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