Sentinal_Prod_Rev1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Sentinal_Prod_Rev1.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMnqD3K85FYhPLjd7Cw8/7L",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sudhir2016/Alexa/blob/master/Sentinal_Prod_Rev1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dnhitYmUDvaK"
      },
      "source": [
        "Install sentinalsat, geopandas and rasterio."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "USkzgZGED7iv"
      },
      "source": [
        "! pip install sentinelsat"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N49y9erYn2_9"
      },
      "source": [
        "! pip install geopandas"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "whocVGN7oVsb"
      },
      "source": [
        "! pip install rasterio"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B52BVeQxEBSG"
      },
      "source": [
        "Various imports."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bmlINOZLnvRL"
      },
      "source": [
        "from sentinelsat import SentinelAPI,read_geojson, geojson_to_wkt\n",
        "import geopandas as gpd\n",
        "import rasterio as rio\n",
        "from rasterio.plot import show\n",
        "from rasterio import mask\n",
        "import numpy as np\n",
        "import glob\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QK5U3-zQEGw2"
      },
      "source": [
        "User name and Password for Sentinal."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CtX1njrM4BP"
      },
      "source": [
        "user= 'UID'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5gQtK6LwM_5X"
      },
      "source": [
        "password= 'PWD'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V4eZfxuPFFE1"
      },
      "source": [
        "Create sentinal query based on our plot coordinates,  required time window and cloud cover filter. Sentinal will provide list of availalble tiles called products."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-NW_fVGINFEw"
      },
      "source": [
        "api = SentinelAPI(user, password, 'https://scihub.copernicus.eu/dhus')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIp1qCYiS6MS"
      },
      "source": [
        "footprint = geojson_to_wkt(read_geojson('plot.geojson'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A57H5HpiSpjE"
      },
      "source": [
        "products = api.query(footprint,\n",
        "                     date = ('20201101', '20201130'),\n",
        "                     platformname = 'Sentinel-2',\n",
        "                     processinglevel = 'Level-2A',\n",
        "                     cloudcoverpercentage = (0,100)\n",
        "                    )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sI52YLYhIn6w"
      },
      "source": [
        "load products as geopandas dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SY2AOtcGk3TZ"
      },
      "source": [
        "products_gdf = api.to_geodataframe(products)\n",
        "#print(products_gdf)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jeUXr0rnGvko"
      },
      "source": [
        "Obtain ID of most recent Sentinal dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGtEXB57tzel"
      },
      "source": [
        "uuid=products_gdf['uuid']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VWH7wM66KKte"
      },
      "source": [
        "#cloud=products_gdf['cloudcoverpercentage']\n",
        "#print(cloud)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3fOjRq2zkJQ"
      },
      "source": [
        "uuid1=uuid[0]\n",
        "#print(uuid1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ybHhA3esHXxo"
      },
      "source": [
        "Download most recent Sentinal data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "drHGzgGcl0ot"
      },
      "source": [
        "api.download(uuid1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ei4RjgIwHfuO"
      },
      "source": [
        "Obtain filename of downloaded Sentilnal zip file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "usAzSam4e6qX"
      },
      "source": [
        "for name in glob.glob('/content/*.zip'):\n",
        "  file=name\n",
        "#print(file)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7vnUqBLiJEwQ"
      },
      "source": [
        "Unzip Sentinal file."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Bc-57nmgVAN"
      },
      "source": [
        "! unzip '$file'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s0xZr5tULSMG"
      },
      "source": [
        "Obtain the name to the Sentinal data directory."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Me81pZsNLcOL"
      },
      "source": [
        "for name in glob.glob('/content/*.SAFE'):\n",
        "  file1=name\n",
        "#print(file1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dl6w8k-oM2I9"
      },
      "source": [
        "Obtain the path of the Sentinal image data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2pVeY2_HMHwQ"
      },
      "source": [
        "for name in glob.glob(file1+'/GRANULE/*'):\n",
        "  file2=name\n",
        "#print(file2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ou5DFzPDRAeX"
      },
      "source": [
        "Obtain the file names of B2(B),B3(G),B4(R) and B8(NIR) channel images."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PMBXmyxLXKQ-"
      },
      "source": [
        "file3=[]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DcKc6PN-Wa2l"
      },
      "source": [
        "for name in glob.glob(file2+'/IMG_DATA/R10m/*'):\n",
        "  file3.append(name)\n",
        "file3.sort()\n",
        "#print(file3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IvCARpmMRN_N"
      },
      "source": [
        "Read the B4 and B8 channel data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5D1FtXQrZOBr"
      },
      "source": [
        "b4=rio.open(file3[3])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upgmmxvsaA4X"
      },
      "source": [
        "b8=rio.open(file3[4])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mic5YXtH2tlA"
      },
      "source": [
        "Convert plot coodinates to Sentinal crs."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A_HK9ymkD5fc"
      },
      "source": [
        "crs=b4.crs\n",
        "#print(crs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1dk50r2T2pbV"
      },
      "source": [
        "plot=gpd.read_file('plot.geojson')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACYzSVcQ2zbs"
      },
      "source": [
        "plot1=plot.to_crs({'init': crs})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vHvZk8vl22lQ"
      },
      "source": [
        "geo=plot1.geometry"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xy-SCMbYSP8H"
      },
      "source": [
        "Read B4(red), create red.tiff, extract red_plot.tiff and red_data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ni_S9zbzBsrV"
      },
      "source": [
        "red = b4.read()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0Lqdb3kuw9oM"
      },
      "source": [
        "meta = b4.meta\n",
        "meta.update(driver='GTiff')\n",
        "meta.update(dtype=rio.float32)\n",
        "\n",
        "with rio.open('red.tiff', 'w', **meta) as dst:\n",
        "    dst.write(red.astype(rio.float32))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oqe3frL1xmop"
      },
      "source": [
        "with rio.open(\"red.tiff\") as src:\n",
        "    out_image, out_transform = rio.mask.mask(src,geo,crop=True)\n",
        "    out_meta = src.meta.copy()\n",
        "    out_meta.update({\"driver\": \"GTiff\",\n",
        "                 \"height\": out_image.shape[1],\n",
        "                 \"width\": out_image.shape[2],\n",
        "                 \"transform\": out_transform})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ClDKK3WGxusl"
      },
      "source": [
        "with rio.open(\"red_plot.tiff\", \"w\", **out_meta) as dest:\n",
        "    dest.write(out_image)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Ozi0VaNzDO_"
      },
      "source": [
        "src = rio.open(\"red_plot.tiff\")\n",
        "red_data=src.read(1)\n",
        "print(red_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ksX0rp4DyK7h"
      },
      "source": [
        "Read B8(nir), create nir.tiff and extract nir_plot.tiff and nir_plot_data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u8qL16BUyk2F"
      },
      "source": [
        "nir = b8.read()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5rA6IYYPDg6-"
      },
      "source": [
        "meta = b4.meta\n",
        "meta.update(driver='GTiff')\n",
        "meta.update(dtype=rio.float32)\n",
        "\n",
        "with rio.open('nir.tiff', 'w', **meta) as dst:\n",
        "    dst.write(nir.astype(rio.float32))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G3hUR3iOywxV"
      },
      "source": [
        "with rio.open(\"nir.tiff\") as src:\n",
        "    out_image, out_transform = rio.mask.mask(src,geo,crop=True)\n",
        "    out_meta = src.meta.copy()\n",
        "    out_meta.update({\"driver\": \"GTiff\",\n",
        "                 \"height\": out_image.shape[1],\n",
        "                 \"width\": out_image.shape[2],\n",
        "                 \"transform\": out_transform})"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RwCmrb08y3xj"
      },
      "source": [
        "with rio.open(\"nir_plot.tiff\", \"w\", **out_meta) as dest:\n",
        "    dest.write(out_image)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sxc_slRdzz8D"
      },
      "source": [
        "src = rio.open(\"nir_plot.tiff\")\n",
        "nir_data=src.read(1)\n",
        "print(nir_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7YOn68T1z4gd"
      },
      "source": [
        "Calculate ndvi_plot."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3Yu3XhS3QOB"
      },
      "source": [
        "ndvi_plot = (nir_data.astype(float)-red_data.astype(float))/(nir_data+red_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TOb4GkMq3heG"
      },
      "source": [
        "ndvi_plot1=np.nan_to_num(ndvi_plot)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pbnp52VS3pbp"
      },
      "source": [
        "ndvi_plot2=ndvi_plot1[ndvi_plot1 != 0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2NRVMrl13wbd"
      },
      "source": [
        "Mean_ndvi_plot=np.sum(ndvi_plot2)/ndvi_plot2.size\n",
        "print(Mean_ndvi_plot)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}