Sentinel-1 Rapid Flood Mapping (Otsu + JRC permanent-water mask)

// ============================================================
// Sentinel-1 Rapid Flood Mapping (Otsu + JRC permanent water)
// Output: flood-only mask + exposed-population summary
// ============================================================
var eventDate = ee.Date('2024-08-25');                     // adjust
var preStart  = eventDate.advance(-30, 'day');
var postEnd   = eventDate.advance(7, 'day');

function refinedLee(img) {
  var bandNames = img.bandNames();
  img = ee.Image(10).pow(img.divide(10.0));
  var k = ee.Kernel.square({radius: 3, units: 'pixels'});
  var mean = img.reduceNeighborhood(ee.Reducer.mean(), k);
  var v    = img.reduceNeighborhood(ee.Reducer.variance(), k);
  var b = v.divide(mean.multiply(mean));
  var w = b.divide(b.add(1));
  var f = mean.add(img.subtract(mean).multiply(w));
  return ee.Image(10).multiply(f.log10()).rename(bandNames);
}

var s1 = ee.ImageCollection('COPERNICUS/S1_GRD')
  .filterBounds(aoi)
  .filter(ee.Filter.eq('instrumentMode', 'IW'))
  .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VV'))
  .select('VV');

var pre  = s1.filterDate(preStart, eventDate).map(refinedLee).median().rename('pre');
var post = s1.filterDate(eventDate, postEnd).map(refinedLee).median().rename('post');
var diff = post.subtract(pre).rename('diff');

function otsu(histogram) {
  var counts = ee.Array(ee.Dictionary(histogram).get('histogram'));
  var means  = ee.Array(ee.Dictionary(histogram).get('bucketMeans'));
  var size = means.length().get([0]);
  var total = counts.reduce(ee.Reducer.sum(), [0]).get([0]);
  var sum   = means.multiply(counts).reduce(ee.Reducer.sum(), [0]).get([0]);
  var mean  = sum.divide(total);
  var idx = ee.List.sequence(1, size);
  var bss = idx.map(function(i){
    var aC = counts.slice(0,0,i); var aN = aC.reduce(ee.Reducer.sum(),[0]).get([0]);
    var aM = means.slice(0,0,i).multiply(aC).reduce(ee.Reducer.sum(),[0]).get([0]).divide(aN);
    var bN = total.subtract(aN);
    var bM = sum.subtract(aN.multiply(aM)).divide(bN);
    return aN.multiply(aM.subtract(mean).pow(2))
      .add(bN.multiply(bM.subtract(mean).pow(2)));
  });
  return means.sort(bss).get([-1]);
}
var hist = diff.reduceRegion({reducer: ee.Reducer.histogram(255, 0.2),
  geometry: aoi, scale: 30, bestEffort: true, tileScale: 4});
var t = ee.Number(otsu(hist.get('diff')));
print('Otsu threshold (dB drop):', t);

// flood = strong decrease in VV (water = low backscatter)
var floodRaw = diff.lt(t);

// remove JRC permanent water (occurrence > 50%)
var perm = ee.Image('JRC/GSW1_4/GlobalSurfaceWater').select('occurrence').gt(50).unmask(0);
var flood = floodRaw.updateMask(floodRaw).updateMask(perm.not()).rename('flood');

// exposed population
var pop = ee.ImageCollection('WorldPop/GP/100m/pop').filter(ee.Filter.eq('year', 2020))
  .filterBounds(aoi).mosaic().rename('pop');
var exposed = pop.updateMask(flood).reduceRegion({
  reducer: ee.Reducer.sum(), geometry: aoi, scale: 100, bestEffort: true, tileScale: 4, maxPixels: 1e13
});
print('Exposed population:', exposed);

Map.centerObject(aoi, 9);
Map.addLayer(perm, {palette:['#0c4a6e']}, 'Permanent water', false);
Map.addLayer(flood, {palette:['#22d3ee']}, 'Flood mask');

Export.image.toDrive({image: flood.clip(aoi), description: 'flood_mask',
  folder: 'gee_exports', region: aoi, scale: 30, maxPixels: 1e13,
  fileFormat: 'GeoTIFF', formatOptions: {cloudOptimized: true}});

# Python (geemap) — see JS version for the full algorithm.
import ee
ee.Initialize()
# Reproduce by porting the Otsu helper and Refined-Lee functions.