I am trying to create a 2 cloudless composites of Sentinel-2 imagery over a region of interest that is pretty cloudy. I found an example script in GEE java script API that does a pretty aggressive cloud masking, however, when I tried to run it for a larger region I get this error:
median Before: Tile error: User memory limit exceeded.
I tried increasing the tileScale parameter to 16, but it didn't solve my issue. Is there a way to make this work?
Here is the code I tried so far:
// Sentinel-2 Level 1C data. Bands B7, B8, B8A and B10 from this
// dataset are needed as input to CDI and the cloud mask function.
var s2 = ee.ImageCollection('COPERNICUS/S2');
// Cloud probability dataset. The probability band is used in
// the cloud mask function.
var s2c = ee.ImageCollection('COPERNICUS/S2_CLOUD_PROBABILITY');
// Sentinel-2 surface reflectance data for the composite.
var s2Sr = ee.ImageCollection('COPERNICUS/S2_SR');
// Dates over which to create a median composite.
var start = ee.Date('2021-11-15');
var end = ee.Date('2021-12-04');
// S2 L1C for Cloud Displacement Index (CDI) bands.
s2 = s2.filterBounds(StudyArea).filterDate(start, end)
.select(['B7', 'B8', 'B8A', 'B10']);
// S2Cloudless for the cloud probability band.
s2c = s2c.filterDate(start, end).filterBounds(StudyArea);
// S2 L2A for surface reflectance bands.
s2Sr = s2Sr.filterDate(start, end).filterBounds(StudyArea)
.select(['B2', 'B3', 'B4', 'B5']);
// Join two collections on their 'system:index' property.
// The propertyName parameter is the name of the property
// that references the joined image.
function indexJoin(collectionA, collectionB, propertyName) {
var joined = ee.ImageCollection(ee.Join.saveFirst(propertyName).apply({
primary: collectionA,
secondary: collectionB,
condition: ee.Filter.equals({
leftField: 'system:index',
rightField: 'system:index'})
}));
// Merge the bands of the joined image.
return joined.map(function(image) {
return image.addBands(ee.Image(image.get(propertyName)));
});
}
// Aggressively mask clouds and shadows.
function maskImage(image) {
// Compute the cloud displacement index from the L1C bands.
var cdi = ee.Algorithms.Sentinel2.CDI(image);
var s2c = image.select('probability');
var cirrus = image.select('B10').multiply(0.0001);
// Assume low-to-mid atmospheric clouds to be pixels where probability
// is greater than 65%, and CDI is less than -0.5. For higher atmosphere
// cirrus clouds, assume the cirrus band is greater than 0.01.
// The final cloud mask is one or both of these conditions.
var isCloud = s2c.gt(65).and(cdi.lt(-0.5)).or(cirrus.gt(0.01));
// Reproject is required to perform spatial operations at 20m scale.
// 20m scale is for speed, and assumes clouds don't require 10m precision.
isCloud = isCloud.focal_min(3).focal_max(16);
isCloud = isCloud.reproject({crs: cdi.projection(), scale: 20});
// Project shadows from clouds we found in the last step. This assumes we're working in
// a UTM projection.
var shadowAzimuth = ee.Number(90)
.subtract(ee.Number(image.get('MEAN_SOLAR_AZIMUTH_ANGLE')));
// With the following reproject, the shadows are projected 5km.
isCloud = isCloud.directionalDistanceTransform(shadowAzimuth, 50);
isCloud = isCloud.reproject({crs: cdi.projection(), scale: 100});
isCloud = isCloud.select('distance').mask();
return image.select('B2', 'B3', 'B4').updateMask(isCloud.not());
}
// Join the cloud probability dataset to surface reflectance.
var withCloudProbability = indexJoin(s2Sr, s2c, 'cloud_probability');
// Join the L1C data to get the bands needed for CDI.
var withS2L1C = indexJoin(withCloudProbability, s2, 'l1c');
// Map the cloud masking function over the joined collection.
var masked = ee.ImageCollection(withS2L1C.map(maskImage));
// Take the median, specifying a tileScale to avoid memory errors.
var median = masked.reduce(ee.Reducer.median(), 16);
// Display the results.
var viz = {bands: ['B4_median', 'B3_median', 'B2_median'], min: 0, max: 3000};
Map.addLayer(median, viz, 'median');
