I started using GEE recently. I wanted to apply the non-parametric Mann-Kendall method to a collection of Sentinel-1 images. However there is a memory error in the code and this is my problem. Link code:https://code.earthengine.google.com/4bf1dbbcd116c8e3f5c584fbb45e8c19
var quadricula = ee.Geometry.Polygon([[-51.20732599322313,-15.60026586116677],[-51.20732599322313,-15.455379154650098],[-51.262600956602036,-15.455379154650098],[-51.262600956602036,-15.60026586116677]]);
//Map.addLayer(quadricula);
// Filter collection to dates of interest.
var Sentinel1 = ee.ImageCollection('COPERNICUS/S1_GRD')
.filterDate('2015-01-01', '2022-01-01')
.filterBounds(quadricula);
print('Quantidade de imagens no intervalo definido:',Sentinel1.size());
//===========================================================================//
var wrapper = require('users/adugnagirma/gee_s1_ard:wrapper');
var helper = require('users/adugnagirma/gee_s1_ard:utilities');
//---------------------------------------------------------------------------//
// DEFINE PARAMETERS
//---------------------------------------------------------------------------//
//var geometry = ee.Geometry.Polygon([[-51.20732599322313,-15.60026586116677],[-51.20732599322313,-15.455379154650098],[-51.262600956602036,-15.455379154650098],[-51.262600956602036,-15.60026586116677]]);
//Map.addLayer(geometry);
var parameter = {//1. Data Selection
START_DATE: "2015-01-01",
STOP_DATE: "2022-01-01",
POLARIZATION:'VVVH',
ORBIT : 'BOTH',
GEOMETRY: quadricula, //uncomment if interactively selecting a region of interest
//GEOMETRY: ee.Geometry.Polygon([[[104.80, 11.61],[104.80, 11.36],[105.16, 11.36],[105.16, 11.61]]], null, false), //Uncomment if providing coordinates
//GEOMETRY: ee.Geometry.Polygon([[[112.05, -0.25],[112.05, -0.45],[112.25, -0.45],[112.25, -0.25]]], null, false),
//2. Additional Border noise correction
APPLY_ADDITIONAL_BORDER_NOISE_CORRECTION: true,
//3.Speckle filter
APPLY_SPECKLE_FILTERING: true,
SPECKLE_FILTER_FRAMEWORK: 'MULTI',
SPECKLE_FILTER: 'REFINED LEE',
SPECKLE_FILTER_KERNEL_SIZE: 5,
SPECKLE_FILTER_NR_OF_IMAGES: 10,
//4. Radiometric terrain normalization
APPLY_TERRAIN_FLATTENING: true,
DEM: ee.Image('NASA/NASADEM_HGT/001'),
TERRAIN_FLATTENING_MODEL: 'VOLUME',
TERRAIN_FLATTENING_ADDITIONAL_LAYOVER_SHADOW_BUFFER: 0,
//5. Output
FORMAT : 'DB',
CLIP_TO_ROI: true,
SAVE_ASSETS: false
};
//---------------------------------------------------------------------------//
// DO THE JOB
//---------------------------------------------------------------------------//
//Preprocess the S1 collection
var s1_preprocces = wrapper.s1_preproc(parameter);
var s1 = s1_preprocces[0];
s1_preprocces = s1_preprocces[1];
//---------------------------------------------------------------------------//
// VISUALIZE
//---------------------------------------------------------------------------//
//Visulaization of the first image in the collection in RGB for VV, VH, images
var visparam = {};
if (parameter.POLARIZATION=='VVVH'){
if (parameter.FORMAT=='DB'){
var s1_preprocces_view = s1_preprocces.map(helper.add_ratio_lin).map(helper.lin_to_db2);
var s1_view = s1.map(helper.add_ratio_lin).map(helper.lin_to_db2);
visparam = {bands:['VV','VH','VVVH_ratio'],min: [-20, -25, 1],max: [0, -5, 15]};
}
else {
var s1_preprocces_view = s1_preprocces.map(helper.add_ratio_lin);
var s1_view = s1.map(helper.add_ratio_lin);
visparam = {bands:['VV','VH','VVVH_ratio'], min: [0.01, 0.0032, 1.25],max: [1, 0.31, 31.62]};
}
}
else {
if (parameter.FORMAT=='DB') {
s1_preprocces_view = s1_preprocces.map(helper.lin_to_db);
s1_view = s1.map(helper.lin_to_db);
visparam = {bands:[parameter.POLARIZATION],min: -25,max: 0} ;
}
else {
s1_preprocces_view = s1_preprocces;
s1_view = s1;
visparam = {bands:[parameter.POLARIZATION],min: 0,max: 0.2};
}
}
// Calcula o MRFDI
var MRFDI = s1_preprocces.map(function(image) {
return image.normalizedDifference(['VV', 'VH']).rename('MRFDI');
});
var addMRFDI = function (image) {
var MRFDI = image.expression('(VV - VH)/(VV + VH)', {
'VV' : image.select('VV'),
'VH' : image.select('VH'),
}).float();
return image.addBands(MRFDI.rename('MRFDI'));
};
var MRFDI = s1_preprocces.map(addMRFDI)
.select('MRFDI');
// Calcula o RVI
var RVI = s1_preprocces.map(function(image) {
return image.normalizedDifference(['VV', 'VH']).rename('RVI');
});
var addRVI = function (image) {
var RVI = image.expression('(4*VH)/(VV + VH)', {
'VV' : image.select('VV'),
'VH' : image.select('VH'),
}).float();
return image.addBands(RVI.rename('RVI'));
};
var RVI = s1_preprocces.map(addRVI)
.select('RVI');
Map.centerObject(parameter.GEOMETRY, 12);
Map.addLayer(s1_view.first(), visparam, 'First image in the input S1 collection', true);
Map.addLayer(s1_preprocces_view.first(), visparam, 'First image in the processed S1 collection', true);
//---------------------------------------------------------------------------//
// EXPORT
//---------------------------------------------------------------------------//
//Convert format for export
if (parameter.FORMAT=='DB'){
s1_preprocces = s1_preprocces.map(helper.lin_to_db);
}
//Save processed collection to asset
if(parameter.SAVE_ASSETS) {
helper.Download.ImageCollection.toAsset(s1_preprocces, '',
{scale: 10,
region: s1_preprocces.geometry(),
type: 'float'});
}
print('Quantidade de imagens s1_preprocces:',s1_preprocces.size());
// Define an image collection time series to chart, MODIS vegetation indices
// in this case.
var imgCol = s1_preprocces
.select(['VV', 'VH']);
//=================================================================================//
//mann-kendall
var coll = s1_preprocces.map(function(image) {
return image.select().addBands(image.normalizedDifference(['VH', 'VV']));
})
.filter(ee.Filter.calendarRange(8, 9, 'month'));
Map.addLayer(coll, {}, 'coll');
var afterFilter = ee.Filter.lessThan({
leftField: 'system:time_start',
rightField: 'system:time_start'
});
var joined = ee.ImageCollection(ee.Join.saveAll('after').apply({
primary: coll,
secondary: coll,
condition: afterFilter
}));
var sign = function(i, j) { // i and j are images
return ee.Image(j).neq(i) // Zero case
.multiply(ee.Image(j).subtract(i).clamp(-1, 1)).int();
};
var kendall = ee.ImageCollection(joined.map(function(current) {
var afterCollection = ee.ImageCollection.fromImages(current.get('after'));
return afterCollection.map(function(image) {
// The unmask is to prevent accumulation of masked pixels that
// result from the undefined case of when either current or image
// is masked. It won't affect the sum, since it's unmasked to zero.
return ee.Image(sign(current, image)).unmask(0);
});
// Set parallelScale to avoid User memory limit exceeded.
}).flatten()).reduce('sum', 2);
var palette_sens = {min: -0.001, max: 0.001, palette: [
'red', 'white', 'green']};
var palette = ['red', 'white', 'green'];
// Stretch this as necessary.g
Map.addLayer(kendall, {palette: palette}, 'kendall');
var slope = function(i, j) { // i and j are images
return ee.Image(j).subtract(i)
.divide(ee.Image(j).date().difference(ee.Image(i).date(), 'days'))
.rename('slope')
.float();
};
var slopes = ee.ImageCollection(joined.map(function(current) {
var afterCollection = ee.ImageCollection.fromImages(current.get('after'));
return afterCollection.map(function(image) {
return ee.Image(slope(current, image));
});
}).flatten());
var sensSlope = slopes.reduce(ee.Reducer.median(), 2); // Set parallelScale.
Map.addLayer(sensSlope, palette_sens, 'sensSlope');
var epochDate = ee.Date('1970-01-01');
var sensIntercept = coll.map(function(image) {
var epochDays = image.date().difference(epochDate, 'days').float();
return image.subtract(sensSlope.multiply(epochDays)).float();
}).reduce(ee.Reducer.median(), 2);
Map.addLayer(sensIntercept, {}, 'sensIntercept');
Map.addLayer(table, {color: 'blue'});
I hope to resolve this impasse without changing the adopted time interval and reducing the size of the study area.
