I want to extract for each point of my shapefile the NDVI and the related time-series charts but I cannot understand how to extract values.

This is the code

// load the S2 dataset
var S2 = ee.ImageCollection('COPERNICUS/S2')
           .filterDate("2015-01-01", "2021-12-31")
           .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 10));

// S2.limit(10).aside(print)

// load S2_CLOUD_PROBABILITY dataset

// Join S2_summer2018 and S2_CLOUD_PROBABILITY
var S2withCloudMask = ee.Join.saveFirst('cloud_mask').apply({
primary: S2,
condition: ee.Filter.equals({leftField: 'system:index', rightField: 'system:index'})
S2withCloudMask = ee.ImageCollection(S2withCloudMask);

// S2withCloudMask.first().aside(print)

// define a function to mask-out clouds from each image
var maskClouds = function(img) {
var clouds = ee.Image(img.get('cloud_mask')).select('probability');
var isNotCloud = clouds.lt(20);
return img.mask(isNotCloud);

// // use the maskClouds function  
var S2_cloudsMasked = S2withCloudMask.map(maskClouds);
// add NDVI
// var addNDVI = function(image) {
function addNDVI(image) {
 var ndvi = image.normalizedDifference(['B8', 'B4']).rename('ndvi');
 return image.addBands(ndvi);
// Map the function over the collection
var S2_cloudsMasked_ndvi = S2_cloudsMasked.map(addNDVI);

// S2_cloudsMasked_ndvi.limit(5).aside(print);

// visualize the time series
var chart = ui.Chart.image.series({
 imageCollection: S2_cloudsMasked_ndvi.select('ndvi'),
 region: geometry,
 reducer: ee.Reducer.mean(),
 scale: 20
     lineWidth: 1,
     title: 'NDVI Time Series',
     interpolateNulls: true,
     vAxis: {title: 'NDVI'},
     hAxis: {title: '', format: 'YYYY-MMM'}// ,
     // explorer: {}
     // explorer: {axis: 'horizontal'}  // or 'vertical'

// // Map.addLayer(geometry);
// Map.centerObject(geometry, 15);
// ```

1 Answer 1


You didn't specify what format you want the time-series in. Below you have one approach that gives you a single ee.FeatureCollection with NDVI for all points. If this isn't exactly what you're looking for, it could at least be a starting point.

var points = ee.FeatureCollection([
  ee.Feature(ee.Geometry.Point([12.487749926662959, 41.88485067969689])),
  ee.Feature(ee.Geometry.Point([12.492396084115729, 41.890305914470154]))

var timeSeries = points
var chart = ui.Chart.feature.groups({
  features: timeSeries, 
  xProperty: 'date', 
  yProperty: 'ndvi', 
  seriesProperty: 'point'

Map.addLayer(points, null, 'points')

function timeSeriesForPoint(point) {
  return S2_cloudsMasked_ndvi
    .map(function (image) {
      return extractNdvi(image, point)

function extractNdvi(image, point) {
    var ndvi = image
        reducer: ee.Reducer.first(), 
        geometry: point.geometry(), 
        scale: 10
    return ee.Feature(null, {
      point: point.geometry().coordinates().join(', '),
      ndvi: ndvi,
      date: image.date()


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