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Are radar images already processed with speckle filter and in decibels? Below is my code, I am looking for a way to adapt it to those processes, though I don't know if the speckle can be added before obtaining the minimum and maximum values.

//seleccionar la region de donde se realizará la descarga
var table = table.filter(ee.Filter.eq('NOMBRE_DPT', 'CASANARE'));

var sentinel1 = ee.ImageCollection('COPERNICUS/S1_GRD')
                    .filterDate('2021-01-01', '2021-01-15')
                    .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
                    .filter(ee.Filter.eq('instrumentMode', 'IW'))
                    .filter(ee.Filter.or((ee.Filter.eq('orbitProperties_pass', 'ASCENDING'), ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))))
                    .filterBounds(table);
var sentinel2 = ee.ImageCollection('COPERNICUS/S1_GRD')
                    .filterDate('2021-01-16', '2021-01-30')
                    .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
                    .filter(ee.Filter.eq('instrumentMode', 'IW'))
                    .filter(ee.Filter.or((ee.Filter.eq('orbitProperties_pass', 'ASCENDING'), ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))))
                    .filterBounds(table);
var sentinel3 = ee.ImageCollection('COPERNICUS/S1_GRD')
                    .filterDate('2021-02-01', '2021-02-15')
                    .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
                    .filter(ee.Filter.eq('instrumentMode', 'IW'))
                    .filter(ee.Filter.or((ee.Filter.eq('orbitProperties_pass', 'ASCENDING'), ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))))
                    .filterBounds(table);
var sentinel4 = ee.ImageCollection('COPERNICUS/S1_GRD')
                    .filterDate('2021-02-16', '2021-02-28')
                    .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
                    .filter(ee.Filter.eq('instrumentMode', 'IW'))
                    .filter(ee.Filter.or((ee.Filter.eq('orbitProperties_pass', 'ASCENDING'), ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))))
                    .filterBounds(table);
                    
var sentinel5 = ee.ImageCollection('COPERNICUS/S1_GRD')
                    .filterDate('2021-03-01', '2021-03-15')
                    .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
                    .filter(ee.Filter.eq('instrumentMode', 'IW'))
                    .filter(ee.Filter.or((ee.Filter.eq('orbitProperties_pass', 'ASCENDING'), ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))))
                    .filterBounds(table);
var sentinel6 = ee.ImageCollection('COPERNICUS/S1_GRD')
                    .filterDate('2021-03-16', '2021-03-31')
                    .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
                    .filter(ee.Filter.eq('instrumentMode', 'IW'))
                    .filter(ee.Filter.or((ee.Filter.eq('orbitProperties_pass', 'ASCENDING'), ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))))
                     .filterBounds(table);   

var image1 = sentinel1.select('VH').mean();
var image2 = sentinel2.select('VH').mean();
var image3 = sentinel3.select('VH').mean();
var image4 = sentinel4.select('VH').mean();
var image5 = sentinel5.select('VH').mean();
var image6 = sentinel6.select('VH').mean();


// Crear un stacking de todas las bandas radar

var stacked = ee.ImageCollection.fromImages([image1,image2,image3,image4,image5,image6]);

// Scale all the images

var stacked_scaled = stacked.map(function(image){
  return ee.Image(image).multiply(10).add(350).uint8();
  });
  
//crear minimo y maximo

var min = stacked_scaled.min();
var max = stacked_scaled.max();

Map.addLayer(min, {}, 'min');
Map.addLayer(max, {}, 'max');

print(min);
print(max);

// diferencia entre minimo y maximo
var diff = min.subtract(max);
Map.addLayer(diff,{},'subtraction');

print(diff);
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  • COPERNICUS/S1_GRD is in decibels and COPERNICUS/S1_GRD_FLOAT is linear. Speckle has not been filtered in any of the collections. Commented Aug 18, 2022 at 11:04

1 Answer 1

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You may not have access to this paper but Lees et al. (2021) use a simple point buffer to reduce speckle interference, and they're also using Sentinel-1 GRD like you. In their validation with on-the-ground soil moisture indices, it improved correlation by a modest 10% but it's something.

I can't provide any sample code specific to your task here because I don't see any features you're working with, but a simple way I did it with mine is:

var start = '2014-10-03'
var end = '2021-12-31'

var imgVV = ee.ImageCollection('COPERNICUS/S1_GRD')
        .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))
        .filter(ee.Filter.eq('instrumentMode', 'IW'))
        .filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))
        .filterDate(start, end)
        .filterBounds(MftF_E)
        .select('VV','angle')
        .map(function(image) {
          var edge = image.lt(-30.0);
          var maskedImage = image.mask().and(edge.not());
          return image.updateMask(maskedImage);
        });

var desc = imgVV.select('VV');

var MftF_E_buffered = MftF_E.buffer({'distance': 50})
print("Point buffer:", MftF_E_buffered)

var chart = ui.Chart.image.series(
  desc, MftF_E_buffered, ee.Reducer.mean());
chart.setOptions({title:'Buffered'});
print(chart);

Map.setCenter(-1.8554,53.3826, 13);
Map.addLayer(MftF_E,
             {'color': 'black'},
             'Geometry [black]: point');
Map.addLayer(MftF_E_buffered,
             {'color': 'red'},
             'Result [red]: point.buffer');

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