How to Calculate AVI (Advanced vegetation Index) using sentinel 2 Data in GEE

Question

I am trying to download time series data of AVI using Sentinel 2 also I want to make a time series chart?

This is my code:

var batch = require('users/fitoprincipe/geetools:batch')
var avi = function(image) {
  return image.select().addBands(image.expression(
                          'NIR * ((1 - RED) * (NIR - RED))**(1/3)', {
                        'NIR': image.select('B8'),
                        'RED': image.select('B4')
                    }).float()).rename('AVI')};

// Import all available Sentinel 2 surface reflectance imagery as an ee.ImageCollection
// and filter for images within your study area
var S2_SR = ee.ImageCollection('COPERNICUS/S2_SR').filter(ee.Filter.date('2015-10-01', '2021-12-31')).select([
      // Make this list whichever bands you want to view or analyze
      'B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8','B8A','B9','B11','B12'
    ]).map(avi)
var clippedSAR= S2_SR.map(function(im){ 
  return im.clip(geometry);
});     
// Center your map at your study area
var visParams = {
  min: 0,
  max: 20000,
  gamma: 1.2,
  bands: ['B4', 'B8', 'B2'],
};


Map.addLayer(geometry);
Map.centerObject(geometry);
var chart = Chart.image.series(avi, geometry, ee.Reducer.mean(),1000, 'system:time_start').setOptions({
          title: 'AVI',
          vAxis: {title: 'NIR & RED'},
});
print(chart)
batch.Download.ImageCollection.toDrive(avi, 'work',
                {scale: 30,
                 region: geometry,
                 type: 'float',
                  description: 'Landsat8',
                  maxPixels: 1e13,
                  fileFormat: 'GeoTIFF',
                })

Answer 1

You're missing a quite important step. You never actually have any imagery to work with, and simply pass the avi function instead of an ee.ImageCollection to the chart. The below adds a Sentinel 2 SR image collection, calculates AVI and removes clouds in each image within a date range, for some geometry. Note about the image collection from the EE data-set catalog:

WARNING: ESA did not produce L2 data for all L1 assets, and earlier L2 coverage is not global.

If you want your time-series to go further back, you might want to switch to the COPERNICUS/S2_HARMONIZED collection instead.

var avi = function(image) {
  image = image.divide(10000) // Rescale the bands from 0-10000 to 0-1, which the expression expects
  return image.select().addBands(image.expression('NIR * ((1 - RED) * (NIR - RED))**(1/3)', {
    'NIR': image.select('B8'),
    'RED': image.select('B4')
  }).float()).rename('AVI')
}

var geometry = ee.Geometry(Map.getBounds(true))
var startDate = '2019-01-01'
var endDate = '2022-04-01'

var cloudThreshold = 30
var filter = ee.Filter.and(
  ee.Filter.bounds(geometry),
  ee.Filter.date(startDate, endDate)
)
var s2 = ee.ImageCollection(
  ee.Join.saveFirst('cloudProbability').apply({
    primary: ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED').filter(filter),
    secondary: ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY').filter(filter),
    condition: ee.Filter.equals({
      leftField: 'system:index',
      rightField: 'system:index'
    })
  })
).map(function(image) {
  var cloudFree = ee.Image(image.get('cloudProbability')).lt(cloudThreshold)
  return image
    .addBands(avi(image))
    .updateMask(cloudFree)
})

https://code.earthengine.google.com/3a6b71fb3e34d32a5336fdbbb01e7706