Cloud Shadow Removal for Sentinel-2

Question

I am trying to classify my study area with a RandomForest classifier, using Sentinel-2 Surface Reflectance images. This classification gave me an accuracy of about 87%. I calculated the misclassified points and below are its results.

Mangrove Misclassified Points
FeatureCollection (303 elements, 0 columns)
Water Misclassified Points
FeatureCollection (989 elements, 0 columns)
Other Misclassified Points
FeatureCollection (242 elements, 0 columns)
All Misclassified Points
FeatureCollection (1534 elements, 0 columns)

As you can see, so many points are getting misclassified. It is because of many shadows appearing.

I looked into example, to remove the clouds and their shadows, but I am finding it very difficult to get my head around it.

I applied the below snippet

function maskS2clouds(image) {
  var qa = image.select('QA60');

  // Bits 10 and 11 are clouds and cirrus, respectively.
  var cloudBitMask = 1 << 10;
  var cirrusBitMask = 1 << 11;

  // Both flags should be set to zero, indicating clear conditions.
  var mask = qa.bitwiseAnd(cloudBitMask).eq(0)
      .and(qa.bitwiseAnd(cirrusBitMask).eq(0));

  return image.updateMask(mask).divide(10000);
}

This gave the misclassified points as

Mangrove Misclassified Points
FeatureCollection (282 elements, 0 columns)
Water Misclassified Points
FeatureCollection (986 elements, 0 columns)
Other Misclassified Points
FeatureCollection (269 elements, 0 columns)
All Misclassified Points
FeatureCollection (1551 elements)

The misclassified points increased, when it is expected to reduce.

Please guide me in understanding how I can incorporate it into my code, to improve the accuracy and minimize the misclassification points

Here is my implementation GEE

Answer 1

I usually use the following to obtain clear sky composites:

// Generate 'clear_sky' Sentinel-2 images using SCL.
    var s2_clear_sky = function(image){
      var scl = image.select('SCL');
      var clear_sky_pixels = scl.eq(4).or(scl.eq(5)).or(scl.eq(6)).or(scl.eq(11));
      return image.updateMask(clear_sky_pixels);
    };
    
    // Map 's2_clear_sky' function over the imageCollection.
    var s2_data = s2_data.map(s2_clear_sky);

It removes 0-no data, 1-satured or defective, 2-dark area pixels, 3-cloud shadows, 7-unclassified, 8- cloud medium probability, 9-cloud high probability, 10- thin cirrus according to the SCL product: https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm