No valid data training when doing a supervised classification using training data from a different year

Question

I'm doing a supervised classification using training data from a 2023 Image to a 2022 Image. However, when doing the migration I got the error message "No valid training data were found" for the 2022 Image even though the 2023 Image worked as usual. The Image for 2023 has a previous pre processing to add some bands like elevation and index, and the training data was taken using this same 2023 Image.

The code is right here: https://code.earthengine.google.com/7e3ca5825d18782c457c32b38b793007

var distance = Image_2022.spectralDistance(Image_2023);

Map.addLayer(distance,{}, 'Distancia Espectral')
var samples= Forest.merge(Nonforest)

Map.addLayer(samples, {}, 'Puntos 2023')
var samples_distance= distance.sampleRegions({
 collection: samples,
 properties: ['name','id'],
 scale:10
})

Map.addLayer(samples_distance, {}, 'Puntos Migrados')

var threshold = 0.2
var newGcp = samples_distance.filter(ee.Filter.lt('distance', threshold));
print('Total GCPs', samples.size());
print('Migrated GCPs', newGcp.size());
performClassification(Image_2023, samples, 'Classificacion 2023');
performClassification(Image_2022, newGcp, 'Classificacion 2022');

function performClassification(Image_2022, samples, year) {
  var samples = samples.randomColumn();
  var trainingGcp = samples.filter(ee.Filter.lt('random', 0.6));
  var validationGcp = samples.filter(ee.Filter.gte('random', 0.6));
  
  // Overlay the point on the image to get training data.
  var training_2 = Image_2022.sampleRegions({
    collection: trainingGcp,
    properties: ['name','id'],
    scale: 10,
    tileScale: 16
  });
  
  // Train a classifier.
  var classifier_2 = ee.Classifier.smileRandomForest(50)
  .train({
    features: training_2,  
    classProperty: 'id',
    inputProperties: Image_2022.bandNames()
  });
   
  // Classify the image.
  var classified_2022 = Image_2022.classify(classifier_2);
  
  Map.addLayer(classified_2022, classVis, year);
  
  // Use classification map to assess accuracy using the validation fraction
  // of the overall training set created above.
  var test = classified_2022.sampleRegions({
    collection: validationGcp,
    properties: ['id'],
    tileScale: 16,
    scale: 10,
  });
  
  var testConfusionMatrix = test.errorMatrix('id', 'clasificacion')
  // Printing of confusion matrix may time out. Alternatively, you can export it as CSV
  print('Confusion Matrix ' + year, testConfusionMatrix);
  print('Test Accuracy ' + year, testConfusionMatrix.accuracy());

}
var styling = {color: 'black', fillColor: '00000000'};

Map.addLayer(Poligonos_Leaf.style(styling), {}, 'Poligonos Sosty')

I was following this methodology: https://courses.spatialthoughts.com/end-to-end-gee.html#classification-with-migrated-training-samples

Answer 1

The problem is that samples_distance doesn't contain any geometries. This cause training_2 = Image_2022.sampleRegions() to give you an empty collection, hence the "No training data" error. Luckily, there's a very simple fix - specify that you want to keep the geometries:

var samples_distance = distance.sampleRegions({
  collection: samples,
  properties: ['name', 'id'],
  scale: 10,
  geometries: true
})

https://code.earthengine.google.com/25aeb520d708a24a12333ace1065f247