Google Earth Engine - Long-term mean loading using Sentinel-1 data (db value)

// Import Sentinel No. 1 data

var S1 = ee.ImageCollection('COPERNICUS/S1_GRD')

.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))

.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))

.filter(ee.Filter.eq('instrumentMode', 'IW'))

.filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))

.filterBounds(roi)

.filterDate('2014-01-01','2023-12-31')

.select(["VV","VH"])

//Convert to DB value

function toNatural(img) {

return ee.Image(10.0).pow(img.select(0).divide(10.0));

}

//Function to convert to dB

function toDB(img) {

return ee.Image(img).log10().multiply(10.0);

}

//Apply SNAP 3.0 S1TBX encoded refined Li's spot filter:

///senbox-org/s1tbx/blob/master/s1tbx-op-sar-processing/src/main/java/org/esa/s1tbx/sar/gpf/filtering/SpeckleFilters/

//Adapted from Guido Lemoine

function RefinedLee(img) {

// img must use natural units, i.e. not dB! Setting up the 3x3 kernel

// Convert to natural unit!

var myimg = toNatural(img);

var weights3 = ee.List.repeat(ee.List.repeat(1,3),3);

var kernel3 = ee.Kernel.fixed(3,3, weights3, 1, 1, false);

var mean3 = myimg.reduceNeighborhood(ee.Reducer.mean(), kernel3);

var variance3 = myimg.reduceNeighborhood(ee.Reducer.variance(), kernel3);

// Use 3x3 window samples in 7x7 windows to determine gradients and orientations

var sample_weights = ee.List([[0,0,0,0,0,0,0], [0,1,0,1,0,1,0],[0,0,0,0,0,0,0], [0,1,0,1,0,1,0], [0,0,0,0,0,0,0], [0,1,0,1,0,1,0],[0,0,0,0,0,0,0]]);

var sample_kernel = ee.Kernel.fixed(7,7, sample_weights, 3,3, false);

// Calculate the average value and variance of the sampling window and store it as 9 bands

var sample_mean = mean3.neighborhoodToBands(sample_kernel);

var sample_var = variance3.neighborhoodToBands(sample_kernel);

// Determine the 4 gradients of the sampling window

var gradients = sample_mean.select(1).subtract(sample_mean.select(7)).abs();

gradients = gradients.addBands(sample_mean.select(6).subtract(sample_mean.select(2)).abs());

gradients = gradients.addBands(sample_mean.select(3).subtract(sample_mean.select(5)).abs());

gradients = gradients.addBands(sample_mean.select(0).subtract(sample_mean.select(8)).abs());

// Find the maximum gradient in the gradient band

var max_gradient = gradients.reduce(ee.Reducer.max());

// Create mask for striped pixels of maximum gradient

var gradmask = gradients.eq(max_gradient);

// Repeated gradient mask band: Each gradient represents 2 directions

gradmask = gradmask.addBands(gradmask);

// Determine 8 directions

var directions = sample_mean.select(1).subtract(sample_mean.select(4)).gt(sample_mean.select(4).subtract(sample_mean.select(7))).multiply(1);

directions = directions.addBands(sample_mean.select(6).subtract(sample_mean.select(4)).gt(sample_mean.select(4).subtract(sample_mean.select(2))).multiply(2));

directions = directions.addBands(sample_mean.select(3).subtract(sample_mean.select(4)).gt(sample_mean.select(4).subtract(sample_mean.select(5))).multiply(3));

directions = directions.addBands(sample_mean.select(0).subtract(sample_mean.select(4)).gt(sample_mean.select(4).subtract(sample_mean.select(8))).multiply(4));

// The next 4 are the not() of the previous 4

directions = directions.addBands(directions.select(0).not().multiply(5));

directions = directions.addBands(directions.select(1).not().multiply(6));

directions = directions.addBands(directions.select(2).not().multiply(7));

directions = directions.addBands(directions.select(3).not().multiply(8));

// Block all values ​​not 1-8

directions = directions.updateMask(gradmask);

// "fold" the stack into a single band image (due to masking, each pixel has only one value (1-8) in its direction band, otherwise it will be blocked).

directions = directions.reduce(ee.Reducer.sum());

var sample_stats = sample_var.divide(sample_mean.multiply(sample_mean));

// Calculate local noise variance

var sigmaV = sample_stats.toArray().arraySort().arraySlice(0,0,5).arrayReduce(ee.Reducer.mean(), [0]);

// Set 7*7 kernel for directional statistics

var rect_weights = ee.List.repeat(ee.List.repeat(0,7),3).cat(ee.List.repeat(ee.List.repeat(1,7),4));

var diag_weights = ee.List([[1,0,0,0,0,0,0], [1,1,0,0,0,0,0], [1,1,1,0,0,0,0],

[1,1,1,1,0,0,0], [1,1,1,1,1,0,0], [1,1,1,1,1,1,0], [1,1,1,1,1,1,1]]);

var rect_kernel = ee.Kernel.fixed(7,7, rect_weights, 3, 3, false);

var diag_kernel = ee.Kernel.fixed(7,7, diag_weights, 3, 3, false);

// Create mean and variance stacks using the original kernel. Block the relevant directions.

var dir_mean = myimg.reduceNeighborhood(ee.Reducer.mean(), rect_kernel).updateMask(directions.eq(1));

var dir_var = myimg.reduceNeighborhood(ee.Reducer.variance(), rect_kernel).updateMask(directions.eq(1));

dir_mean = dir_mean.addBands(myimg.reduceNeighborhood(ee.Reducer.mean(), diag_kernel).updateMask(directions.eq(2)));

dir_var = dir_var.addBands(myimg.reduceNeighborhood(ee.Reducer.variance(), diag_kernel).updateMask(directions.eq(2)));

// and add bands to the rotating kernel

for (var i=1; i<4; i++) {

dir_mean = dir_mean.addBands(myimg.reduceNeighborhood(ee.Reducer.mean(), rect_kernel.rotate(i)).updateMask(directions.eq(2*i+1)));

dir_var = dir_var.addBands(myimg.reduceNeighborhood(ee.Reducer.variance(), rect_kernel.rotate(i)).updateMask(directions.eq(2*i+1)));

dir_mean = dir_mean.addBands(myimg.reduceNeighborhood(ee.Reducer.mean(), diag_kernel.rotate(i)).updateMask(directions.eq(2*i+2)));

dir_var = dir_var.addBands(myimg.reduceNeighborhood(ee.Reducer.variance(), diag_kernel.rotate(i)).updateMask(directions.eq(2*i+2)));

}

// "fold" the stack into a single band image (due to masking, each pixel has only one value in its direction band, otherwise it will be blocked).

dir_mean = dir_mean.reduce(ee.Reducer.sum());

dir_var = dir_var.reduce(ee.Reducer.sum());

// A Finally generates filtered value

var varX = dir_var.subtract(dir_mean.multiply(dir_mean).multiply(sigmaV)).divide(sigmaV.add(1.0));

var b = varX.divide(dir_var);

var result = dir_mean.add(b.multiply(myimg.subtract(dir_mean)));

//return(result);

return(img.addBands(ee.Image(toDB(result.arrayGet(0))).rename("filter")));

}

var collection = S1.map(RefinedLee);

var col = ee.ImageCollection(collection.select("filter"));

print(ui.Chart.image.series(col, roi, ee.Reducer.mean(), 20).setOptions({

title: 'TimeSeries analysis',

lineWidth: 1,

pointSize: 3 }));