Google Earth EngineGEE合成长时序的月NDVI和LST

今天来简单分享下如何在GEE合成长时序的月NDVI与LST，并进行分析

目标：

利用MODIS为数据源，在GEE计算某一地区对月NDVI与LST，并制作统计图

以武汉市为研究区

GEE实现代码：

首先确定研究区和使用的数据集

1.  
   var roi = ee.FeatureCollection("users/lilei655123/WUhan");
2.  
   Map.centerObject(roi,7)
3.  
   var styling = {color:"red",fillColor:"00000000"};
4.  
   Map.addLayer(roi.style(styling),{},"geometry")
5.  
   //添加MODIS植被指数16天全球250米
6.  
   var Coll_NDVI = ee.ImageCollection("MODIS/006/MOD13Q1")
7.  
   //MODIS/006/MOD13A1
8.  
   var Coll_LST = ee.ImageCollection("MODIS/006/MOD11A2")
9.  
   //MODIS/006/MOD11A1

确定起止时间和月份

1.  
   var startYear = 2010;
2.  
   var endYear = 2020;
3.  
   var startDate = ee.Date.fromYMD(startYear, 1, 1);
4.  
   var endDate = ee.Date.fromYMD(endYear, 12, 31);

合成NDVI和LST

1.  
   Coll_NDVI = Coll_NDVI.filterDate(startDate, endDate).select("NDVI");
2.  
   Coll_NDVI = ee.ImageCollection(ee.Algorithms.If(Coll_NDVI.size().eq(0),
3.  
   ee.ImageCollection(ee.Image(0).selfMask().rename('NDVI')),
4.  
   Coll_NDVI
5.  
   ));
6.  
   Coll_LST = Coll_LST.filterDate(startDate, endDate).select("LST_Day_1km");
7.  
   Coll_LST = ee.ImageCollection(ee.Algorithms.If(Coll_LST.size().eq(0),
8.  
   ee.ImageCollection(ee.Image(0).selfMask().rename('LST_Day_1km')),
9.  
   Coll_LST
10.  
    ));
11.  
    // MOD12Q1数据的NDVI比例为0.0001 [ 最小值=-2000，最大值=10000]
12.  
    var Coll_NDVI = Coll_NDVI.map(function(img) {
13.  
    return img
14.  
    .divide(10000).float().set("system:time_start", img.get("system:time_start")); // keep time info
15.  
    });
16.  
     
17.  
     
18.  
    var Coll_LST = Coll_LST.map(function(img) {
19.  
    return img
20.  
    .multiply(0.02).subtract(273.15).float().set("system:time_start", img.get("system:time_start")); // keep time info
21.  
    });
22.  
     
23.  
     
24.  
    print(Coll_NDVI);
25.  
    print(Coll_LST);

合成月NDVI

1.  
   var monthlyNDVI = ee.ImageCollection.fromImages(
2.  
   years.map(function (y) {
3.  
   return months.map(function(m) {
4.  
   var monthly = Coll_NDVI
5.  
   .filter(ee.Filter.calendarRange(y, y, "year"))
6.  
   .filter(ee.Filter.calendarRange(m, m, "month"))
7.  
   .mean();
8.  
   return monthly
9.  
   .set("year", y)
10.  
    .set("month", m)
11.  
    .set('date', ee.Date.fromYMD(y,m,1))
12.  
    .set("system:time_start", ee.Date.fromYMD(y, m, 1));}); })
13.  
    .flatten());
14.  
    print('monthlyNDVI',monthlyNDVI)
15.  
    //最大合成NDVI
16.  
    var MonthlyMAX = ee.ImageCollection.fromImages(months
17.  
    .map(function (m) {
18.  
    var maxNDVI = monthlyNDVI.filter(ee.Filter.eq("month", m))
19.  
    .reduce(ee.Reducer.percentile({percentiles: [90]}));
20.  
    return maxNDVI
21.  
    .set("month", m);})
22.  
      .flatten());
23.  
    //print (MonthlyMAX, 'MonthlyMAX');
24.  
    Map.addLayer (MonthlyMAX.first().clip(roi), {min:0, max:1, 'palette': ['red','yellow', 'green']}, 'MonthlyMAX');
25.  
     
26.  
     
27.  
    var MonthlyMIN = ee.ImageCollection.fromImages(months
28.  
    .map(function (m) {
29.  
    var minNDVI = monthlyNDVI.filter(ee.Filter.eq("month", m))
30.  
    .reduce(ee.Reducer.percentile({percentiles: [10]}));
31.  
    return minNDVI
32.  
    .set("month", m);})
33.  
      .flatten()); 
34.  
    //print (MonthlyMIN, 'MonthlyMIN');
35.  
    Map.addLayer (MonthlyMIN.first().clip(roi), {min:0, max:1, 'palette': ['red','yellow', 'green']}, 'MonthlyMIN');

同样的方法合成月LST

1.  
   var monthlyLST = ee.ImageCollection.fromImages(
2.  
   years.map(function (y) {
3.  
   return months.map(function(m) {
4.  
   var monthly = Coll_LST
5.  
   .filter(ee.Filter.calendarRange(y, y, "year"))
6.  
   .filter(ee.Filter.calendarRange(m, m, "month"))
7.  
   .mean();
8.  
   return monthly
9.  
   .set("year", y)
10.  
    .set("month", m)
11.  
    .set('date', ee.Date.fromYMD(y,m,1))
12.  
    .set("system:time_start", ee.Date.fromYMD(y, m, 1));}); })
13.  
    .flatten());
14.  
    print('monthlyLST',monthlyLST)
15.  
     
16.  
     
17.  
     
18.  
     
19.  
    //最大合成LST
20.  
    var Monthly_LST_MAX = ee.ImageCollection.fromImages(months
21.  
    .map(function (m) {
22.  
    var maxNDVI = monthlyLST.filter(ee.Filter.eq("month", m))
23.  
    .reduce(ee.Reducer.percentile({percentiles: [90]}));
24.  
    return maxNDVI
25.  
    .set("month", m);})
26.  
    .flatten());
27.  
     
28.  
    //print (Monthly_LST_MAX, 'Monthly_LST_MAX');
29.  
    Map.addLayer (Monthly_LST_MAX.first().clip(roi), {min:15, max:35, 'palette': ['red','yellow', 'green']}, 'Monthly_LST_MAX');
30.  
     
31.  
     
32.  
    var Monthly_LST_MIN = ee.ImageCollection.fromImages(months
33.  
    .map(function (m) {
34.  
    var minNDVI = monthlyNDVI.filter(ee.Filter.eq("month", m))
35.  
    .reduce(ee.Reducer.percentile({percentiles: [10]}));
36.  
    return minNDVI
37.  
    .set("month", m);})
38.  
    .flatten());
39.  
     
40.  
    //print (Monthly_LST_MIN, 'Monthly_LST_MIN');
41.  
    Map.addLayer (Monthly_LST_MIN.first().clip(roi), {min:0, max:1, 'palette': ['red','yellow', 'green']}, 'Monthly_LST_MIN');

创建统计图

1.  
   var ndviTimeSeries = ui.Chart.image.seriesByRegion(
2.  
   monthlyNDVI, roi, ee.Reducer.mean(), 'NDVI', 500, 'system:time_start', 'label')
3.  
   .setChartType('ScatterChart')
4.  
   .setOptions({trendlines: {0: {color: 'green'}},lineWidth: 1,pointSize: 3,
5.  
   title: 'MODIS NDVI Time Series',
6.  
   vAxis: {title: 'NDVI'},
7.  
   series: {
8.  
   0: {color: 'green'},
9.  
    
10.  
    }});
11.  
    print(ndviTimeSeries);
12.  
    print(ui.Chart.image.series(monthlyNDVI , roi , ee.Reducer.mean(), 500));

运行结果如下:

LST

NDVI的带状平均值

LST的带状平均值

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