I was trying to perform a supervised classification of a mangrove forest using collocated sentinel 1 and 2 images using this code. With the help provided by @Xunilk, I finally managed to run the code. However, there is an error, which is: image: Layer error: Can't encode object: min()

Reduces an image collection by calculating the minimum value of each pixel across the stack of all matching bands. Bands are matched by name.

Args: this:collection (ImageCollection): The image collection to reduce.

Other results seem as expected. I am not understanding the problem!

  • Sir, you adapted the code of your former question wrong for this question. The fixed code, without supervised classification, is as follows: code.earthengine.google.com/8594f4c93e0c663c0d97f3b0406e8000 . Please, use it for editing your question again and corroborate if your supervised classification now works or which kind of error appears. Thanks.
    – xunilk
    Commented Sep 3, 2023 at 17:37
  • By the way, there are 11 composite images and it was not necessary to use any flatten command. Your issue was in a wrong adaptation of your previous code.
    – xunilk
    Commented Sep 3, 2023 at 17:53
  • @xunilk edited. The code now seems fine, and the results are as expected. but only one error remains, can you please check? Commented Sep 3, 2023 at 18:43
  • The problem is with the Map.addLayer(SCcomposite, CIcomposites, 'image'). The Map.addLayer() function needs a visualization parameter in the second place, while an imageCollection CIcomposites is provided there. If you adjust the parameter with visualization values, it will be fine. Or you can keep like this Map.addLayer(SCcomposite, {}, 'image').
    – Padmanabha
    Commented Sep 3, 2023 at 19:56

1 Answer 1


I checked your code out and it looks now without any problem. I also fixed an issue to export classified image to Google Drive.

// Load Sentinel-1 and Sentinel-2 Image Collections
var sentinel1Collection = ee.ImageCollection('COPERNICUS/S1_GRD');
var sentinel2Collection = ee.ImageCollection('COPERNICUS/S2');

// Filter Sentinel-1 collection for the study area and time range
var sentinel1Filtered = sentinel1Collection
  .filterDate('2022-12-01', '2023-12-30');


// Filter Sentinel-2 collection for the study area and time range
var sentinel2Filtered = sentinel2Collection
  .filterDate('2022-12-01', '2023-12-30')
  .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 5));

// Define a function to collocate Sentinel-1 and Sentinel-2 images
var collocateImages = function(image) {
  var date = ee.Image(image).date();
  // Filter Sentinel-1 collection for the specific date
  var sentinel1Image = sentinel1Filtered
    .filterDate(date, date.advance(1, 'day'))
  // Collocate the two images using the closest in time principle
  var collocatedImage = ee.Image(image).addBands(sentinel1Image.select(['VV', 'VH']))
                                       .copyProperties(sentinel1Image); // Copy properties from the Sentinel-1 image
  return ee.Algorithms.If(sentinel1Image, collocatedImage, 0); 


// Map over the Sentinel-2 images and collocate with Sentinel-1
var collocatedImages = ee.ImageCollection(sentinel2Filtered.toList(sentinel2Filtered.size())


// Function to create a composite from a collocated image
var createComposite = function(collocatedImage) {
  // Select bands from Sentinel-2 and Sentinel-1 images
  var s2Bands = collocatedImage.select(['B2', 'B3']); // Select Sentinel-2 bands B2 and B3
  var s1Bands = collocatedImage.select(['VV', 'VH']); // Select Sentinel-1 bands VV and VH
  // Calculate the band ratio of VV and VH from Sentinel-1 image
  var vvRatioVH = s1Bands.select('VV').divide(s1Bands.select('VH'));
  // Create a composite by blending Sentinel-2 bands, Sentinel-1 band ratio, and VH band
  var composite = ee.Image.cat([s2Bands, vvRatioVH, s1Bands.select('VH')]);
  return composite.copyProperties(collocatedImage); // Copy properties from the original collocated image


// Map over the collocated images and create composites
var CIcomposites = collocatedImages.map(createComposite);



//Supervised classification//
// Median composite of Collocated Image bands

var SCcomposite = CIcomposites.median().clip(studyArea);

// Display the input composite.
Map.addLayer(SCcomposite, {}, 'image');

// Create sample featureCollection
var gcps = shallowater.merge(deepwater).merge(mudflats).merge(Barelands).merge(Builtups).merge(Agriculture).merge(Crabfarms).merge(shrimpfarms).merge(trees).merge(shrubs);

// Add a random column and split the GCPs into training and validation set
var gcps = gcps.randomColumn();

// In this hypothetical case we'll use 60% of the points for validation
// It is common to use 70% for training and 30% for validation
// Note that the number of points is very low in this example
var trainingGcps = gcps.filter(ee.Filter.lt('random', 0.6));
var validationGcps = gcps.filter(ee.Filter.gte('random', 0.6));

// Overlay the points on the image to get training data
var training = SCcomposite.sampleRegions({
  collection: trainingGcps,
  properties: ['landcover'],
  scale: 10,
  tileScale: 16

// Train a classifier
var classifier = ee.Classifier.libsvm()
  features: training,
  classProperty: 'landcover',
  inputProperties: SCcomposite.bandNames()

// Classify the image
var classified = SCcomposite.classify(classifier);

// Visualize result
var classVis = {
  min: 0,
  max: 9,
  palette: ['aqua', 'blue', 'brown', 'gray', 'red', 'yellow', 'orange', 'maroon', 'green', 'olive',]

Map.addLayer(classified, classVis, 'Mongla_LULC');

  image: classified,
  description: 'Sundarbans_LULC',
  scale: 10,
  region: studyArea,
  maxPixels: 1e13

// Accuracy Assessment

// Use classification map to assess accuracy using the validation fraction
// of the overall training set created above.
var test = classified.sampleRegions({
  collection: validationGcps,
  properties: ['landcover'],
  scale: 10,
  tileScale: 16

var testConfusionMatrix = test.errorMatrix('landcover', 'classification');
// Printing of confusion matrix may time out. Alternatively, you can export it as CSV
print('Confusion Matrix', testConfusionMatrix);
print('Test Accuracy', testConfusionMatrix.accuracy());

//Area Measurement for multiple class
var areaImage = ee.Image.pixelArea().addBands(
var areas = areaImage.reduceRegion({
      reducer: ee.Reducer.sum().group({
      groupField: 1,
      groupName: 'class',
    geometry: studyArea,
    scale: 500,
    maxPixels: 1e10

var nestedList = ee.List(
  [['a', 'b'], ['c', 'd'], ['e', 'f']]);
// Output: [["a","b"],["c","d"],["e","f"]]
// Output: ["a","b","c","d","e","f"]

var classAreas = ee.List(areas.get('groups'));
var classAreaLists = classAreas.map(function(item) {
  var areaDict = ee.Dictionary(item);
  var classNumber = ee.Number(areaDict.get('class')).format();
  var area = ee.Number(
  return ee.List([classNumber, area]);
var result = ee.Dictionary(classAreaLists.flatten());

Map.centerObject(studyArea, 9);

After running it in the GEE code editor, I got the output as below picture. Notice that the export task also ran successfully.

enter image description here

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