GEE长时序月均值MNDWI计算与导出

背景：有小伙伴需要计算近20年间每月的MNDWI合成图，大津算法OTSU提取水体，并自动导出水体二值图。

该程序实现了，计算每个月的合成图像组合成imageCollection，再计算该集合的MNDWI，大津算法OTSU提取水体，并自动导出水体二值图。 

话不多说，直接上代码。

// author by lvbta;
// email: [email protected];

var geometry = 
    /* color: #d63000 */
    /* shown: false */
    /* displayProperties: [
      {
        "type": "rectangle"
      }
    ] */
    ee.Geometry.Polygon(
        [[[112.98527995989568, 30.082709101129765],
          [112.98527995989568, 29.59669322410755],
          [113.63896648333318, 29.59669322410755],
          [113.63896648333318, 30.082709101129765]]], null, false);


// landsat8   2013-04-11-------2020-12-31
var landsat8 = ee.ImageCollection("LANDSAT/LC08/C02/T1_L2").filterBounds(geometry);

// computer mndwi for landsat8
function computermNdwi(image)
{
  var mndwi = image.normalizedDifference(['SR_B3','SR_B5']).rename('mndwi');
  return mndwi.updateMask(mndwi.gt(-1).and(mndwi.lt(1)));
}

//otsu算法
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 indices = ee.List.sequence(1, size);
  var bss = indices.map(function(i) {
    var aCounts = counts.slice(0, 0, i);
    var aCount = aCounts.reduce(ee.Reducer.sum(), [0]).get([0]);
    var aMeans = means.slice(0, 0, i);
    var aMean = aMeans.multiply(aCounts)
        .reduce(ee.Reducer.sum(), [0]).get([0])
        .divide(aCount);
    var bCount = total.subtract(aCount);
    var bMean = sum.subtract(aCount.multiply(aMean)).divide(bCount);
    return aCount.multiply(aMean.subtract(mean).pow(2)).add(
           bCount.multiply(bMean.subtract(mean).pow(2)));
  });
  return means.sort(bss).get([-1]);
}

// landsat8  2014-2020
function seclectMonthly(imageCol,startYear){
  for(var i=1;i<=7;i++){
    for(var j=1;j<=12;j++){
      var NN = startYear+i-1
      var imN = ee.String(NN.toString()).cat('-').cat(ee.String(j.toString()))
      var yearName = ee.String(NN.toString()).cat('year').cat(ee.String(j.toString()).cat('month'))
      yearName = ee.Image(computermNdwi(imageCol.filterDate(ee.String(NN.toString()).cat('-01-01'),ee.String(NN.toString()).cat('-12-31')).filter(ee.Filter.calendarRange(j,j,'month')).mean().clip(geometry)));
     //print(ee.Image(yearName).select('mndwi'))
     
      var histogram = yearName.reduceRegion({
        reducer: ee.Reducer.histogram(), 
        geometry: geometry, 
        scale: 30,
        maxPixels: 1e13,
        tileScale: 8
      });
      var threshold = otsu(histogram.get("mndwi"));
      var mask = yearName.gte(threshold);
      var water = mask.rename("water").setMulti({'time':imN});//.updateMask(mask) 
      new_imageCol = new_imageCol.add(water)
    }
  }
  return ee.ImageCollection(new_imageCol)
}
var new_imageCol = ee.List([]);
var new_imag = seclectMonthly(landsat8,2014,new_imageCol);
print(new_imag)
Map.addLayer(new_imag.first(),{min:0,max:1,palette:['black','blue']},'water_landsat8_otsu_based')

//export water
function exportImageCollection(imgCol) { 
  var indexList = imgCol.reduceColumns(ee.Reducer.toList(), ["system:index"]) 
                        .get("list"); 
  var namE = ee.List(imgCol.reduceColumns(ee.Reducer.toList(), ["time"]) 
                        .get("list")); 
  namE.evaluate(function(indexs) { 
    for (var i=0; i