Landsat Cloud Cover – How to Filter Landsat Images by Cloud Cover Using Google Earth Engine

google-earth-enginelandsattime series

I'm trying to filter Landsat images based on the percent of cloud cover for my ROI. I have tried to adapt code in link one and link two but cannot get what I want. My ROI has been imported as a feaureCollection and covers 8 Landsat tiles.

This is my code

//select images from image collection
var l8 = ee.ImageCollection('LANDSAT/LC08/C01/T1')
//LT05, LE07, LC08
.filterDate('1980-01-01', '2018-12-31')
.filter(ee.Filter.or(
ee.Filter.and(ee.Filter.eq('WRS_PATH', 208),         
              ee.Filter.eq('WRS_ROW', 22)),
ee.Filter.and(ee.Filter.eq('WRS_PATH', 208),         
              ee.Filter.eq('WRS_ROW', 23)),
ee.Filter.and(ee.Filter.eq('WRS_PATH', 208),         
              ee.Filter.eq('WRS_ROW', 24)),              
ee.Filter.and(ee.Filter.eq('WRS_PATH', 207),         
              ee.Filter.eq('WRS_ROW', 22)),              
ee.Filter.and(ee.Filter.eq('WRS_PATH', 207),         
              ee.Filter.eq('WRS_ROW', 23)),              
ee.Filter.and(ee.Filter.eq('WRS_PATH', 207),         
              ee.Filter.eq('WRS_ROW', 24)),
ee.Filter.and(ee.Filter.eq('WRS_PATH', 206),         
              ee.Filter.eq('WRS_ROW', 23)),
ee.Filter.and(ee.Filter.eq('WRS_PATH', 206),         
              ee.Filter.eq('WRS_ROW', 24))));

var c = l8.map(function(img) { return img.clip(my_geom); });

var withCloudiness = c.map(function(image) {
var cloud = ee.Algorithms.Landsat.simpleCloudScore(image).select('cloud');
var cloudiness = cloud.reduceRegion({
reducer: 'mean', 
geometry: my_geom, 
scale: 30,
});

return image.set(cloudiness);
});
var filteredCollection = withCloudiness.filter(ee.Filter.lt('cloud', 20));
print(filteredCollection);

So I have two problems:

1: When using Landsat 8, the code runs, but it does not filter based on a threshold, 20% in this case. When i write the output to a file it gives two columns, cloud_cover and cloud_cover_land, both have observations of cloud greater than 20%.

2: When I try using Landsat 5 or 7 my file is empty…but I know there are scenes that are below 20% cloud cover for Landsat 5.

What I am I doing wrong?

Best Answer

The problem is that simpleCloudScore wants a TOA image and you're passing it raw images. The fact that L8 does anything is simply coincidence.

var c = ee.ImageCollection('LANDSAT/LT05/C01/T1_TOA')
  .filterDate('1980-01-01', '2018-12-31')
  .filter(ee.Filter.rangeContains('WRS_PATH', 206, 208))
  .filter(ee.Filter.rangeContains('WRS_ROW', 22, 24))

var withCloudiness = c.map(function(image) {
  var cloud = ee.Algorithms.Landsat.simpleCloudScore(image).select('cloud');
  var cloudiness = cloud.reduceRegion({
    reducer: 'mean', 
    geometry: my_geom, 
    scale: 30,
  });

  return image.set(cloudiness);
});

var filteredCollection = withCloudiness.filter(ee.Filter.lt('cloud', 20));

print(withCloudiness.size())
print(filteredCollection.size());

Related Solutions

Google Earth Engine Landsat – How to Filter by Cloud Cover Over Multiple Polygons

The error is quite clear. Parameter geometry of function reduceRegion must be a Geometry. In the documentation says:

geometry (Geometry, default: null): The region over which to reduce data. Defaults to the footprint of the image's first band.

So, if you want to reduce over fc:

var fc = ee.FeatureCollection('ft:omit');

  var imgcollection = ee.ImageCollection("LANDSAT/LC08/C01/T1_RT_TOA");

  var combine = imgcollection.map(function(img) { return img.clip(fc); });

  var withCloudiness = combine.map(function(image) {
    var cloud =       ee.Algorithms.Landsat.simpleCloudScore(image).select('cloud');
    var cloudiness = cloud.reduceRegion({
      reducer: 'mean', 
      geometry: fc.geometry(), 
      scale: 30,
      maxPixels: 1e13,
    });
    return image.set(cloudiness);
  });

  var filteredCollection = withCloudiness.filter(ee.Filter.lt('cloud', 10));
  print(filteredCollection);

Google Earth Engine – Using Cloud Confidence to Create Cloud Mask from Landsat 8 BQA

Here is a more flexible approach that can handle dual (or larger) bit patterns. The bit shifts are performed server-side, using the ee.Image.rightShift() and ee.Image.mod() methods.

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;
};

Once the bits are extracted, you can test them against a desired pattern using comparison operators (such as ee.Image.lte())

Below is a (verbose) example of masking out clouds and shadows in a Landsat 8 TOA image using its quality band (BQA). Clouds can be identified using the "Cloud Confidence" bits:

Bits 5-6: Cloud Confidence  
  0: Not Determined / Condition does not exist. \
  1: Low, (0-33 percent confidence)
  2: Medium, (34-66 percent confidence)
  3: High, (67-100 percent confidence)

and shadows can be identified using the "Cloud Shadow Confidence" bits:

Bits 7-8: Cloud Shadow Confidence
  0: Not Determined / Condition does not exist.
  1: Low, (0-33 percent confidence)
  2: Medium, (34-66 percent confidence)
  3: High, (67-100 percent confidence)

Here is the full script which displays the final masked image an various intermediate step as map layers:

// Landsat 8 Cloud Masking Example

var RADIX = 2;  // Radix for binary (base 2) data.

// Reference a sample Landsat 8 TOA image.
var image = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_043034_20180202');
// Extract the QA band.
var image_qa = image.select('BQA');

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;
};

// 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);

Map.addLayer(image, {bands:"B4,B3,B2", min:0, max:0.3}, 'original image', false);
Map.addLayer(image.select('BQA'), {min:0, max:10000}, 'BQA', false);
Map.addLayer(testCloudConfidence.mask(testCloudConfidence), {min:0, max:1, palette:'grey,white'}, 'testCloudConfidence');
Map.addLayer(testShadowConfidence.mask(testShadowConfidence), {min:0, max:1, palette:'grey,black'}, 'testShadowConfidence');
Map.addLayer(maskComposite.mask(maskComposite), {min:0, max:1, palette:'green'}, 'maskComposite', false);
Map.addLayer(imageMasked, {bands:"B4,B3,B2", min:0, max:0.3}, 'imageMasked');

Script link: https://code.earthengine.google.com/d1de93debe720642c02b1d5b3a57fb82

Depending on your particular use case, you may want to adjust the sensitivity to clouds and shadows by modifying testCloudConfidence and testShadowConfidence.

Best Answer

Related Solutions

Google Earth Engine Landsat – How to Filter by Cloud Cover Over Multiple Polygons

Google Earth Engine – Using Cloud Confidence to Create Cloud Mask from Landsat 8 BQA

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