I have this script that creates a composite image From Sentinel-2 in GEE and filters it for date and ROI:

/* Import Sentinel-2 imagery and mask clouds using the Sentinel-2 QA band */
function maskS2clouds(COPERNICUS/S2_SR) {
  var qa = S2L2A.select('QA60');

  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;

  var mask = qa.bitwiseAnd(cloudBitMask).eq(0)

  return S2L2A.updateMask(mask).divide(10000);
var S2_Spring= ee.ImageCollection(COPERNICUS/S2_SR)
                  .filterDate('2022-05-01', '2022-08-31')
                  // Pre-filter to get less cloudy granules.
                  .map(function(image){return image.clip(Region_Border)});
var visualization = {
 min: 0.0,
 max: 0.3,
 bands: ['B4', 'B3', 'B2'],

After, I calculate NDVI and add it to the collection as a band:

var addNDVI = function(COPERNICUS/S2_SR) {
  var ndvi = S2L2A.normalizedDifference(['B8', 'B4']).rename('NDVI');
  return S2L2A.addBands(ndvi);

var S2_Spring = S2_Spring.map(addNDVI);

Finaly, I select the desired bands and create a composite:

var S2_Spring = ee.ImageCollection(S2_Spring.select(["B2", "B3", "B4","B8","NDVI", "NDWI","B5", "B6", "B7","B8A","B11", "B12"] ,["Sp_B2", "Sp_B3", "Sp_B4","Sp_B8","Sp_NDVI", "Sp_NDWI","Sp_B5", "Sp_B6", "Sp_B7","Sp_B8A","Sp_B11","Sp_B12"]));

var S2_Spring_Composite = S2_Spring.mosaic();

Then, I use it for supervised classification in GEE. I need to minimize my error in classification, so I decided to use a filter to mask out areas that are not useful for classification. Is it possible to calculate NDVI over the final composite image 'S2_Spring_Composite' and filter it for a specific range? For example, find and deleted NDVI values less than 0 and more than 0.2?

  • Instead of deleting those pixels, why not add classes for water bodies, snow and so?
    – aldo_tapia
    Feb 23, 2023 at 17:57
  • Because they are not snow or water bodies. They are solar panels that I am trying to separate from fields around them and have low NDVI values , so it is what I thought could help to classify them
    – Paris
    Feb 24, 2023 at 15:17
  • 1
    BTW .divide(10000) won't convert DN to reflectance to S2 products after Jan 22, 2022. The new conversion is (DN-1000)/10000
    – aldo_tapia
    Feb 24, 2023 at 15:56
  • 1
    Yes, Dealing with products before and after baseline 4.0 is a pain (although you can add a logic test based on Jan 25th for reflectance conversion). But the product you mention will save you time
    – aldo_tapia
    Feb 27, 2023 at 8:44
  • 1
    Fortunately, my time period starts way after January 2022, so I am working on summer/autumn 2022. So by using the Harmonized collection, all will be as before. Thanks so much for letting me know
    – Paris
    Feb 27, 2023 at 8:48

1 Answer 1


You can use updateMask to mask the values you indicate. You just need to define a mask of the NDVI values you wish to retain in the image and apply it to your image.

// Create a mask with the values you wish to retain
var maskIm = S2_Spring_Composite.select('Sp_NDVI').gte(0)

// Masked image
var maskedIm = S2_Spring_Composite.updateMask(maskIm);
  • Thanks. I added what you wrote and it works for masking, but I realized that if I add an NDVI map separately to the script and compare the values, I see differences between the NDVI added as a band to the collection and NDVI map separately. I share my script here with assets. If you use the inspector on the point geometry, the 'Sp_NDVI' band shows 0.04, while the NDVI map separately calculated shows teh value 0.37! Which one is the correct NDVI values? I am confused because the formula for calculating NDVI is the same.
    – Paris
    Feb 24, 2023 at 8:55
  • 1
    The difference is caused by using mosaic and mean to obtain the final composite. mosaic takes the most recent pixels and add it to the final result, while mean calculates the mean value of all the unmasked pixels. You might want to take a look at this: developers.google.com/earth-engine/guides/ic_composite_mosaic Feb 25, 2023 at 2:21

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