03-Accessing_Satellite_Imagery_Using Python.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Accessing Satellite Imagery Using Python.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMhd3HXAv9gwDgLBOmxfezW",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KimutaiLawrence/Geospatial-Data-Science/blob/main/Accessing_Satellite_Imagery_Using%C2%A0Python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2662988b"
      },
      "source": [
        "Accessing data: We will use Sentinel 2 data. There are many options to access Sentinel 2 images and \n",
        "most of them will require you to access through website interaction whether directly via a downloading service utility \n",
        "or via the cloud. However, since we are using Jupyter notebook, we will access them right here using, sentinelsat a python \n",
        "library which makes searching, retrieving and downloading Sentinel satellite images easy. So let us start installing \n",
        "sentinelsat through pip. We also install other packages that we will use as we continue."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "02a8e489",
        "outputId": "d2f410a3-f25a-49bc-cf27-86a532d13d77"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting sentinelsat\n",
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            "\u001b[?25l\r\u001b[K     |██████▊                         | 10 kB 20.3 MB/s eta 0:00:01\r\u001b[K     |█████████████▌                  | 20 kB 23.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████▏           | 30 kB 12.0 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 40 kB 10.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 48 kB 3.2 MB/s \n",
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            "Installing collected packages: html2text, geomet, geojson, sentinelsat\n",
            "Successfully installed geojson-2.5.0 geomet-0.3.0 html2text-2020.1.16 sentinelsat-1.1.1\n",
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            "Successfully installed affine-2.3.1 click-plugins-1.1.1 cligj-0.7.2 rasterio-1.2.10 snuggs-1.4.7\n",
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            "Successfully installed fiona-1.8.21 geopandas-0.10.2 munch-2.5.0 pyproj-3.2.1\n",
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          ]
        }
      ],
      "source": [
        "!pip install sentinelsat\n",
        "!pip install folium\n",
        "!pip install descartes\n",
        "!pip install rasterio\n",
        "!pip install wget\n",
        "!pip install geopandas\n",
        "!pip install pandas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6fd9e2ce"
      },
      "outputs": [],
      "source": [
        "import folium\n",
        "import os\n",
        "import numpy as np\n",
        "\n",
        "from sentinelsat import SentinelAPI, read_geojson, geojson_to_wkt \n",
        "import geopandas as gpd\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import rasterio\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from shapely.geometry import MultiPolygon, Polygon\n",
        "import fiona\n",
        "import wget"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e9a62342"
      },
      "source": [
        "Before we are able to use sentinelsat, we need to register a username in Copernicus Open Access Hub and note down your username \n",
        "and password and paste them here inside the code."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d4af30e1"
      },
      "outputs": [],
      "source": [
        "from sentinelsat import SentinelAPI\n",
        "\n",
        "user = '<your username>' \n",
        "password = '<your password>' \n",
        "api = SentinelAPI(user, password, 'https://scihub.copernicus.eu/dhus')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "19071542"
      },
      "source": [
        "You are set to use sentinelsat and download Sentinel Satellite images.We then use boundary data from Lagos City, Nigeria. I uploaded the boundary for Lagos city,Nigeria here on google colab . We thus access the boundary data(Lagos shapefile) via the Lagos shapefile path and we will read it with Geopandas and visualize it with Folium python library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 719
        },
        "id": "e50b19d2",
        "outputId": "70214af9-bc9a-4f22-ff3a-3f56177a7a11"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installing geopandas...\n",
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            "Loading Shapefile...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   ID_0  ISO   NAME_0  ID_1 NAME_1  ID_2            NAME_2           TYPE_2  \\\n",
              "0   163  NGA  Nigeria    25  Lagos   506             Agege  Local Authority   \n",
              "1   163  NGA  Nigeria    25  Lagos   507  Ajeromi/Ifelodun  Local Authority   \n",
              "2   163  NGA  Nigeria    25  Lagos   508          Alimosho  Local Authority   \n",
              "3   163  NGA  Nigeria    25  Lagos   509      Amuwo Odofin  Local Authority   \n",
              "4   163  NGA  Nigeria    25  Lagos   510             Apapa  Local Authority   \n",
              "\n",
              "         ENGTYPE_2 NL_NAME_2 VARNAME_2  \\\n",
              "0  Local Authority      None      None   \n",
              "1  Local Authority      None      None   \n",
              "2  Local Authority      None      None   \n",
              "3  Local Authority      None      None   \n",
              "4  Local Authority      None      None   \n",
              "\n",
              "                                            geometry  \n",
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              "      <td>Nigeria</td>\n",
              "      <td>25</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>506</td>\n",
              "      <td>Agege</td>\n",
              "      <td>Local Authority</td>\n",
              "      <td>Local Authority</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>POLYGON ((3.33228 6.64879, 3.33108 6.64763, 3....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>163</td>\n",
              "      <td>NGA</td>\n",
              "      <td>Nigeria</td>\n",
              "      <td>25</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>507</td>\n",
              "      <td>Ajeromi/Ifelodun</td>\n",
              "      <td>Local Authority</td>\n",
              "      <td>Local Authority</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>POLYGON ((3.32256 6.44672, 3.32138 6.44733, 3....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>163</td>\n",
              "      <td>NGA</td>\n",
              "      <td>Nigeria</td>\n",
              "      <td>25</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>508</td>\n",
              "      <td>Alimosho</td>\n",
              "      <td>Local Authority</td>\n",
              "      <td>Local Authority</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>POLYGON ((3.28883 6.58545, 3.28600 6.58480, 3....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>163</td>\n",
              "      <td>NGA</td>\n",
              "      <td>Nigeria</td>\n",
              "      <td>25</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>509</td>\n",
              "      <td>Amuwo Odofin</td>\n",
              "      <td>Local Authority</td>\n",
              "      <td>Local Authority</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>POLYGON ((3.18543 6.40375, 3.18554 6.40677, 3....</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>163</td>\n",
              "      <td>NGA</td>\n",
              "      <td>Nigeria</td>\n",
              "      <td>25</td>\n",
              "      <td>Lagos</td>\n",
              "      <td>510</td>\n",
              "      <td>Apapa</td>\n",
              "      <td>Local Authority</td>\n",
              "      <td>Local Authority</td>\n",
              "      <td>None</td>\n",
              "      <td>None</td>\n",
              "      <td>POLYGON ((3.32256 6.44672, 3.32486 6.44819, 3....</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "\n",
        "print(\"Installing geopandas...\")\n",
        "\n",
        "# We need to install geopandas and descartes using PIP because they are \n",
        "# not installed on Jupyter by default. \n",
        "\n",
        "!pip install geopandas\n",
        "!pip install descartes \n",
        "\n",
        "import geopandas as gpd\n",
        "\n",
        "# Next, print out the shapefile. \n",
        "# It won't look like much because we are looking at geographic coordinates. \n",
        "\n",
        "print(\"Loading Shapefile...\")\n",
        "\n",
        "# If using your files, replace below filename (\"/content/Lagosnigeria.shp\") with the \n",
        "# shapefile filename you uploaded. \n",
        "\n",
        "shapefile = gpd.read_file(\"/content/Lagosnigeria.shp\")\n",
        "\n",
        "# The \"head\" function prints out the first five rows in full, so you can see\n",
        "# the columns in the data set too! \n",
        "\n",
        "shapefile.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4a99ea05"
      },
      "source": [
        "One last step before we can search and download sentinel 2 images is to create a footprint from the Lagos Shapefile Geometry. \n",
        "Here we will use Shapely Python library since our data is in Shapefiles and have read it already as Geopandas GeodataFrame. \n",
        "(Note that if you have Geojson data, sentinelsatprovides a handy way to convert your data into a proper format in the query)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2cdc42f0"
      },
      "outputs": [],
      "source": [
        "from shapely.geometry import MultiPolygon, Polygon\n",
        "\n",
        "footprint = None\n",
        "for i in shapefile['geometry']:\n",
        "    footprint = i"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f9339553"
      },
      "source": [
        "Now we can run a query on the api we have created above. There are different ways you can construct your query here depending on your use case. In this example, we will create a query for Sentinel 2 images Level 2A with cloud coverage between 0 and 10 \n",
        "that fall or intersect with the footprint (Area of study:\"in our case Lagos City\"). For the time period, we are interested only in Sentinel Level 2A satellite images taken between ‘20220101’ and ‘20220120’ (For reference on valid search queries please refer to scihub)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5a777bef"
      },
      "outputs": [],
      "source": [
        "products = api.query(footprint,\n",
        "                     date = ('20190101', '20190110'),\n",
        "                     platformname = 'Sentinel-2',\n",
        "                     processinglevel = 'Level-2A',\n",
        "                     cloudcoverpercentage = (0,10)\n",
        "                    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fbee93ae"
      },
      "source": [
        "Sorting the Sentinel 2 data "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "id": "de090293",
        "outputId": "9aa8b410-fac7-4141-bdd4-2bf96d364fea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                                                                  title  \\\n",
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              "\n",
              "                                                                                   link  \\\n",
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              "                                                                                summary  \\\n",
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              "\n",
              "                                                 endposition  orbitnumber  \\\n",
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              "ab40b37d-d1ae-4592-bb01-3ed0adf2527a 2019-01-01 10:04:11.024        18423   \n",
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              "                                      ...  platformidentifier  orbitdirection  \\\n",
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              "                                      platformserialidentifier  \\\n",
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              "ab40b37d-d1ae-4592-bb01-3ed0adf2527a               Sentinel-2A   \n",
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              "                                      processingbaseline  processinglevel  \\\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-34e0c2b1-e4b8-4929-aa92-9a75c739c2f5 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-34e0c2b1-e4b8-4929-aa92-9a75c739c2f5');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "products_gdf = api.to_geodataframe(products)\n",
        "products_gdf_sorted = products_gdf.sort_values(['cloudcoverpercentage'], ascending=[True])\n",
        "products_gdf_sorted"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8a048376"
      },
      "source": [
        "From the results that are acquired above, you can now download your Sentinel 2 data (specific) from the list of outputs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336,
          "referenced_widgets": [
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            "261d13c2d4b34c6da146daf35b2389a7",
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            "0f89b73cb51a46a3a86c0d983e05643a",
            "d1729759d31e46eab2dd832a4435933c",
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            "3367060473d540e380aaac09d869ce9e",
            "846cd44de5b84c03b7cbc25daa28f8f5"
          ]
        },
        "id": "7f388680",
        "outputId": "a057cc55-1bc1-43ec-fb01-a10e068fba38"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading S2A_MSIL2A_20190101T100411_N0211_R122_T31NEH_20190101T122648.zip:   0%|          | 0.00/1.10G [00:…"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {
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              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/a8874ba6619b6106/manager.min.js"
                }
              }
            }
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "MD5 checksumming:   0%|          | 0.00/1.10G [00:00<?, ?B/s]"
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              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/a8874ba6619b6106/manager.min.js"
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              }
            }
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Creation Date': datetime.datetime(2019, 1, 1, 16, 3, 17, 192000),\n",
              " 'Ingestion Date': datetime.datetime(2019, 1, 1, 15, 54, 36, 102000),\n",
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              " 'id': 'ab40b37d-d1ae-4592-bb01-3ed0adf2527a',\n",
              " 'md5': 'b53789caf34ab20839aea9503364c0eb',\n",
              " 'path': 'S2A_MSIL2A_20190101T100411_N0211_R122_T31NEH_20190101T122648.zip',\n",
              " 'quicklook_url': \"https://scihub.copernicus.eu/dhus/odata/v1/Products('ab40b37d-d1ae-4592-bb01-3ed0adf2527a')/Products('Quicklook')/$value\",\n",
              " 'size': 1099405101,\n",
              " 'title': 'S2A_MSIL2A_20190101T100411_N0211_R122_T31NEH_20190101T122648',\n",
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            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "api.download(\"ab40b37d-d1ae-4592-bb01-3ed0adf2527a\")"
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}