Published on May 19, 2020 | Bikesh Bade | 3534 Views

Normalized Difference Vegetation Index (NDVI) quantifies vegetation by measuring the difference between near-infrared (which vegetation strongly reflects) and red light (which vegetation absorbs).

You can export images, map tiles, tables, and videos from Earth Engine. The exports can be sent to your Google Drive account, to Google Cloud Storage, or to a new Earth Engine asset.

Step 1: Import two sentinel-2 images

var s2 = ee.ImageCollection('COPERNICUS/S2')
                  .filterDate('2019-01-01', '2019-12-30')
                  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))
                  .filterBounds(studyArea); //choose your own study Area

Step 2: Calculate the NDVI

//  Normalized difference vegetation index (NDVI)
function getNDVI (image) {
    var ndvi = image.normalizedDifference(['B8','B4']).rename("ndvi");
    image = image.addBands(ndvi);
    return image;
}

Step 3: Add images to the map

//create composite with reducer median
s2 = s2.median();

//get NDVI
s2 = getNDVI(s2).clip(studyArea);

//select NDVI layer 
var s2NDVI = s2.select('ndvi');

//get infromation of the images
print(s2);


// visualization
var ndviVis = {
  min: 0.0,
  max: 0.8,
  palette: [
    'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901',
    '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01',
    '012E01', '011D01', '011301'
  ],
};


var rgbVis = {
  min: 0.0,
  max: 0.3,
  bands: ['B4', 'B3', 'B2'],
};


//add to map
Map.centerObject(studyArea, 9);
Map.addLayer(s2, rgbVis, 'Sentinel');
Map.addLayer(s2NDVI, ndviVis, 'NDVI');

Step 4: Export

Export.image.toDrive({
  image: s2NDVI,
  scale:10,
  description: 'NDVI',
  region: studyArea,
});

Get the code in the link