├── .gitattributes ├── Dual_pol_descriptors_GEE.js ├── GEE_App_Demo.mp4 ├── Jupyter Notebooks ├── .ipynb_checkpoints │ └── 1.Dual-pol descriptors-checkpoint.ipynb ├── 1.Dual-pol descriptors.ipynb └── data │ ├── ALOS_HH_2020_10km.tif │ └── ALOS_HV_2020_10km.tif ├── LICENSE └── README.md /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /Dual_pol_descriptors_GEE.js: -------------------------------------------------------------------------------- 1 | /*----------------------------------------------------------------------------------------------------- 2 | 3 | This GEE script calculates dual-polarimetric descriptors 4 | (co-pol purity parameter mc, Pseudo scattering entropy Hc and psuedo scattering type parameter Theta_c) 5 | for Sentinel-1 GRD data. 6 | 7 | OUTPUT: 8 | 9 | 1) Temporal Sentinel-1 dialy mosaic scenes over the given ROI with following paramters as layers : 10 | Hc, Theta_c, mc, class, ratio, VV, VH, inc 11 | 12 | 2) Extracted values of the above paramters for given list of sampling points in *.csv format 13 | 14 | 15 | Author Details: 16 | Narayana Rao B. 17 | 206-MRSLab, CSRE, 18 | IIT Bombay, India. 19 | email: bnarayanarao@iitb.ac.in 20 | web: https://narayana-rao.github.io 21 | 22 | A detailed explanation of the implemented algorithm can be found in the following articles. 23 | 24 | Narayanarao Bhogapurapu, Subhadip Dey, Avik Bhattacharya, Dipankar Mandal, 25 | Juan Lopez-Sanchez, Heather McNairn, Carlos Lopez-Martinez and Y. S. Rao 2021 26 | “Dual-polarimetric descriptors from Sentinel-1 GRD SAR data for crop growth assessment”. 27 | ISPRS Journal of Photogrammetry and Remote Sensing. 20-35, 178. 28 | doi: 10.1016/j.isprsjprs.2021.05.013 29 | 30 | Narayanarao Bhogapurapu, Subhadip Dey, Dipankar Mandal, Avik Bhattacharya, 31 | L. Karthikeyan, Heather McNairn and Y. S. Rao 2022 32 | “Soil Moisture Retrieval Over Croplands Using dual-pol L-band GRD SAR Data”. 33 | Remote Sensing of Environment. Volume 271, 2022, Pages 112900, ISSN 0034-4257 34 | doi: 10.1016/j.rse.2022.112900 35 | -------------------------------------------------------------------------------------------------------*/ 36 | 37 | 38 | /*---------------------------------------------------------------------------------------------- 39 | 40 | 1) Import ROI(extent) and sampling points 41 | 42 | ----------------------------------------------------------------------------------------------*/ 43 | 44 | 45 | var extent = 46 | ee.Geometry.Polygon( 47 | [[[-97.33036743800388, 49.68259937829183], 48 | [-97.33036743800388, 49.55804339464324], 49 | [-97.20951782862888, 49.55804339464324], 50 | [-97.20951782862888, 49.68259937829183]]], null, false); 51 | 52 | var sample_pts = ee.FeatureCollection([ 53 | ee.Feature(ee.Geometry.Point(-98.04639258,49.68454278), {label: 'P1'}), 54 | ee.Feature(ee.Geometry.Point(-98.04642806,49.68251043), {label: 'P2'}), 55 | ee.Feature(ee.Geometry.Point(-98.04643989,49.68183298), {label: 'P3'}), 56 | ee.Feature(ee.Geometry.Point(-98.04646585,49.68116492), {label: 'P4'}), 57 | 58 | ]); 59 | 60 | 61 | /*---------------------------------------------------------------------------------------------- 62 | 63 | 2) Cloud filtering and data preparation 64 | 65 | ----------------------------------------------------------------------------------------------*/ 66 | 67 | 68 | var ref_start=ee.Date('2016-08-15'); 69 | var ref_end = ee.Date('2016-09-30'); 70 | 71 | var window_size = 2.5; //window size for filtering 72 | print('window size',window_size*2); 73 | 74 | var S1 = ee.ImageCollection('COPERNICUS/S1_GRD') 75 | .filterDate(ref_start, ref_end) 76 | .filter(ee.Filter.eq('instrumentMode', 'IW')) 77 | .filter(ee.Filter.eq('orbitProperties_pass', 'ASCENDING')) 78 | .select('VV','VH','angle') 79 | .sort('system:time_start', false) 80 | .filterBounds(extent); 81 | 82 | 83 | // Difference in days between start and finish 84 | var diff = ref_end.difference(ref_start, 'day'); 85 | 86 | // Make a list of all dates 87 | var range = ee.List.sequence(0, diff.subtract(1)).map(function(day) 88 | {return ref_start.advance(day,'day')}) 89 | 90 | // Funtion for iteraton over the range of dates 91 | var day_mosaics = function(date, newlist) { 92 | // Cast 93 | date = ee.Date(date) 94 | newlist = ee.List(newlist) 95 | 96 | // Filter collection between date and the next day 97 | var filtered = S1.filterDate(date, date.advance(1,'day')) 98 | 99 | // Make the mosaic 100 | var image = ee.Image(filtered.mosaic()); 101 | // copy image meta 102 | image = image 103 | .set('system:time_start', filtered.first().get('system:time_start')) 104 | .set('system:index', filtered.first().get('system:index')) 105 | .set('system:id', filtered.first().get('system:id')) 106 | .set('system:version', filtered.first().get('system:version')) 107 | // .set('system:bands', filtered.first().get('system:bands')) 108 | .set('system:footprint', filtered.first().get('system:footprint')) 109 | ; 110 | 111 | // Add the mosaic to a list only if the collection has images 112 | return ee.List(ee.Algorithms.If(filtered.size(), newlist.add(image), newlist)) 113 | } 114 | 115 | // Iterate over the range to make a new list, and then cast the list to an imagecollection 116 | var newS1col = ee.ImageCollection(ee.List(range.iterate(day_mosaics, ee.List([])))) 117 | 118 | 119 | /*---------------------------------------------------------------------------------------------- 120 | 121 | 3) Generating Dual-pol descriptors and the clusters 122 | 123 | ----------------------------------------------------------------------------------------------*/ 124 | 125 | 126 | var m = newS1col.map(function(image) { 127 | var C11_mean = image.expression( '10 ** (VV / 10)', {'VV': image.select('VV')}) 128 | .reduceNeighborhood({ 129 | reducer: ee.Reducer.mean(), 130 | kernel: ee.Kernel.square(window_size) 131 | }); 132 | var C22_mean = image.expression( '10 ** (VH / 10)', {'VH': image.select('VH')}) 133 | .reduceNeighborhood({ 134 | reducer: ee.Reducer.mean(), 135 | kernel: ee.Kernel.square(window_size) 136 | }); 137 | 138 | var span = C11_mean.add(C22_mean); 139 | var ratio = C22_mean.divide(C11_mean); 140 | var vmask = C11_mean.subtract(C22_mean); 141 | vmask = vmask.expression('b(0) >0? 1:0'); 142 | 143 | var m = (C11_mean.subtract(C22_mean).abs()).divide(span); 144 | var d_dpol = m.multiply(m).subtract(1).multiply(-1); 145 | var theta_c = ((C11_mean.subtract(C22_mean).abs()).multiply(span).multiply(m)) 146 | .divide((C11_mean.multiply(C22_mean)).add(span.pow(2).multiply(m.pow(2)))) 147 | .atan(); 148 | theta_c = theta_c.multiply(180).divide(Math.PI); 149 | 150 | var p1 = C11_mean.divide(span); 151 | var p2 = C22_mean.divide(span); 152 | var cnst = ee.Number(2); 153 | var Hp1 = p1.multiply(p1.log10()).divide(cnst.log10()).multiply(-1); 154 | var Hp2 = p2.multiply(p2.log10()).divide(cnst.log10()).multiply(-1); 155 | var H = Hp1.add(Hp2); 156 | var q = ratio; 157 | var DpRVIc_n = q.multiply(q.add(ee.Number(3))); 158 | var DpRVIc_d = (q.add(ee.Number(1))).multiply(q.add(ee.Number(1))); 159 | var DpRVIc = DpRVIc_n.divide(DpRVIc_d); 160 | 161 | 162 | var H_rc = H.expression('b(0) >0 && b(0)<0.3 ? 1 : b(0) > 0.3 && b(0) <0.5? 2 : b(0)>0.5&&b(0) < 0.7 ? 3 :b(0)>0.7 && b(0)<1.0 ? 4: 0'); 163 | var theta_c_rc = theta_c.expression('b(0)>0.0 && b(0) <15 ? 5 : b(0)>15 && b(0)<30 ? 6 : b(0)>30 && b(0) < 45? 7 : 0'); 164 | var C11_mean_db = C11_mean.log10().multiply(10);//Linear to dB conversion 165 | var C11_rc = C11_mean_db.expression('b(0)<-17?0:1'); // masking low dB returns (water) 166 | 167 | var out = H_rc.multiply(theta_c_rc).multiply(C11_rc); 168 | var out_rc = out.expression('b(0) ==7 ? 1 : b(0) == 14 ? 2 : b(0) == 21 ? 3: b(0) == 20 ? 6: b(0) == 24 ? 5: b(0) == 28 ? 4 : 0'); 169 | 170 | 171 | //Masked values 172 | m = (m.updateMask(vmask)).updateMask(C11_rc); 173 | H=(H.updateMask(vmask)).updateMask(C11_rc); 174 | theta_c=(theta_c.updateMask(vmask)).updateMask(C11_rc); 175 | DpRVIc=(DpRVIc.updateMask(vmask)).updateMask(C11_rc); 176 | out_rc=(out_rc.updateMask(vmask)).updateMask(C11_rc); 177 | ratio=(ratio.updateMask(vmask)).updateMask(C11_rc); 178 | 179 | var out_raster = H.addBands([theta_c.select('constant_mean'), 180 | m.select('constant_mean'), 181 | 182 | out_rc.select('constant').toDouble(), 183 | ratio.select('constant_mean'), 184 | C11_mean.select('constant_mean'), 185 | C22_mean.select('constant_mean'), 186 | DpRVIc.select('constant_mean'), 187 | image.select('angle')]); 188 | 189 | out_raster = out_raster.select( 190 | ['constant_mean', 'constant_mean_1','constant_mean_1_1','constant','constant_mean_2','constant_mean_3','constant_mean_4','constant_mean_5','angle'], // old names 191 | ['Hc', 'Theta_c','mc','class','ratio','VV','VH','DpRVIc','inc'] 192 | ); 193 | return out_raster.set('system:time_start', image.get('system:time_start')); 194 | 195 | }); 196 | 197 | 198 | // output visualization 199 | 200 | var jet_cmap = [' #000080 ', ' #0000bd ', ' #0000fa ', ' #0022ff ', ' #0057ff ', ' #008dff ', 201 | ' #00c3ff ', ' #0ff8e8 ', ' #3affbc ', ' #66ff91 ', ' #91ff66 ', ' #bcff3a ', ' #e8ff0f ', ' #ffd500 ', 202 | ' #ffa400 ', ' #ff7200 ', ' #ff4000 ', ' #fa0e00 ', ' #bd0000 ', ' #800000 ',] 203 | 204 | Map.centerObject(extent,15); 205 | Map.addLayer(ee.Image(m.select('Hc').first()),{min:0,max:1,palette:jet_cmap},'Hc'); 206 | Map.addLayer(ee.Image(m.select('mc').first()),{min:0,max:1,palette:jet_cmap},'mc'); 207 | Map.addLayer(ee.Image(m.select('Theta_c').first()),{min:0,max:45,palette:jet_cmap},'Theta_c'); 208 | Map.addLayer(ee.Image(m.select('DpRVIc').first()),{min:0,max:1,palette:jet_cmap},'DpRVIc'); 209 | 210 | /*---------------------------------------------------------------------------------------------- 211 | 212 | 4) Exporting the data in ratser format and csv 213 | 214 | ----------------------------------------------------------------------------------------------*/ 215 | 216 | 217 | var bandcol = ee.List(['Hc','Theta_c','mc','DpRVIc','class','ratio','VV','VH','inc']); 218 | var bandsize = bandcol.size().getInfo(); 219 | for (var i = 0; i < bandsize; i++) { 220 | var band = ee.String(bandcol.get(i)); 221 | var sample_pts = sample_pts.map(function(feature) { 222 | return ee.Feature(feature.geometry(), {'id': feature.id()}) 223 | }); 224 | 225 | var triplets = m.map(function(image) { 226 | return image.select(band).reduceRegions({ 227 | collection: sample_pts, 228 | reducer: ee.Reducer.first().setOutputs([band]), 229 | scale: 30, 230 | }).map(function(feature) { 231 | var dpgrd = ee.List([feature.get(band), -9999]) 232 | .reduce(ee.Reducer.firstNonNull()) 233 | return feature.set({band : dpgrd, 'imageID': image.id()}) 234 | }) 235 | }).flatten(); 236 | 237 | var format = function(table, rowId, colId) { 238 | var rows = table.distinct(rowId); 239 | var joined = ee.Join.saveAll('matches').apply({ 240 | primary: rows, 241 | secondary: table, 242 | condition: ee.Filter.equals({ 243 | leftField: rowId, 244 | rightField: rowId 245 | }) 246 | }); 247 | 248 | return joined.map(function(row) { 249 | var values = ee.List(row.get('matches')) 250 | .map(function(feature) { 251 | feature = ee.Feature(feature); 252 | return [feature.get(colId), feature.get(band)]; 253 | }); 254 | return row.select([rowId]).set(ee.Dictionary(values.flatten())); 255 | }); 256 | }; 257 | 258 | var sentinelResults = format(triplets, 'id', 'imageID'); 259 | 260 | var merge = function(table, rowId) { 261 | return table.map(function(feature) { 262 | var id = feature.get(rowId) 263 | var allKeys = feature.toDictionary().keys().remove(rowId) 264 | var substrKeys = ee.List(allKeys.map(function(val) { 265 | return ee.String(val).slice(0,8)} 266 | )) 267 | var uniqueKeys = substrKeys.distinct() 268 | var pairs = uniqueKeys.map(function(key) { 269 | var matches = feature.toDictionary().select(allKeys.filter(ee.Filter.stringContains('item', key))).values() 270 | var val = matches.reduce(ee.Reducer.max()) 271 | return [key, val] 272 | }) 273 | return feature.select([rowId]).set(ee.Dictionary(pairs.flatten())) 274 | }) 275 | } 276 | var sentinelMerged = merge(sentinelResults, 'id'); 277 | // print(ee.String(band)); 278 | var band = bandcol.get(i); 279 | Export.table.toDrive({ 280 | collection: sentinelResults, 281 | description: bandcol.get(i)+'_time_series', 282 | folder: 'dpgrd_out', 283 | fileNamePrefix:band+'_time_series', 284 | fileFormat: 'CSV' 285 | }); 286 | } 287 | 288 | 289 | var ExportCol = function(col, folder, scale, type, 290 | nimg, maxPixels, region) { 291 | type = type || "float"; 292 | nimg = nimg || 500; 293 | scale = scale || 30; 294 | maxPixels = maxPixels || 1e12; 295 | 296 | var colList = col.toList(nimg); 297 | var n = colList.size().getInfo(); 298 | 299 | for (var i = 0; i < n; i++) { 300 | var img = ee.Image(colList.get(i)); 301 | var id = img.id().getInfo(); 302 | region = region || img.geometry().bounds().getInfo()["coordinates"]; 303 | 304 | var imgtype = {"float":img.toFloat(), 305 | "byte":img.toByte(), 306 | "int":img.toInt(), 307 | "double":img.toDouble() 308 | } 309 | 310 | Export.image.toDrive({ 311 | image:imgtype[type], 312 | description: id, 313 | folder: folder, 314 | fileNamePrefix: id, 315 | region: region, 316 | scale: scale, 317 | maxPixels: maxPixels}) 318 | } 319 | } 320 | 321 | //Uncomment the below line to export all the avaialble scenes and corresponding descriptors in the Geotiff format. 322 | 323 | // ExportCol(m, 'dpgrd_out', 30,"double",100,1e12,extent) 324 | 325 | 326 | /*---------------------------------------------------------------------------------------------- 327 | 328 | END of the script 329 | 330 | ----------------------------------------------------------------------------------------------*/ 331 | -------------------------------------------------------------------------------- /GEE_App_Demo.mp4: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Narayana-Rao/dual_pol_descriptors/52ffd8e6aa2cea8837e4501e136dacca93aadafc/GEE_App_Demo.mp4 -------------------------------------------------------------------------------- /Jupyter Notebooks/data/ALOS_HH_2020_10km.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Narayana-Rao/dual_pol_descriptors/52ffd8e6aa2cea8837e4501e136dacca93aadafc/Jupyter Notebooks/data/ALOS_HH_2020_10km.tif -------------------------------------------------------------------------------- /Jupyter Notebooks/data/ALOS_HV_2020_10km.tif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Narayana-Rao/dual_pol_descriptors/52ffd8e6aa2cea8837e4501e136dacca93aadafc/Jupyter Notebooks/data/ALOS_HV_2020_10km.tif -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. 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But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Dual-polarimetric descriptors 2 | 3 | [![DOI](https://zenodo.org/badge/376702145.svg)](https://zenodo.org/badge/latestdoi/376702145) 4 | [![License: GPL 3.0](https://img.shields.io/badge/License-GPL_3.0-green.svg)](https://opensource.org/licenses/gpl-license) 5 | [![Open Source Love svg1](https://badges.frapsoft.com/os/v1/open-source.svg?v=103)](https://github.com/ellerbrock/open-source-badges/) 6 | [![GitHub release](https://img.shields.io/github/release/Narayana-Rao/dual_pol_descriptors.svg)](https://github.com/Narayana-Rao/dual_pol_descriptors/releases) 7 | [![GitHub commits](https://img.shields.io/github/commits-since/Narayana-Rao/dual_pol_descriptors/V0.1.svg)](https://GitHub.com/Narayana-Rao/dual_pol_descriptors/commit/) 8 |

9 | 10 |

11 | 12 | 13 | Google Earth Engine code: https://code.earthengine.google.com/630b7f024ac5cbae8af79b6d6c8b622f 14 | 15 | Google Earth Engine App URL: https://bnarayanarao.users.earthengine.app/view/dpgrd 16 | 17 | References: 18 | - **Narayanarao Bhogapurapu**, Subhadip Dey, Avik Bhattacharya, Dipankar Mandal, Juan Lopez-Sanchez, Heather McNairn, Carlos Lopez-Martinez and Y. S. Rao 2021 “Dual-polarimetric descriptors from Sentinel-1 GRD SAR data for crop growth assessment”. ISPRS Journal of Photogrammetry and Remote Sensing. 20-35, 178. doi: [10.1016/j.isprsjprs.2021.05.013](https://doi.org/10.1016/j.isprsjprs.2021.05.013) 19 | 20 | - **Narayanarao Bhogapurapu**, Subhadip Dey, Dipankar Mandal, Avik Bhattacharya, L. Karthikeyan, Heather McNairn and Y. S. Rao 2022 “Soil Moisture Retrieval Over Croplands Using dual-pol L-band GRD SAR Data”. Remote Sensing of Environment. Volume 271, 2022, Pages 112900, ISSN 0034-4257 doi: [10.1016/j.rse.2022.112900](https://doi.org/10.1016/j.rse.2022.112900) 21 | --------------------------------------------------------------------------------