feat(LST_download): 添加MODIS LST数据支持并调整可视化范围.

GEE_Scripts/LST_download.js

@@ -1,12 +1,12 @@
 /**
- * Landsat 系列地表温度 (LST) 数据下载 —— 以年平均温度处理下载为例
+ * 地表温度 (LST) 数据下载 —— 以年平均温度处理下载为例
  *
  * @author CVEO Team
  * @date 2026-01-15
  *
- * 1. 加载 Landsat-8, Landsat-9 SR 数据
+ * 1. 加载并合并 Landsat-8, 9 30m LST 数据
- * 2. 合并 Landsat-8, Landsat-9 LST 数据
+ * 2. 加载 MODIS MOD11A2 1km LST 数据
- * 3. 合成年度平均 Landsat LST 数据
+ * 3. 合成年度平均 LST 数据
  * 4. 导出 COG 云优化并填补缺失值的 GeoTIFF 影像 (大区域 GEE 自动分块下载)
  */
 
@@ -25,6 +25,7 @@ var start_date = start_year + "-01-01";
 var end_date = end_year + "-12-31";
 var cloud_threshold = 90; // 最大云量阈值
 var LansatBands = ["ST_B10"];
+var MODISBands = ["LST_Day_1km"];
 var commonBands = ["LST"];
 
 var region_geo = region.geometry();
@@ -47,6 +48,16 @@ function applyScaleFactors(image) {
     .addBands(thermalBands, null, true);
 }
 
+/**
+ * Applies scaling factors for MODIS.
+ * @param {ee.Image} image MODIS LST image
+ * @returns {ee.Image} MODIS LST image with scaled bands
+ */
+function applyModisScaleFactors(image) {
+  return image.multiply(0.02)
+    .copyProperties(image, ["system:time_start", "system:index", "system:id"]);
+}
+
 /**
  * 开尔文转摄氏度
  * @param {ee.Image} image Landsat LST image
@@ -90,6 +101,14 @@ var L9dataset = ee.ImageCollection("LANDSAT/LC09/C02/T1_L2")
   .filter(ee.Filter.lt("CLOUD_COVER", cloud_threshold));
 print(start_date + " - " + end_date + " Landsat-9 SR dataset", L9dataset);
 
+// 加载 MODIS LST 数据
+var MODISLSTdataset = ee.ImageCollection('MODIS/061/MOD11A2')
+  .filter(common_filter)
+  .select(MODISBands, commonBands)
+  .map(applyModisScaleFactors)
+  .map(kelvinToCelsius);
+print(start_date + " - " + end_date + " MODIS LST dataset", MODISLSTdataset);
+
 // 合并 Landsat-8, Landsat-9 LST 数据
 var LSTdataset = L8dataset.merge(L9dataset)
   .map(applyScaleFactors)
@@ -123,15 +142,39 @@ if (start_year == end_year) {
 }
 print(year_str + " Annual Mean Landsat LST dataset", yearlyLST);
 
+// 合成年度平均 MODIS LST 数据
+var yearlyMODISLST = ee.ImageCollection.fromImages(
+  years.map(function (y) {
+    return ee.ImageCollection.fromImages(
+      months.map(function (m) {
+        return MODISLSTdataset
+          .filter(ee.Filter.calendarRange(y, y, "year"))
+          .filter(ee.Filter.calendarRange(m, m, "month"))
+          .mean()
+          .clip(bounds)
+          .set("month", m)
+          .set("year", y);
+      })
+    ).mean().set("year", y);
+  })
+);
+print(year_str + " Annual Mean MODIS LST dataset", yearlyMODISLST);
+
 var total_mean_LST = LSTdataset.select("LST").mean();
 print(year_str + " Total Year Mean Landsat LST Histogram", ui.Chart.image.histogram(total_mean_LST, region, 100, 258));
 
 var annual_mean_LST = yearlyLST.select("LST").mean();
 print(year_str + " Annual Mean Landsat LST Histogram", ui.Chart.image.histogram(annual_mean_LST, region, 100, 258));
 
+var total_mean_MODIS_LST = MODISLSTdataset.select("LST").mean();
+print(year_str + " Total Year Mean MODIS LST Histogram", ui.Chart.image.histogram(total_mean_MODIS_LST, region, 1000, 258));
+
+var annual_mean_MODIS_LST = yearlyMODISLST.select("LST").mean();
+print(year_str + " Annual Mean MODIS LST Histogram", ui.Chart.image.histogram(annual_mean_MODIS_LST, region, 1000, 258));
+
 var lst_vis = {
-  min: 2,
+  min: 5,
-  max: 40,
+  max: 35,
   palette: [
     '040274', '040281', '0502a3', '0502b8', '0502ce', '0502e6',
     '0602ff', '235cb1', '307ef3', '269db1', '30c8e2', '32d3ef',
@@ -149,6 +192,8 @@ var styling = {
 Map.centerObject(region, 10);
 Map.addLayer(total_mean_LST, lst_vis, year_str + " Landsat Total Year Mean LST");
 Map.addLayer(annual_mean_LST, lst_vis, year_str + " Landsat Annual Mean LST");
+Map.addLayer(total_mean_MODIS_LST, lst_vis, year_str + " MODIS Total Year Mean LST");
+Map.addLayer(annual_mean_MODIS_LST, lst_vis, year_str + " MODIS Annual Mean LST");
 Map.addLayer(region.style(styling), {}, region_name);
 
 // 导出合并后的影像
@@ -158,7 +203,7 @@ var processed_img = annual_mean_LST.toFloat().unmask(-9999.0);
 print("Start exporting " + year_str + "Yearly Mean LST image (" + crs + ")", processed_img);
 Export.image.toDrive({
   image: processed_img,
-  description: region_name_en + "_LST_" + year_str,
+  description: region_name_en + "_LST_" + year_str + "_30m",
   folder: "LST",
   region: region, // 添加后会自动裁剪
   scale: 30,
@@ -171,3 +216,21 @@ Export.image.toDrive({
     noData: -9999.0,
   },
 });
+
+// 导出 MODIS 合并后的影像
+var processed_modis_img = annual_mean_MODIS_LST.toFloat().unmask(-9999.0);
+print("Start exporting " + year_str + " MODIS Yearly Mean LST image (" + crs + ")", processed_modis_img);
+Export.image.toDrive({
+  image: processed_modis_img,
+  description: region_name_en + "_LST_" + year_str + "_1km",
+  folder: "LST",
+  region: region,
+  scale: 1000,
+  crs: crs,
+  maxPixels: 1e13,
+  fileFormat: "GeoTIFF",
+  formatOptions: {
+    cloudOptimized: true,
+    noData: -9999.0,
+  },
+});