118018</td>\n",
       "      <td>2483.409319</td>\n",
       "      <td>1769.139761</td>\n",
       "      <td>1248.967591</td>\n",
       "      <td>0.495334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>713.715490</td>\n",
       "      <td>674.111737</td>\n",
       "      <td>814.196549</td>\n",
       "      <td>762.241744</td>\n",
       "      <td>1063.607400</td>\n",
       "      <td>1859.509284</td>\n",
       "      <td>2135.481440</td>\n",
       "      <td>2204.291793</td>\n",
       "      <td>2277.310334</td>\n",
       "      <td>2245.968346</td>\n",
       "      <td>1608.457863</td>\n",
       "      <td>1118.698330</td>\n",
       "      <td>0.492314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>502.736591</td>\n",
       "      <td>513.561306</td>\n",
       "      <td>668.794393</td>\n",
       "      <td>642.032352</td>\n",
       "      <td>950.786251</td>\n",
       "      <td>1664.179621</td>\n",
       "      <td>1887.612383</td>\n",
       "      <td>2027.235478</td>\n",
       "      <td>2038.715091</td>\n",
       "      <td>2019.598156</td>\n",
       "      <td>1477.807547</td>\n",
       "      <td>1019.368519</td>\n",
       "      <td>0.523740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>444.718043</td>\n",
       "      <td>454.320010</td>\n",
       "      <td>609.939419</td>\n",
       "      <td>615.729910</td>\n",
       "      <td>949.681781</td>\n",
       "      <td>1569.479999</td>\n",
       "      <td>1766.565454</td>\n",
       "      <td>1952.115234</td>\n",
       "      <td>1957.527019</td>\n",
       "      <td>1925.262026</td>\n",
       "      <td>1487.039678</td>\n",
       "      <td>1000.262238</td>\n",
       "      <td>0.522517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>242.449353</td>\n",
       "      <td>389.011563</td>\n",
       "      <td>586.442197</td>\n",
       "      <td>657.550764</td>\n",
       "      <td>1028.405382</td>\n",
       "      <td>1709.297122</td>\n",
       "      <td>1937.935579</td>\n",
       "      <td>2230.793579</td>\n",
       "      <td>2197.096863</td>\n",
       "      <td>2149.018613</td>\n",
       "      <td>1860.272934</td>\n",
       "      <td>1263.094670</td>\n",
       "      <td>0.541726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2020</th>\n",
       "      <th>1</th>\n",
       "      <td>413.810836</td>\n",
       "      <td>511.282068</td>\n",
       "      <td>715.834322</td>\n",
       "      <td>787.863431</td>\n",
       "      <td>1187.223148</td>\n",
       "      <td>1881.465428</td>\n",
       "      <td>2118.888295</td>\n",
       "      <td>2373.900111</td>\n",
       "      <td>2388.117788</td>\n",
       "      <td>2332.425033</td>\n",
       "      <td>1936.813800</td>\n",
       "      <td>1317.079617</td>\n",
       "      <td>0.501726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>404.063544</td>\n",
       "      <td>466.894946</td>\n",
       "      <td>679.605220</td>\n",
       "      <td>761.009263</td>\n",
       "      <td>1125.918829</td>\n",
       "      <td>1801.077414</td>\n",
       "      <td>2033.657602</td>\n",
       "      <td>2319.821202</td>\n",
       "      <td>2276.733312</td>\n",
       "      <td>2055.915354</td>\n",
       "      <td>1950.089614</td>\n",
       "      <td>1366.611775</td>\n",
       "      <td>0.503563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>486.280060</td>\n",
       "      <td>541.476628</td>\n",
       "      <td>741.876787</td>\n",
       "      <td>789.708541</td>\n",
       "      <td>1167.490895</td>\n",
       "      <td>1928.590265</td>\n",
       "      <td>2169.580604</td>\n",
       "      <td>2397.559582</td>\n",
       "      <td>2370.537526</td>\n",
       "      <td>2249.818625</td>\n",
       "      <td>1996.871994</td>\n",
       "      <td>1408.981831</td>\n",
       "      <td>0.502504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>617.386307</td>\n",
       "      <td>645.725492</td>\n",
       "      <td>842.630819</td>\n",
       "      <td>817.858376</td>\n",
       "      <td>1240.701757</td>\n",
       "      <td>2051.814377</td>\n",
       "      <td>2291.116457</td>\n",
       "      <td>2503.185381</td>\n",
       "      <td>2460.819495</td>\n",
       "      <td>2338.837166</td>\n",
       "      <td>1895.283303</td>\n",
       "      <td>1367.885466</td>\n",
       "      <td>0.506692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>820.113960</td>\n",
       "      <td>776.650223</td>\n",
       "      <td>1031.386289</td>\n",
       "      <td>939.568603</td>\n",
       "      <td>1405.168395</td>\n",
       "      <td>2365.557775</td>\n",
       "      <td>2614.339888</td>\n",
       "      <td>2724.139266</td>\n",
       "      <td>2738.811863</td>\n",
       "      <td>2676.447986</td>\n",
       "      <td>1938.038740</td>\n",
       "      <td>1426.448760</td>\n",
       "      <td>0.491414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>972.933433</td>\n",
       "      <td>912.430905</td>\n",
       "      <td>1108.432414</td>\n",
       "      <td>1050.889557</td>\n",
       "      <td>1431.534176</td>\n",
       "      <td>2492.332551</td>\n",
       "      <td>2839.578617</td>\n",
       "      <td>2865.079933</td>\n",
       "      <td>2960.662066</td>\n",
       "      <td>2915.497594</td>\n",
       "      <td>2087.390349</td>\n",
       "      <td>1536.260696</td>\n",
       "      <td>0.469604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>893.827530</td>\n",
       "      <td>844.528755</td>\n",
       "      <td>1014.140276</td>\n",
       "      <td>964.895808</td>\n",
       "      <td>1306.156396</td>\n",
       "      <td>2282.685318</td>\n",
       "      <td>2638.801893</td>\n",
       "      <td>2617.588113</td>\n",
       "      <td>2767.617824</td>\n",
       "      <td>2690.182388</td>\n",
       "      <td>1998.634600</td>\n",
       "      <td>1450.491478</td>\n",
       "      <td>0.470019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>821.603035</td>\n",
       "      <td>772.791674</td>\n",
       "      <td>938.671734</td>\n",
       "      <td>879.272329</td>\n",
       "      <td>1211.786407</td>\n",
       "      <td>2151.255629</td>\n",
       "      <td>2492.119892</td>\n",
       "      <td>2494.471169</td>\n",
       "      <td>2636.228190</td>\n",
       "      <td>2576.269732</td>\n",
       "      <td>1873.665736</td>\n",
       "      <td>1320.903531</td>\n",
       "      <td>0.481946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>670.722037</td>\n",
       "      <td>654.048718</td>\n",
       "      <td>819.214433</td>\n",
       "      <td>756.222141</td>\n",
       "      <td>1074.540743</td>\n",
       "      <td>1970.965590</td>\n",
       "      <td>2272.288069</td>\n",
       "      <td>2374.202457</td>\n",
       "      <td>2429.863257</td>\n",
       "      <td>2380.927295</td>\n",
       "      <td>1665.475793</td>\n",
       "      <td>1135.152935</td>\n",
       "      <td>0.517057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>550.554917</td>\n",
       "      <td>520.668428</td>\n",
       "      <td>697.714654</td>\n",
       "      <td>663.259657</td>\n",
       "      <td>994.944337</td>\n",
       "      <td>1754.907130</td>\n",
       "      <td>1986.902999</td>\n",
       "      <td>2111.279765</td>\n",
       "      <td>2143.936007</td>\n",
       "      <td>2124.461894</td>\n",
       "      <td>1480.227893</td>\n",
       "      <td>1005.387183</td>\n",
       "      <td>0.525296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>409.479462</td>\n",
       "      <td>453.995583</td>\n",
       "      <td>629.649458</td>\n",
       "      <td>645.923401</td>\n",
       "      <td>1012.767636</td>\n",
       "      <td>1598.343539</td>\n",
       "      <td>1797.873926</td>\n",
       "      <td>1992.777087</td>\n",
       "      <td>2018.856458</td>\n",
       "      <td>1951.336330</td>\n",
       "      <td>1581.465597</td>\n",
       "      <td>1043.835495</td>\n",
       "      <td>0.511937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>398.427884</td>\n",
       "      <td>441.760766</td>\n",
       "      <td>669.325024</td>\n",
       "      <td>693.071578</td>\n",
       "      <td>1064.515142</td>\n",
       "      <td>1773.126654</td>\n",
       "      <td>1990.565854</td>\n",
       "      <td>2220.394320</td>\n",
       "      <td>2219.492735</td>\n",
       "      <td>2173.132468</td>\n",
       "      <td>1813.759991</td>\n",
       "      <td>1244.234397</td>\n",
       "      <td>0.525850</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    B1          B2           B3           B4           B5  \\\n",
       "year month                                                                  \n",
       "2018 12     366.890296  474.257977   669.791987   713.175710  1105.460265   \n",
       "2019 1      283.669683  400.008886   617.465586   689.473221  1047.557866   \n",
       "     2      128.061669  311.512021   564.544378   694.306952  1098.241998   \n",
       "     3      457.725369  546.913049   791.604955   916.170830  1341.641245   \n",
       "     4      587.736324  560.587508   808.992238   802.469543  1233.485397   \n",
       "     5      799.206412  780.180970   987.765898   898.966353  1305.302635   \n",
       "     6      885.754437  840.141623  1022.934260   957.348050  1327.254342   \n",
       "     7      879.833092  833.048621   984.920866   914.361023  1256.584740   \n",
       "     8      768.250755  727.865706   890.727666   836.190329  1154.991798   \n",
       "     9      713.715490  674.111737   814.196549   762.241744  1063.607400   \n",
       "     10     502.736591  513.561306   668.794393   642.032352   950.786251   \n",
       "     11     444.718043  454.320010   609.939419   615.729910   949.681781   \n",
       "     12     242.449353  389.011563   586.442197   657.550764  1028.405382   \n",
       "2020 1      413.810836  511.282068   715.834322   787.863431  1187.223148   \n",
       "     2      404.063544  466.894946   679.605220   761.009263  1125.918829   \n",
       "     3      486.280060  541.476628   741.876787   789.708541  1167.490895   \n",
       "     4      617.386307  645.725492   842.630819   817.858376  1240.701757   \n",
       "     5      820.113960  776.650223  1031.386289   939.568603  1405.168395   \n",
       "     6      972.933433  912.430905  1108.432414  1050.889557  1431.534176   \n",
       "     7      893.827530  844.528755  1014.140276   964.895808  1306.156396   \n",
       "     8      821.603035  772.791674   938.671734   879.272329  1211.786407   \n",
       "     9      670.722037  654.048718   819.214433   756.222141  1074.540743   \n",
       "     10     550.554917  520.668428   697.714654   663.259657   994.944337   \n",
       "     11     409.479462  453.995583   629.649458   645.923401  1012.767636   \n",
       "     12     398.427884  441.760766   669.325024   693.071578  1064.515142   \n",
       "\n",
       "                     B6           B7           B8          B8A           B9  \\\n",
       "year month                                                                    \n",
       "2018 12     1788.623335  2014.399389  2271.544527  2263.221912  2145.778932   \n",
       "2019 1      1690.233044  1911.793309  2184.571110  2172.754187  2067.139284   \n",
       "     2      1725.436658  1967.199268  2342.863524  2327.722801  2229.458386   \n",
       "     3      2052.821389  2310.310244  2617.022706  2601.749515  2428.161018   \n",
       "     4      1991.858313  2208.541357  2349.175411  2384.913770  2282.142064   \n",
       "     5      2209.444291  2466.214536  2546.433208  2573.410041  2534.696143   \n",
       "     6      2404.851597  2770.517927  2820.544952  2894.564955  2844.862453   \n",
       "     7      2238.497112  2615.106194  2592.702103  2757.936723  2673.779212   \n",
       "     8      2069.986948  2390.644347  2443.112033  2524.118018  2483.409319   \n",
       "     9      1859.509284  2135.481440  2204.291793  2277.310334  2245.968346   \n",
       "     10     1664.179621  1887.612383  2027.235478  2038.715091  2019.598156   \n",
       "     11     1569.479999  1766.565454  1952.115234  1957.527019  1925.262026   \n",
       "     12     1709.297122  1937.935579  2230.793579  2197.096863  2149.018613   \n",
       "2020 1      1881.465428  2118.888295  2373.900111  2388.117788  2332.425033   \n",
       "     2      1801.077414  2033.657602  2319.821202  2276.733312  2055.915354   \n",
       "     3      1928.590265  2169.580604  2397.559582  2370.537526  2249.818625   \n",
       "     4      2051.814377  2291.116457  2503.185381  2460.819495  2338.837166   \n",
       "     5      2365.557775  2614.339888  2724.139266  2738.811863  2676.447986   \n",
       "     6      2492.332551  2839.578617  2865.079933  2960.662066  2915.497594   \n",
       "     7      2282.685318  2638.801893  2617.588113  2767.617824  2690.182388   \n",
       "     8      2151.255629  2492.119892  2494.471169  2636.228190  2576.269732   \n",
       "     9      1970.965590  2272.288069  2374.202457  2429.863257  2380.927295   \n",
       "     10     1754.907130  1986.902999  2111.279765  2143.936007  2124.461894   \n",
       "     11     1598.343539  1797.873926  1992.777087  2018.856458  1951.336330   \n",
       "     12     1773.126654  1990.565854  2220.394320  2219.492735  2173.132468   \n",
       "\n",
       "                    B11          B12      NDVI  \n",
       "year month                                      \n",
       "2018 12     1871.699570  1274.378281  0.517861  \n",
       "2019 1      1909.465115  1337.823091  0.519557  \n",
       "     2      2135.347196  1474.833077  0.550262  \n",
       "     3      2306.914060  1608.154181  0.479863  \n",
       "     4      1914.652529  1347.244218  0.490103  \n",
       "     5      1848.974430  1368.227850  0.484578  \n",
       "     6      2037.530319  1468.383425  0.495806  \n",
       "     7      1952.010271  1374.325680  0.481857  \n",
       "     8      1769.139761  1248.967591  0.495334  \n",
       "     9      1608.457863  1118.698330  0.492314  \n",
       "     10     1477.807547  1019.368519  0.523740  \n",
       "     11     1487.039678  1000.262238  0.522517  \n",
       "     12     1860.272934  1263.094670  0.541726  \n",
       "2020 1      1936.813800  1317.079617  0.501726  \n",
       "     2      1950.089614  1366.611775  0.503563  \n",
       "     3      1996.871994  1408.981831  0.502504  \n",
       "     4      1895.283303  1367.885466  0.506692  \n",
       "     5      1938.038740  1426.448760  0.491414  \n",
       "     6      2087.390349  1536.260696  0.469604  \n",
       "     7      1998.634600  1450.491478  0.470019  \n",
       "     8      1873.665736  1320.903531  0.481946  \n",
       "     9      1665.475793  1135.152935  0.517057  \n",
       "     10     1480.227893  1005.387183  0.525296  \n",
       "     11     1581.465597  1043.835495  0.511937  \n",
       "     12     1813.759991  1244.234397  0.525850  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_2A.groupby(['year','month']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "90caa3a0-801a-4be4-a1fe-b4c35047f956",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sentinel_2A[[\"date\",\"B1\"]].groupby([sentinel_2A[\"date\"].dt.year, sentinel_2A[\"date\"].dt.month]).count()[\"B1\"].plot(kind=\"bar\", figsize=(20,6), title=\"sentinel_2A Monthly Capture\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f0cbc950-1e9c-44fe-ad0c-523cfbc50bda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is strange... I wonder if it has to do with cloud coverage over more urban regions during the winter months artificially expanding the count of vegetation during the winter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "f6643a93-83f6-4531-9340-9ffe29edfaf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sentinel_2A.groupby(['year','month']).median()['NDVI'].plot(kind=\"bar\", figsize=(20,6), title=\"sentinel_2A Monthly Capture\")\n",
    "plt.ylim(0.45,0.58)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "939c15cc-f9ef-44de-998d-0354b312b2ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year  month\n",
       "2018  1        0.044991\n",
       "      3        0.022640\n",
       "      4        0.031741\n",
       "      5        0.072324\n",
       "      6        0.092509\n",
       "      7        0.093968\n",
       "      8        0.089214\n",
       "      9        0.080866\n",
       "      10       0.069427\n",
       "      11       0.045017\n",
       "      12       0.044459\n",
       "2019  1        0.025248\n",
       "      2        0.023928\n",
       "      3        0.027523\n",
       "      4        0.045785\n",
       "      5        0.083605\n",
       "      6        0.090865\n",
       "      7        0.089257\n",
       "      8        0.086645\n",
       "      9        0.070669\n",
       "      10       0.057483\n",
       "      11       0.052246\n",
       "      12            NaN\n",
       "2020  1        0.026933\n",
       "      2        0.024057\n",
       "      3        0.025223\n",
       "      4        0.036072\n",
       "      5        0.065456\n",
       "      6        0.091405\n",
       "      7        0.088281\n",
       "      8        0.103481\n",
       "      9        0.078846\n",
       "      10       0.076061\n",
       "      11       0.036900\n",
       "      12       0.038644\n",
       "Name: NDVI, dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "landsat_ndvi = landsat.groupby(['year','month']).median()['NDVI']\n",
    "landsat_ndvi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e3a57d9a-5ae2-4771-9b88-14cc6837b16f",
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_function = 'mean'\n",
    "\n",
    "landsat_ndvi = landsat.groupby(['year','month']).agg(agg_function)['NDVI']\n",
    "sentinel_2A_ndvi = sentinel_2A.groupby(['year','month']).agg(agg_function)['NDVI']\n",
    "sentinel_1C_ndvi = sentinel_1C.groupby(['year','month']).agg(agg_function)['NDVI']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "e555a49d-f0a0-4b81-9362-804d58444189",
   "metadata": {},
   "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></th>\n",
       "      <th>Landsat</th>\n",
       "      <th>Sentinel-2A</th>\n",
       "      <th>Sentinel-1C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2018</th>\n",
       "      <th>1</th>\n",
       "      <td>0.055288</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.013353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.027354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.021937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.038148</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.043119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.082370</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.090463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.102195</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.123435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.101359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.125276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.096111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.104807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.090253</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.095416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.077940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.096403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.055541</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.039064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.053006</td>\n",
       "      <td>0.517861</td>\n",
       "      <td>-0.007548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2019</th>\n",
       "      <th>1</th>\n",
       "      <td>0.030748</td>\n",
       "      <td>0.519557</td>\n",
       "      <td>-0.011343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.028999</td>\n",
       "      <td>0.550262</td>\n",
       "      <td>0.009142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.031377</td>\n",
       "      <td>0.479863</td>\n",
       "      <td>0.020531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.054273</td>\n",
       "      <td>0.490103</td>\n",
       "      <td>0.040537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.092366</td>\n",
       "      <td>0.484578</td>\n",
       "      <td>0.109216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.099964</td>\n",
       "      <td>0.495806</td>\n",
       "      <td>0.135782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.097451</td>\n",
       "      <td>0.481857</td>\n",
       "      <td>0.108235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.094662</td>\n",
       "      <td>0.495334</td>\n",
       "      <td>0.117153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.078766</td>\n",
       "      <td>0.492314</td>\n",
       "      <td>0.092408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.066005</td>\n",
       "      <td>0.523740</td>\n",
       "      <td>0.098210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.060074</td>\n",
       "      <td>0.522517</td>\n",
       "      <td>0.059603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.541726</td>\n",
       "      <td>-0.000858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2020</th>\n",
       "      <th>1</th>\n",
       "      <td>0.034685</td>\n",
       "      <td>0.501726</td>\n",
       "      <td>-0.006939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.029801</td>\n",
       "      <td>0.503563</td>\n",
       "      <td>0.020358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.031294</td>\n",
       "      <td>0.502504</td>\n",
       "      <td>0.029957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.043578</td>\n",
       "      <td>0.506692</td>\n",
       "      <td>0.061380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.074755</td>\n",
       "      <td>0.491414</td>\n",
       "      <td>0.111556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.101092</td>\n",
       "      <td>0.469604</td>\n",
       "      <td>0.125992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.096143</td>\n",
       "      <td>0.470019</td>\n",
       "      <td>0.114650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.116467</td>\n",
       "      <td>0.481946</td>\n",
       "      <td>0.114027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.084108</td>\n",
       "      <td>0.517057</td>\n",
       "      <td>0.112188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.083718</td>\n",
       "      <td>0.525296</td>\n",
       "      <td>0.100834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.045257</td>\n",
       "      <td>0.511937</td>\n",
       "      <td>0.034091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.047888</td>\n",
       "      <td>0.525850</td>\n",
       "      <td>-0.005667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Landsat  Sentinel-2A  Sentinel-1C\n",
       "year month                                    \n",
       "2018 1      0.055288          NaN     0.001961\n",
       "     2           NaN          NaN     0.013353\n",
       "     3      0.027354          NaN     0.021937\n",
       "     4      0.038148          NaN     0.043119\n",
       "     5      0.082370          NaN     0.090463\n",
       "     6      0.102195          NaN     0.123435\n",
       "     7      0.101359          NaN     0.125276\n",
       "     8      0.096111          NaN     0.104807\n",
       "     9      0.090253          NaN     0.095416\n",
       "     10     0.077940          NaN     0.096403\n",
       "     11     0.055541          NaN     0.039064\n",
       "     12     0.053006     0.517861    -0.007548\n",
       "2019 1      0.030748     0.519557    -0.011343\n",
       "     2      0.028999     0.550262     0.009142\n",
       "     3      0.031377     0.479863     0.020531\n",
       "     4      0.054273     0.490103     0.040537\n",
       "     5      0.092366     0.484578     0.109216\n",
       "     6      0.099964     0.495806     0.135782\n",
       "     7      0.097451     0.481857     0.108235\n",
       "     8      0.094662     0.495334     0.117153\n",
       "     9      0.078766     0.492314     0.092408\n",
       "     10     0.066005     0.523740     0.098210\n",
       "     11     0.060074     0.522517     0.059603\n",
       "     12          NaN     0.541726    -0.000858\n",
       "2020 1      0.034685     0.501726    -0.006939\n",
       "     2      0.029801     0.503563     0.020358\n",
       "     3      0.031294     0.502504     0.029957\n",
       "     4      0.043578     0.506692     0.061380\n",
       "     5      0.074755     0.491414     0.111556\n",
       "     6      0.101092     0.469604     0.125992\n",
       "     7      0.096143     0.470019     0.114650\n",
       "     8      0.116467     0.481946     0.114027\n",
       "     9      0.084108     0.517057     0.112188\n",
       "     10     0.083718     0.525296     0.100834\n",
       "     11     0.045257     0.511937     0.034091\n",
       "     12     0.047888     0.525850    -0.005667"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_ndvi = pd.DataFrame({\"Landsat\": landsat_ndvi, \"Sentinel-2A\": sentinel_2A_ndvi, \"Sentinel-1C\": sentinel_1C_ndvi})\n",
    "monthly_ndvi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f412905a-8d3e-4b57-822a-13db0460aa23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "183.60035933576188\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2400x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "difference = 100 * ((monthly_ndvi['Landsat'] - monthly_ndvi['Sentinel-1C']) / monthly_ndvi['Sentinel-1C'])\n",
    "print(np.abs(difference).mean())\n",
    "\n",
    "plt.figure(figsize=(8, 2), dpi=300)\n",
    "difference.plot(kind='bar')\n",
    "plt.xticks(rotation=90, ha='center')\n",
    "plt.ylabel(\"Percent Deviation\")\n",
    "plt.ylim(-100,100)\n",
    "plt.title(\"NDVI Percent Deviation - Landsat8 vs. Sentinel2 - Level 1C\")\n",
    "plt.savefig('2d/landsat_sentinel_ndvi.png',bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "3e158b5a-3ec1-49e0-ae0f-aa4e848a44ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(difference, bins=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "6ed747ed-4ec6-42f0-bad8-67297e213c55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9393172241621476"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.abs((monthly_ndvi['Sentinel-1C'] / monthly_ndvi['Landsat'])).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "71e45893",
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_ndvi.reset_index().to_csv(\"monthly_ndvidata.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "311fc294-1603-472c-bddf-cccec31fce6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 1, figsize=(8,2), dpi=300)\n",
    "\n",
    "monthly_ndvi.plot(kind=\"bar\", ax=axs)\n",
    "plt.xticks(rotation=90, ha='right')\n",
    "plt.title(\"Monthly NDVI\")\n",
    "\n",
    "plt.savefig('2d/satellite_ndvi_comparison.png',bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "ad0bdd42-c4aa-45c4-b111-8f1db71b3f10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b1 = sentinel_1C[[\"date\",\"NDVI\"]]\n",
    "b1 = b1.dropna().reset_index().drop(columns={\"index\"})\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,6))\n",
    "ax.plot(b1.groupby('date').mean(), '.')\n",
    "plt.title(\"NDVI Temporal Trend - Sentinel 2A\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "47e7412a-716a-421d-a587-3fe78b2f2d22",
   "metadata": {},
   "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>date</th>\n",
       "      <th>NDVI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01 16:01:22</td>\n",
       "      <td>10696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03 15:46:51</td>\n",
       "      <td>2389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-06 15:56:30</td>\n",
       "      <td>10400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-13 15:46:08</td>\n",
       "      <td>9096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-16 16:01:06</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>2020-12-26 16:01:50</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>2020-12-26 16:02:01</td>\n",
       "      <td>1296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>2020-12-28 15:51:45</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>2020-12-28 15:51:48</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>2020-12-28 15:52:03</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>987 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   date   NDVI\n",
       "0   2018-01-01 16:01:22  10696\n",
       "1   2018-01-03 15:46:51   2389\n",
       "2   2018-01-06 15:56:30  10400\n",
       "3   2018-01-13 15:46:08   9096\n",
       "4   2018-01-16 16:01:06      3\n",
       "..                  ...    ...\n",
       "982 2020-12-26 16:01:50      8\n",
       "983 2020-12-26 16:02:01   1296\n",
       "984 2020-12-28 15:51:45     56\n",
       "985 2020-12-28 15:51:48    193\n",
       "986 2020-12-28 15:52:03    151\n",
       "\n",
       "[987 rows x 2 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b1_grouped = b1.groupby(b1.date).count().reset_index()\n",
    "b1_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "6f0d803a-62eb-4e7f-9246-41b556047a3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "b1_grouped['month'] = b1_grouped.date.dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "00f0acd9-aba8-4f7c-8824-c22fa82988ae",
   "metadata": {},
   "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>date</th>\n",
       "      <th>NDVI</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01 16:01:22</td>\n",
       "      <td>10696</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-03 15:46:51</td>\n",
       "      <td>2389</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-06 15:56:30</td>\n",
       "      <td>10400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-13 15:46:08</td>\n",
       "      <td>9096</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-16 16:01:06</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>2020-12-26 16:01:50</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>2020-12-26 16:02:01</td>\n",
       "      <td>1296</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>2020-12-28 15:51:45</td>\n",
       "      <td>56</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>2020-12-28 15:51:48</td>\n",
       "      <td>193</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>2020-12-28 15:52:03</td>\n",
       "      <td>151</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>987 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   date   NDVI  month\n",
       "0   2018-01-01 16:01:22  10696      1\n",
       "1   2018-01-03 15:46:51   2389      1\n",
       "2   2018-01-06 15:56:30  10400      1\n",
       "3   2018-01-13 15:46:08   9096      1\n",
       "4   2018-01-16 16:01:06      3      1\n",
       "..                  ...    ...    ...\n",
       "982 2020-12-26 16:01:50      8     12\n",
       "983 2020-12-26 16:02:01   1296     12\n",
       "984 2020-12-28 15:51:45     56     12\n",
       "985 2020-12-28 15:51:48    193     12\n",
       "986 2020-12-28 15:52:03    151     12\n",
       "\n",
       "[987 rows x 3 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b1_grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "2343a55c-2f9b-4e64-8617-9381c0646cdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5), dpi=100)\n",
    "b1_grouped['NDVI'].hist(bins=50, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f182f8e4-1182-412c-b08d-e694e3c042c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5), dpi=100)\n",
    "sns.kdeplot(\n",
    "    data=b1_grouped,\n",
    "    x=\"NDVI\",\n",
    "    hue=\"month\",\n",
    "    multiple=\"fill\",\n",
    "    palette=\"Spectral\",\n",
    "    ax=ax\n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "609203df-5f1b-4748-b419-52cab4e2635b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,5), dpi=200)\n",
    "ax.plot(b1_grouped['date'], b1_grouped['NDVI'], '.')\n",
    "plt.plot(title=\"Sentinel-2A Count by Day\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4981e82c-632d-445f-b8c2-a93332576f45",
   "metadata": {},
   "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>bbl</th>\n",
       "      <th>bin</th>\n",
       "      <th>date</th>\n",
       "      <th>B1</th>\n",
       "      <th>B2</th>\n",
       "      <th>B3</th>\n",
       "      <th>B4</th>\n",
       "      <th>B5</th>\n",
       "      <th>B6</th>\n",
       "      <th>B7</th>\n",
       "      <th>B8</th>\n",
       "      <th>B8A</th>\n",
       "      <th>B9</th>\n",
       "      <th>B11</th>\n",
       "      <th>B12</th>\n",
       "      <th>NDVI</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000020001</td>\n",
       "      <td>1085792</td>\n",
       "      <td>1194</td>\n",
       "      <td>108</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>109</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000080039</td>\n",
       "      <td>1078968</td>\n",
       "      <td>1194</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000080051</td>\n",
       "      <td>1090472</td>\n",
       "      <td>1194</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000090001</td>\n",
       "      <td>1000018</td>\n",
       "      <td>1194</td>\n",
       "      <td>112</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000090014</td>\n",
       "      <td>1000020</td>\n",
       "      <td>1194</td>\n",
       "      <td>112</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9245</th>\n",
       "      <td>5078410154</td>\n",
       "      <td>5087579</td>\n",
       "      <td>1194</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>67</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9246</th>\n",
       "      <td>5078940001</td>\n",
       "      <td>5088090</td>\n",
       "      <td>1194</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
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       "    <tr>\n",
       "      <th>9247</th>\n",
       "      <td>5078980001</td>\n",
       "      <td>5107854</td>\n",
       "      <td>1194</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>61</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
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       "    <tr>\n",
       "      <th>9248</th>\n",
       "      <td>5079710250</td>\n",
       "      <td>5107860</td>\n",
       "      <td>1194</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
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       "      <td>1194</td>\n",
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       "    <tr>\n",
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       "      <td>50</td>\n",
       "      <td>50</td>\n",
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       "      <td>50</td>\n",
       "      <td>50</td>\n",
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       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>9250 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             bbl      bin  date   B1   B2   B3   B4   B5   B6   B7   B8  B8A  \\\n",
       "0     1000020001  1085792  1194  108  109  109  109  109  109  109  109  109   \n",
       "1     1000080039  1078968  1194    0    0    0    0    0    0    0    0    0   \n",
       "2     1000080051  1090472  1194    0    0    0    0    0    0    0    0    0   \n",
       "3     1000090001  1000018  1194  112  113  113  113  113  113  113  113  113   \n",
       "4     1000090014  1000020  1194  112  113  113  113  113  113  113  113  113   \n",
       "...          ...      ...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "9245  5078410154  5087579  1194   67   67   67   67   67   67   67   67   67   \n",
       "9246  5078940001  5088090  1194   68   68   68   68   68   68   68   68   68   \n",
       "9247  5078980001  5107854  1194   61   61   61   61   61   61   61   61   61   \n",
       "9248  5079710250  5107860  1194   83   83   83   83   83   83   83   83   83   \n",
       "9249  5079910100  5088569  1194   50   50   50   50   50   50   50   50   50   \n",
       "\n",
       "       B9  B11  B12  NDVI  month  year  \n",
       "0     109  109  109   109   1194  1194  \n",
       "1       0    0    0     0   1194  1194  \n",
       "2       0    0    0     0   1194  1194  \n",
       "3     113  113  113   113   1194  1194  \n",
       "4     113  113  113   113   1194  1194  \n",
       "...   ...  ...  ...   ...    ...   ...  \n",
       "9245   67   67   67    67   1194  1194  \n",
       "9246   68   68   68    68   1194  1194  \n",
       "9247   61   61   61    61   1194  1194  \n",
       "9248   83   83   83    83   1194  1194  \n",
       "9249   50   50   50    50   1194  1194  \n",
       "\n",
       "[9250 rows x 18 columns]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_counts = sentinel_2A.groupby(['bbl','bin']).count().reset_index()\n",
    "sentinel_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "2c0b8e11-9f7f-4562-a774-4003ead0e925",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "             bbl      bin  percent_found\n",
       "0     1000020001  1085792       0.090452\n",
       "1     1000080039  1078968       0.000000\n",
       "2     1000080051  1090472       0.000000\n",
       "3     1000090001  1000018       0.093802\n",
       "4     1000090014  1000020       0.093802\n",
       "...          ...      ...            ...\n",
       "9245  5078410154  5087579       0.056114\n",
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       "9248  5079710250  5107860       0.069514\n",
       "9249  5079910100  5088569       0.041876\n",
       "\n",
       "[9250 rows x 3 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_counts['percent_found'] = sentinel_counts['B1'] / sentinel_counts['date']\n",
    "sentinel_percentages = sentinel_counts[['bbl','bin','percent_found']]\n",
    "sentinel_percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "868bac09-339d-423c-a89d-37f0a491c5fe",
   "metadata": {},
   "outputs": [
    {
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       "      <td>61</td>\n",
       "      <td>61</td>\n",
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       "      <td>61</td>\n",
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       "      <td>1194</td>\n",
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       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>0.069514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9249</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>1194</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>0.041876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9250 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             bbl      bin  date   B1   B2   B3   B4   B5   B6   B7   B8  B8A  \\\n",
       "0     1000020001  1085792  1194  108  109  109  109  109  109  109  109  109   \n",
       "1     1000080039  1078968  1194    0    0    0    0    0    0    0    0    0   \n",
       "2     1000080051  1090472  1194    0    0    0    0    0    0    0    0    0   \n",
       "3     1000090001  1000018  1194  112  113  113  113  113  113  113  113  113   \n",
       "4     1000090014  1000020  1194  112  113  113  113  113  113  113  113  113   \n",
       "...          ...      ...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "9245  5078410154  5087579  1194   67   67   67   67   67   67   67   67   67   \n",
       "9246  5078940001  5088090  1194   68   68   68   68   68   68   68   68   68   \n",
       "9247  5078980001  5107854  1194   61   61   61   61   61   61   61   61   61   \n",
       "9248  5079710250  5107860  1194   83   83   83   83   83   83   83   83   83   \n",
       "9249  5079910100  5088569  1194   50   50   50   50   50   50   50   50   50   \n",
       "\n",
       "       B9  B11  B12  NDVI  month  year  percent_found  \n",
       "0     109  109  109   109   1194  1194       0.090452  \n",
       "1       0    0    0     0   1194  1194       0.000000  \n",
       "2       0    0    0     0   1194  1194       0.000000  \n",
       "3     113  113  113   113   1194  1194       0.093802  \n",
       "4     113  113  113   113   1194  1194       0.093802  \n",
       "...   ...  ...  ...   ...    ...   ...            ...  \n",
       "9245   67   67   67    67   1194  1194       0.056114  \n",
       "9246   68   68   68    68   1194  1194       0.056951  \n",
       "9247   61   61   61    61   1194  1194       0.051089  \n",
       "9248   83   83   83    83   1194  1194       0.069514  \n",
       "9249   50   50   50    50   1194  1194       0.041876  \n",
       "\n",
       "[9250 rows x 19 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "773d070a-3d6b-48df-a830-53846f06b611",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5), dpi=100)\n",
    "sentinel_percentages.hist(bins=50, ax=ax)\n",
    "plt.ylim(0,500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "f4597381-b24a-4b24-8e8e-6dc7533c5b09",
   "metadata": {},
   "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>bbl</th>\n",
       "      <th>bin</th>\n",
       "      <th>id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>date</th>\n",
       "      <th>B1</th>\n",
       "      <th>B2</th>\n",
       "      <th>B3</th>\n",
       "      <th>B4</th>\n",
       "      <th>...</th>\n",
       "      <th>B7</th>\n",
       "      <th>B8</th>\n",
       "      <th>B8A</th>\n",
       "      <th>B9</th>\n",
       "      <th>B11</th>\n",
       "      <th>B12</th>\n",
       "      <th>NDVI</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>percent_found</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1007640007</td>\n",
       "      <td>1013712</td>\n",
       "      <td>31a2a</td>\n",
       "      <td>40.739758</td>\n",
       "      <td>-74.001778</td>\n",
       "      <td>1194</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3050830044</td>\n",
       "      <td>3116615</td>\n",
       "      <td>152a6</td>\n",
       "      <td>40.653385</td>\n",
       "      <td>-73.956779</td>\n",
       "      <td>1194</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2033180015</td>\n",
       "      <td>2017499</td>\n",
       "      <td>092e2</td>\n",
       "      <td>40.869700</td>\n",
       "      <td>-73.895017</td>\n",
       "      <td>1194</td>\n",
       "      <td>94</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>...</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>0.078727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3074040001</td>\n",
       "      <td>3202112</td>\n",
       "      <td>9b415</td>\n",
       "      <td>40.594550</td>\n",
       "      <td>-73.951343</td>\n",
       "      <td>1194</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>0.001675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3050580057</td>\n",
       "      <td>3116089</td>\n",
       "      <td>b753d</td>\n",
       "      <td>40.652215</td>\n",
       "      <td>-73.964713</td>\n",
       "      <td>1194</td>\n",
       "      <td>69</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>...</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>0.057789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9245</th>\n",
       "      <td>3017130007</td>\n",
       "      <td>3421590</td>\n",
       "      <td>8cbbd</td>\n",
       "      <td>40.677413</td>\n",
       "      <td>-73.923052</td>\n",
       "      <td>1194</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>0.042714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9246</th>\n",
       "      <td>1008280059</td>\n",
       "      <td>1015659</td>\n",
       "      <td>27520</td>\n",
       "      <td>40.744560</td>\n",
       "      <td>-73.989475</td>\n",
       "      <td>1194</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9247</th>\n",
       "      <td>2029410001</td>\n",
       "      <td>2114529</td>\n",
       "      <td>5830f</td>\n",
       "      <td>40.840495</td>\n",
       "      <td>-73.897780</td>\n",
       "      <td>1194</td>\n",
       "      <td>103</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>...</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>0.086265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9248</th>\n",
       "      <td>4098750001</td>\n",
       "      <td>4211112</td>\n",
       "      <td>2ec2a</td>\n",
       "      <td>40.717143</td>\n",
       "      <td>-73.794127</td>\n",
       "      <td>1194</td>\n",
       "      <td>125</td>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "      <td>...</td>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "      <td>126</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>0.104690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9249</th>\n",
       "      <td>2048190015</td>\n",
       "      <td>2062829</td>\n",
       "      <td>2bf8f</td>\n",
       "      <td>40.887201</td>\n",
       "      <td>-73.864459</td>\n",
       "      <td>1194</td>\n",
       "      <td>82</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>...</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>1194</td>\n",
       "      <td>1194</td>\n",
       "      <td>0.068677</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9250 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             bbl      bin     id   latitude  longitude  date   B1   B2   B3  \\\n",
       "0     1007640007  1013712  31a2a  40.739758 -74.001778  1194    0    0    0   \n",
       "1     3050830044  3116615  152a6  40.653385 -73.956779  1194    0    0    0   \n",
       "2     2033180015  2017499  092e2  40.869700 -73.895017  1194   94   95   95   \n",
       "3     3074040001  3202112  9b415  40.594550 -73.951343  1194    2    2    2   \n",
       "4     3050580057  3116089  b753d  40.652215 -73.964713  1194   69   70   70   \n",
       "...          ...      ...    ...        ...        ...   ...  ...  ...  ...   \n",
       "9245  3017130007  3421590  8cbbd  40.677413 -73.923052  1194   51   51   51   \n",
       "9246  1008280059  1015659  27520  40.744560 -73.989475  1194    0    0    0   \n",
       "9247  2029410001  2114529  5830f  40.840495 -73.897780  1194  103  104  104   \n",
       "9248  4098750001  4211112  2ec2a  40.717143 -73.794127  1194  125  126  126   \n",
       "9249  2048190015  2062829  2bf8f  40.887201 -73.864459  1194   82   83   83   \n",
       "\n",
       "       B4  ...   B7   B8  B8A   B9  B11  B12  NDVI  month  year  percent_found  \n",
       "0       0  ...    0    0    0    0    0    0     0   1194  1194       0.000000  \n",
       "1       0  ...    0    0    0    0    0    0     0   1194  1194       0.000000  \n",
       "2      95  ...   95   95   95   95   95   95    95   1194  1194       0.078727  \n",
       "3       2  ...    2    2    2    2    2    2     2   1194  1194       0.001675  \n",
       "4      70  ...   70   70   70   70   70   70    70   1194  1194       0.057789  \n",
       "...   ...  ...  ...  ...  ...  ...  ...  ...   ...    ...   ...            ...  \n",
       "9245   51  ...   51   51   51   51   51   51    51   1194  1194       0.042714  \n",
       "9246    0  ...    0    0    0    0    0    0     0   1194  1194       0.000000  \n",
       "9247  104  ...  104  104  104  104  104  104   104   1194  1194       0.086265  \n",
       "9248  126  ...  126  126  126  126  126  126   126   1194  1194       0.104690  \n",
       "9249   83  ...   83   83   83   83   83   83    83   1194  1194       0.068677  \n",
       "\n",
       "[9250 rows x 22 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel_footcounts = pd.merge(footprints_ll, sentinel_counts, left_on=[\"bbl\",\"bin\"], right_on=[\"bbl\",\"bin\"]).fillna(0)\n",
    "sentinel_footcounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "5d830c49-1800-43d7-ace4-0f0afa613da8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02646720086921092"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(sentinel_footcounts.percent_found)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "77806106-d6b9-4f84-a635-6b794570a910",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentinel_footcounts.to_csv(os.path.join(destination_dir, \"sentinel_counts.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "1e77b001-9062-4f0a-9eb8-0a604a4d7058",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bbl</th>\n",
       "      <th>bin</th>\n",
       "      <th>date</th>\n",
       "      <th>B1</th>\n",
       "      <th>B2</th>\n",
       "      <th>B3</th>\n",
       "      <th>B4</th>\n",
       "      <th>B5</th>\n",
       "      <th>B6</th>\n",
       "      <th>B7</th>\n",
       "      <th>B8</th>\n",
       "      <th>B8A</th>\n",
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       "      <th>B11</th>\n",
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       "      <th>NDVI</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>214</td>\n",
       "      <td>214</td>\n",
       "      <td>1738</td>\n",
       "      <td>1738</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000080039</td>\n",
       "      <td>1078968</td>\n",
       "      <td>1738</td>\n",
       "      <td>210</td>\n",
       "      <td>210</td>\n",
       "      <td>210</td>\n",
       "      <td>210</td>\n",
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       "      <td>210</td>\n",
       "      <td>210</td>\n",
       "      <td>210</td>\n",
       "      <td>210</td>\n",
       "      <td>210</td>\n",
       "      <td>210</td>\n",
       "      <td>1738</td>\n",
       "      <td>1738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000080051</td>\n",
       "      <td>1090472</td>\n",
       "      <td>1738</td>\n",
       "      <td>210</td>\n",
       "      <td>210</td>\n",
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       "      <td>210</td>\n",
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       "      <td>210</td>\n",
       "      <td>210</td>\n",
       "      <td>210</td>\n",
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       "      <td>210</td>\n",
       "      <td>210</td>\n",
       "      <td>1738</td>\n",
       "      <td>1738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000090001</td>\n",
       "      <td>1000018</td>\n",
       "      <td>1738</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>212</td>\n",
       "      <td>1738</td>\n",
       "      <td>1738</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000090014</td>\n",
       "      <td>1000020</td>\n",
       "      <td>1738</td>\n",
       "      <td>211</td>\n",
       "      <td>211</td>\n",
       "      <td>211</td>\n",
       "      <td>211</td>\n",
       "      <td>211</td>\n",
       "      <td>211</td>\n",
       "      <td>211</td>\n",
       "      <td>211</td>\n",
       "      <td>211</td>\n",
       "      <td>211</td>\n",
       "      <td>211</td>\n",
       "      <td>211</td>\n",
       "      <td>211</td>\n",
       "      <td>1738</td>\n",
       "      <td>1738</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>9245</th>\n",
       "      <td>5078410154</td>\n",
       "      <td>5087579</td>\n",
       "      <td>1738</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>202</td>\n",
       "      <td>1738</td>\n",
       "      <td>1738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9246</th>\n",
       "      <td>5078940001</td>\n",
       "      <td>5088090</td>\n",
       "      <td>1738</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>1738</td>\n",
       "      <td>1738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9247</th>\n",
       "      <td>5078980001</td>\n",
       "      <td>5107854</td>\n",
       "      <td>1738</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>1738</td>\n",
       "      <td>1738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9248</th>\n",
       "      <td>5079710250</td>\n",
       "      <td>5107860</td>\n",
       "      <td>1738</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>1738</td>\n",
       "      <td>1738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9249</th>\n",
       "      <td>5079910100</td>\n",
       "      <td>5088569</td>\n",
       "      <td>1738</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>204</td>\n",
       "      <td>1738</td>\n",
       "      <td>1738</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9250 rows × 18 columns</p>\n",
       "</div>"
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       "             bbl      bin  date   B1   B2   B3   B4   B5   B6   B7   B8  B8A  \\\n",
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       "1     1000080039  1078968  1738  210  210  210  210  210  210  210  210  210   \n",
       "2     1000080051  1090472  1738  210  210  210  210  210  210  210  210  210   \n",
       "3     1000090001  1000018  1738  212  212  212  212  212  212  212  212  212   \n",
       "4     1000090014  1000020  1738  211  211  211  211  211  211  211  211  211   \n",
       "...          ...      ...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "9245  5078410154  5087579  1738  202  202  202  202  202  202  202  202  202   \n",
       "9246  5078940001  5088090  1738  204  204  204  204  204  204  204  204  204   \n",
       "9247  5078980001  5107854  1738  203  203  203  203  203  203  203  203  203   \n",
       "9248  5079710250  5107860  1738  200  200  200  200  200  200  200  200  200   \n",
       "9249  5079910100  5088569  1738  204  204  204  204  204  204  204  204  204   \n",
       "\n",
       "       B9  B11  B12  NDVI  month  year  \n",
       "0     214  214  214   214   1738  1738  \n",
       "1     210  210  210   210   1738  1738  \n",
       "2     210  210  210   210   1738  1738  \n",
       "3     212  212  212   212   1738  1738  \n",
       "4     211  211  211   211   1738  1738  \n",
       "...   ...  ...  ...   ...    ...   ...  \n",
       "9245  202  202  202   202   1738  1738  \n",
       "9246  204  204  204   204   1738  1738  \n",
       "9247  203  203  203   203   1738  1738  \n",
       "9248  200  200  200   200   1738  1738  \n",
       "9249  204  204  204   204   1738  1738  \n",
       "\n",
       "[9250 rows x 18 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel1C_counts = sentinel_1C.groupby(['bbl','bin']).count().reset_index()\n",
    "sentinel1C_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "aa5bbb24-108b-4d91-9899-6a60d0020bbb",
   "metadata": {},
   "outputs": [
    {
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       "<p>9250 rows × 3 columns</p>\n",
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      "text/plain": [
       "             bbl      bin  percent_found\n",
       "0     1000020001  1085792       0.123130\n",
       "1     1000080039  1078968       0.120829\n",
       "2     1000080051  1090472       0.120829\n",
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       "9248  5079710250  5107860       0.115075\n",
       "9249  5079910100  5088569       0.117376\n",
       "\n",
       "[9250 rows x 3 columns]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel1C_counts['percent_found'] = sentinel1C_counts['B1'] / sentinel1C_counts['date']\n",
    "sentinel1C_percentages = sentinel1C_counts[['bbl','bin','percent_found']]\n",
    "sentinel1C_percentages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "8ca1a2d5-b395-4cb8-83c6-41e22a1e6c35",
   "metadata": {},
   "outputs": [
    {
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       "      <td>40.840495</td>\n",
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       "      <td>-73.794127</td>\n",
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       "<p>9250 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             bbl      bin     id   latitude  longitude  percent_found\n",
       "0     1007640007  1013712  31a2a  40.739758 -74.001778       0.116801\n",
       "1     3050830044  3116615  152a6  40.653385 -73.956779       0.119102\n",
       "2     2033180015  2017499  092e2  40.869700 -73.895017       0.117952\n",
       "3     3074040001  3202112  9b415  40.594550 -73.951343       0.238780\n",
       "4     3050580057  3116089  b753d  40.652215 -73.964713       0.121979\n",
       "...          ...      ...    ...        ...        ...            ...\n",
       "9245  3017130007  3421590  8cbbd  40.677413 -73.923052       0.116801\n",
       "9246  1008280059  1015659  27520  40.744560 -73.989475       0.112198\n",
       "9247  2029410001  2114529  5830f  40.840495 -73.897780       0.116801\n",
       "9248  4098750001  4211112  2ec2a  40.717143 -73.794127       0.243959\n",
       "9249  2048190015  2062829  2bf8f  40.887201 -73.864459       0.119102\n",
       "\n",
       "[9250 rows x 6 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentinel1C_footcounts = pd.merge(footprints_ll, sentinel1C_percentages, left_on=[\"bbl\",\"bin\"], right_on=[\"bbl\",\"bin\"]).fillna(0)\n",
    "sentinel1C_footcounts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "52e80734-a495-450a-86fd-63746373489b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1395238391440923"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(sentinel1C_footcounts.percent_found)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "0ec7086a-54c4-4ab2-a98a-b0092e596939",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentinel1C_footcounts.to_csv(os.path.join(destination_dir, \"sentinel_1C_counts.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d2cdbd6-719a-4a7a-b8fe-ff01e64af530",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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