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I Have multiple Landsat8 images at the same place in different years.

I want to have a cloud mask for each image, then merge the mask so that all images will be masked with a same aggregated mask.

I've got masked image for each year using code from this post Using cloud confidence to create cloud mask from Landsat 8 BQA? .

var RADIX = 2;  // Radix for binary (base 2) data.
var extractQABits = function (qaBand, bitStart, bitEnd) {
  var numBits = bitEnd - bitStart + 1;
  var qaBits = qaBand.rightShift(bitStart).mod(Math.pow(RADIX, numBits));
  return qaBits;
};

var getMaskedImg = function (i){

  // Reference a sample Landsat 8 TOA image.
  var image = ee.Image(i).clip(geometry);

  // Extract the QA band.
  var image_qa = image.select('BQA');

  // Create a mask for the dual QA bit "Cloud Confidence".
  var bitStartCloudConfidence = 5;
  var bitEndCloudConfidence = 6;
  var qaBitsCloudConfidence = extractQABits(image_qa, bitStartCloudConfidence, bitEndCloudConfidence);
  // Test for clouds, based on the Cloud Confidence value.
  var testCloudConfidence = qaBitsCloudConfidence.gte(2);

  // Create a mask for the dual QA bit "Cloud Shadow Confidence".
  var bitStartShadowConfidence = 7;
  var bitEndShadowConfidence = 8;
  var qaBitsShadowConfidence = extractQABits(image_qa, bitStartShadowConfidence, bitEndShadowConfidence);
  // Test for shadows, based on the Cloud Shadow Confidence value.
  var testShadowConfidence = qaBitsShadowConfidence.gte(2);

  // Calculate a composite mask and apply it to the image.   
  var maskComposite = (testCloudConfidence.or(testShadowConfidence)).not();
  var imageMasked = image.updateMask(maskComposite);  
  return(imageMasked)
}

How can I merge the masks and let all images share this merged mask?

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The following code shows my approach to

  • get landsat8_sr data from multiple years with multiple images from each year
  • calculate mean value for each year
  • calculate min mask for each year (mask only if cloud appear in all images in a year)
  • calculate max mask for all years (mask as long as cloud appears in one year)
// Landsat 8 Cloud Masking Example (source:https://gis.stackexchange.com/questions/292835/using-cloud-confidence-to-create-cloud-mask-from-landsat-8-bqa)
var RADIX = 2;  // Radix for binary (base 2) data.

var extractQABits = function (qaBand, bitStart, bitEnd) {
  var numBits = bitEnd - bitStart + 1;
  var qaBits = qaBand.rightShift(bitStart).mod(Math.pow(RADIX, numBits));
  //Map.addLayer(qaBits, {min:0, max:(Math.pow(RADIX, numBits)-1)}, 'qaBits');
  return qaBits;
};

var cloudCompositeList = [];

var getMaskedImg = function (i){

  // Reference a sample Landsat 8 TOA image.
  var image = ee.Image(i).clip(geometry);

  // Extract the QA band.
  var image_qa = image.select('pixel_qa');

  // Create a mask for the dual QA bit "Cloud Confidence".
  var bitStartCloudConfidence = 6;
  var bitEndCloudConfidence = 7;
  var qaBitsCloudConfidence = extractQABits(image_qa, bitStartCloudConfidence, bitEndCloudConfidence);
  // Test for clouds, based on the Cloud Confidence value.
  var testCloudConfidence = qaBitsCloudConfidence.gte(2);

  // Create a mask for the dual QA bit "Cloud Shadow Confidence".
  var bitStartShadowConfidence = 8;
  var bitEndShadowConfidence = 9;
  var qaBitsShadowConfidence = extractQABits(image_qa, bitStartShadowConfidence, bitEndShadowConfidence);
  // Test for shadows, based on the Cloud Shadow Confidence value.
  var testShadowConfidence = qaBitsShadowConfidence.gte(2);

  // Calculate a composite mask and apply it to the image.   
  var maskComposite = (testCloudConfidence.or(testShadowConfidence)).not();
  var imageMasked = image.updateMask(maskComposite);

  return(imageMasked);
};


var getCloudComposite = function (i){

  // Reference a sample Landsat 8 TOA image.
  var image = ee.Image(i).clip(geometry);

  // Extract the QA band.
  var image_qa = image.select('pixel_qa');

  // Create a mask for the dual QA bit "Cloud Confidence".
  var bitStartCloudConfidence = 6;
  var bitEndCloudConfidence = 7;
  var qaBitsCloudConfidence = extractQABits(image_qa, bitStartCloudConfidence, bitEndCloudConfidence);
  // Test for clouds, based on the Cloud Confidence value.
  var testCloudConfidence = qaBitsCloudConfidence.gte(2);

  // Create a mask for the dual QA bit "Cloud Shadow Confidence".
  var bitStartShadowConfidence = 8;
  var bitEndShadowConfidence = 9;
  var qaBitsShadowConfidence = extractQABits(image_qa, bitStartShadowConfidence, bitEndShadowConfidence);
  // Test for shadows, based on the Cloud Shadow Confidence value.
  var testShadowConfidence = qaBitsShadowConfidence.gte(2);

  // Calculate a composite mask and apply it to the image.   
  var maskComposite = (testCloudConfidence.or(testShadowConfidence)).not();

  return(maskComposite);
};

var accumulateCloudAnd = function(image, list) {
  var previous = ee.Image(ee.List(list).get(-1));
  var merged = image.and(previous);
  return (ee.List(list)).add(merged);
};

var accumulateCloudOr = function(image, list) {
  var previous = ee.Image(ee.List(list).get(-1));
  var merged = image.or(previous);
  return (ee.List(list)).add(merged);
};



//run mask on all relevant images
var maskedImgs = [];
var maskedImgMeans = [];
var reMaskImg = function(img) {
  return img.mask(img);
};
var bestImg = [];

var allClouds = [];
var allComposite = null;

for (var year = 2014; year < 2019; year++) {
  if (year == 2016)
    continue;
  var startDate = year + '-08-15';
  var endDate = year + '-10-15';

  //get landsat images
  var ic=ee.ImageCollection(Landsat_8_SR
  // Filter the region
  .filter(ee.Filter.eq('WRS_PATH', 231))
  .filter(ee.Filter.eq('WRS_ROW', 62))
  // Filter the time
  .filterDate(startDate, endDate));


  //mask out clouds
  var result = ic.map(getMaskedImg);
  var clouds = ic.map(getCloudComposite);

  var cloudComposite = ee.ImageCollection(ee.List(clouds.iterate(accumulateCloudOr, ee.List([ee.Image(0)]))));
  var cloudCompositeList = cloudComposite.toList(cloudComposite.size());
  var compositeResult = cloudCompositeList.get(cloudCompositeList.length().subtract(1));
  allClouds.push(compositeResult);

  maskedImgs.push(result);
  bestImg.push(result.sort('system:asset_size').first());

  var result2 = result.map(reMaskImg);
  var result3  = result2.reduce(ee.Reducer.mean());

  maskedImgMeans.push(result3);

}


print(maskedImgs);
print(maskedImgMeans);
print(bestImg);
print(allClouds);

var allCloudsCollection = ee.ImageCollection(allClouds);
var allCloudComposite = ee.ImageCollection(ee.List(allCloudsCollection.iterate(
  accumulateCloudAnd, ee.List([ee.Image(1)]))));
var allCloudCompositeList = allCloudComposite.toList(allCloudComposite.size());
var finalComposite = allCloudCompositeList.get(allCloudCompositeList.length().subtract(1))

var im = ee.Image(finalComposite);
Map.addLayer(im.mask(im), {min:0, max:1, pallete:'green'}, 'finalMaskComposite', false);

var getMaskedImgWithComposite = function (i) {
  var image = ee.Image(i).clip(geometry);
  var imageMasked = image.updateMask(finalComposite);
  return(imageMasked);
};

var newMaskedImgs = maskedImgMeans.map(getMaskedImgWithComposite);

print(newMaskedImgs)


//visualise
var x = 0;
for (var year = 2014; year < 2019; year++) { 
  if (year == 2016)
    continue;
  Map.addLayer(newMaskedImgs[x], {bands:"B4_mean,B3_mean,B2_mean", min:-2000, max: 10000}, 'new image'+ year, false);
  x++;
}

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