Google Earth Engine（GEE）——Sentinel-1 和 2 数据的融合，水稻范围识别和水稻种植季节区分地图绘制—马来西亚为例

var geometry_roi = 
    /* color: #d63000 */
    /* shown: false */
    ee.Geometry.Polygon(
        [[[101.00841298710328, 3.5840956245106925],
          [101.15672841679078, 3.3538056172134443],
          [101.30779042850953, 3.4442833673276585],
          [101.11003652225953, 3.655364290850406],
          [101.15672841679078, 3.710182494184478],
          [101.02763906132203, 3.8061061368075664],
          [100.87932363163453, 3.6992191239822603]]]);

// Define ROI
var roi=geometry_roi;
Map.centerObject(roi,10);

//Date
var startDate = ee.Date('2019-01-01');
var endDate =  ee.Date('2020-12-31');

//S2影像的设定
// 为2019-2020年期间创建S2图像集。
var S2 = ee.ImageCollection('COPERNICUS/S2')
         //filter start and end date
         .filter(ee.Filter.date(startDate, endDate))
         .filterMetadata('CLOUDY_PIXEL_PERCENTAGE', 'less_than',100)
         //filter according to drawn boundary
         .filterBounds(roi)


print(S2.limit(10))
print(S2.aggregate_array('SPACECRAFT_NAME'))
// Function to calculate and add an NDVI band
var addNDVI = function(image) {
 return image.addBands(image.normalizedDifference(['B8', 'B4'] )); //'B8', 'B4'
};  
  
// Add NDVI band to image collection
var S2 = S2.map(addNDVI).select(['nd']);
print('S2',S2.limit(10)) ;
var NDVI=S2.select('nd');

// For month
var month = 1;

// 计算区间数，这里用了一个很简单的方法进行了时间的获取
var months = endDate.difference(startDate,'month').divide(month).toInt();
//建立时间序列
var sequence = ee.List.sequence(0, months); 
print(sequence)

var sequence_s1 = sequence.map(function(num){
//数字化其变量
    num = ee.Number(num);
//中间间隔的时间设定，开始时间和就是默认的时间，终止时间往后加一个月
    var Start_interval = startDate.advance(num.multiply(month), 'month');
  
    var End_interval = startDate.advance(num.add(1).multiply(month), 'month');
//按照上面的时间进行影像筛选
    var subset = NDVI.filterDate(Start_interval,End_interval);
//获取子集的最大值，然后设定时间属性，因为在计算了max之后时间贤惠能干会丢失
    return subset.max().set('system:time_start',Start_interval);
});

print('sequence_s1',sequence_s1)
//将上面的影像子集放入到影像集合中
var byMonthYear = ee.ImageCollection.fromImages(sequence_s1);

print('byMonthYear',byMonthYear)
//这里有一个非常实用的函数将一个影像集合转化为一个单波段的影像
var multibandNDVI = byMonthYear.toBands().clip(roi);
print('multiband',multibandNDVI);


//定义时间波段，将上面的子集命名的波段改名
var bandsName=['2019-01','2019-02','2019-03','2019-04','2019-05','2019-06',
               '2019-07','2019-08','2019-09','2019-10','2019-11','2019-12',
               '2020-01','2020-02','2020-03','2020-04','2020-05','2020-06',
               '2020-07','2020-08','2020-09','2020-10','2020-11','2020-12']

var multiband1_ndvi = multibandNDVI.rename(bandsName).clip(roi);//(monList)//


//s1影像的设定，基本步骤同上
var sentinel1_vh = ee.ImageCollection('COPERNICUS/S1_GRD')
  .filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
  .select('VH')
  .filter(ee.Filter.eq('instrumentMode', 'IW'))
  .filter(ee.Filter.eq('resolution_meters', 10))
  .filter(ee.Filter.date(startDate, endDate))
  .filter(ee.Filter.bounds(roi))

print('s1',sentinel1_vh);

// For month
var month = 1;

// Calculating number of intervals
var months = endDate.difference(startDate,'month').divide(month).toInt();
// Generating a sequence 
var sequence = ee.List.sequence(0, months); 
print(sequence)

var sequence_s1 = sequence.map(function(num){
    num = ee.Number(num);
    var Start_interval = startDate.advance(num.multiply(month), 'month');
  
    var End_interval = startDate.advance(num.add(1).multiply(month), 'month');
    var subset = sentinel1_vh.filterDate(Start_interval,End_interval);
    return subset.median().set('system:time_start',Start_interval);
});

print('sequence_s1',sequence_s1)
var byMonthYearS1 = ee.ImageCollection.fromImages(sequence_s1);
var multibands1 = byMonthYearS1.toBands().clip(roi);



var multibands1 = multibands1.rename(bandsName).clip(roi);//.rename(monLists1).clip(roi);//


//将S1和S2合并
var combinedband=multiband1_ndvi.addBands(multibands1);
print('combinedband',combinedband);



//将影像进行采样

var training = combinedband.sample({
  region: roi,
  scale: 10,
  numPixels: 3000,
  tileScale:8,
 // geometries:true
  
});

//Map.addLayer(training,{},'points')

//这里用Kmeans分类器进行训练
var clusterer = ee.Clusterer.wekaKMeans(20).train({
  features:training
  
});

//使用经过训练的聚类器对输入进行聚类。
var result_cluster =combinedband.cluster(clusterer).byte();//combands
// print('result_s2',result_s2)

var clusters = [0, 1, 2, 3, 4,5,6,7,8,9,
10,11,12,13,14,15,16,17,18,19,
20,21,22,23,24,25,26,27,28,29];


var values0 =   [1, 2, 3, 4,5,
                 6,7,8,9,10,
                 11,12,13,14,15,
                 16,17,18,19,20,
                 21,22,23,24,25,26,27,28,29,30];
                 
var values1 =   [0, 0, 0, 1,0,
                1,0,1,0,0,
                0,0,1,0,0,
                1,1,0,0,0,
                0,0,0,0,0,
                0,0,0,0,0];
/*                
var values1 =   [0, 0, 0, 0,1,
                0,1,0,1,0,
                0,0,0,1,0,
                0,1,1,0,0,
                0,0,0,0,0,
                0,0,0,0,0];                
 */               
var values2 =    [0, 0, 0, 2,0,
                3,0,4,0,0,
                0,0,1,0,0,
                2,3,0,0,0,
                0,0,0,4,0,
                0,0,0,0,0];
/*                
var values2 =    [0, 0, 0, 0, 2,0,
                3,0,4,0,0,
                0,0,1,0,0,
                2,3,0,0,0,
                0,0,0,4,0,
                0,0,0,0];
 */               
                

//这里根据定义的value值进行重新赋值映射，
//var result_cluster = result_cluster.remap(clusters,values0);
var remapped_cluster = result_cluster.rename('remapped');// .remap(clusters, values0).byte().clip(roi);//.updateMask(slope.lt(slope_th))
var remapped_cluster1=remapped_cluster.remap(values0,values1);
var remapped_cluster1=remapped_cluster1.updateMask(remapped_cluster1);
var remapped_cluster2=remapped_cluster.remap(values0,values2).updateMask(remapped_cluster1);
//




///
var comb_ndvi_cluster=multiband1_ndvi.addBands(remapped_cluster);
print("comb_ndvi_cluster",comb_ndvi_cluster);
// 定义图表的定制选项。
var options = {
  lineWidth: 1,
  pointSize: 2,
  hAxis: {title: 'Year-Month'},
  vAxis: {title: 'NDVI'},
  title: 'Sentinel-2 NDVI spectra in classified regions'
};

// 制作图表，设置选项。
var chart_class_ndvi = ui.Chart.image.byClass(
    comb_ndvi_cluster, 'remapped', roi, ee.Reducer.median(), 500)//, classNames, wavelengths)
    .setOptions(options)
    .setChartType('ScatterChart');

// Print the chart.
//print(chart_class_ndvi);

//
var comb_vh_cluster=multibands1.addBands(remapped_cluster);
print("comb_vh_cluster",comb_vh_cluster);
// Define chart customization options.
var options = {
  lineWidth: 1,
  pointSize: 2,
  hAxis: {title: 'Year-Month'},
  vAxis: {title: 'VH'},
  title: 'Senetinel-1 VH spectra in classified regions'
};

// Make the chart, set the options.
var chart_class_vh = ui.Chart.image.byClass(
    comb_vh_cluster, 'remapped', roi, ee.Reducer.median(), 500)//, classNames, wavelengths)
    .setOptions(options)
    .setChartType('ScatterChart');

// Print the chart.
//print(chart_class_vh);




Map.addLayer(multiband1_ndvi ,  {min: 0.2, max: 0.8}, 'NDVI',0);
Map.addLayer(multibands1 ,  {min: -25, max: -10}, 'VH',0);

Map.addLayer(remapped_cluster.randomVisualizer(), {}, 're_groups_s2',0);
Map.addLayer(remapped_cluster1, {min:1,max:1,palette:['white','red']}, 'binary_rice',1);
Map.addLayer(remapped_cluster2.randomVisualizer(), {}, 'pattern_rice',1);  
 
 
 
 ///

// 在地图旁边创建一个面板
var panel = ui.Panel({style:{width: '550px'}});
ui.root.add(panel);

 // value: 'SENTINEL-2 NDVI & SENTINEL-1 VH Explorer',
 var label1 = ui.Label({
  value: 'SENTINEL-2 NDVI & SENTINEL-1 VH Explorer: Paddy Clustering',
   style: {
    //fontSize: '20px',
    color: 'white',
    backgroundColor :'blue',
   // border: '1px solid black',
   // fontWeight: 'bold',
    padding: '5px',
   margin: '12px 0px 0px 8px', fontWeight: 'bold', fontSize: '14px', border: '1px solid black', 
   padding: '5px 5px 5px 5px',
  }
//  style: {
 //   fontSize: '16px',
 //   color: 'red',
   // fontWeight: 'bold',
  //  padding: '5px',
 // }
});


 var label2 = ui.Label({
  value: 'Developed by Rudiyanto',
  style: {
    fontSize: '16px',
    color: 'red',
   // fontWeight: 'bold',
    padding: '5px',
  }
});


var affiliationLabel= ui.Label('Instructions',{fontWeight: 'bold',padding: '3px',});
var affiliation= ui.Label(
  "Program of Crop Science\n"+
  "Faculty of Fisheries and Food Science\n"+
  "Universiti Malaysia Terengganu\n"+
  "Email: rudiyanto@umt.edu.my\n"
  , {whiteSpace:'pre',padding: '5px',fontSize: '16px',
    color: 'red',
   // fontWeight: 'bold',
    }
  
);


var affiliationPanel = ui.Panel([affiliationLabel,affiliation]);
var affiliationPanel = ui.Panel([affiliation]);


 
 panel.add(label1);
 panel.add(label2);
 panel.add(affiliationPanel);
 panel.add(chart_class_ndvi);
 panel.add(chart_class_vh);