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利用GEE批量下载基于L8影像SR数据集的某一月份的时序RSEI数据_gee seninel2 时间序列下载

作者：小蓝xlanll | 2024-04-20 03:57:56

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gee seninel2 时间序列下载

SR数据集属于Landsat8影像的L2级数据，优点是经过了大气处理。

本文基于L8影像的SR数据集，计算了北京市2013年到2022年8月份的RSEI数据。

利用GEE批量下载某一月份时序RSEI数据的具体流程如下：

目录

1. 定义研究区和去云函数

2. 定义SR月份数据和去云处理

3. 计算各个指数（NDVI、WET、NDBSI、LST）并去水体

4. 将各个指数分别归一化

5. 定义PCA函数并计算归一化后的RSEI

6. 批量导出RSEI影像

7. 批量RUN

8. RSEI运行结果

9. 完整代码

1. 定义研究区和去云函数

  1.  
  2. var roi = table;
  3. Map.addLayer(roi, {}, 'yanjiuqu');
  4.  
  5. // define cloudMasking function
  6. function maskL8sr(image) {
  7.   var qaMask = image.select('QA_PIXEL').bitwiseAnd(parseInt('11111', 2)).eq(0);
  8.   var saturationMask = image.select('QA_RADSAT').eq(0);
  9.  
  10.   // Apply the scaling factors to the appropriate bands.
  11.   var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2);
  12.   var thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0);
  13.  
  14.   // Replace the original bands with the scaled ones and apply the masks.
  15.   return image.addBands(opticalBands, null, true)
  16.               .addBands(thermalBands, null, true)
  17.               .updateMask(qaMask)
  18.               .updateMask(saturationMask);
  19. }

2. 定义SR月份数据和去云处理

  1. // define SR dateset
  2. var Imagecol = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
  3.                  .filterBounds(roi)
  4.                  .filterDate('2013-04-08', '2022-12-31')
  5.                  .filterMetadata('CLOUD_COVER', 'less_than', 50)
  6.                  .sort('CLOUD_COVER', true)
  7.                  .map(function(image){
  8.                    return image.set('year', ee.Image(image).date().get('year')).set('month', ee.Image(image).date().get('month'));
  9.                  })
  10.                  .map(maskL8sr);
  11.  
  12. print('CloudRemoved_Images', Imagecol);
  13.  
  14. // to prepare mosaiced and clipped dataset
  15. var imgList = ee.List([]);
  16. for (var year = 2013; year < 2023; year++) {
  17.   for (var month = 8; month < 9; month++) {
  18.     var img = Imagecol.filterMetadata('year', 'equals', year)
  19.                       .filterMetadata('month', 'equals', month)
  20.                       .mosaic()
  21.                       .clip(roi);
  22.     var yearmonth = ee.String(ee.Number(year)).cat('-').cat(ee.String(ee.Number(month)));
  23.     var image = img.set('yearmonth', yearmonth);
  24.     var imgList = imgList.add(image);
  25.   }
  26. }
  27.  
  28. print('Filtered_Images', imgList);
  29.  
  30. // to filter valid images
  31. var Valid_Imgs = ee.ImageCollection(imgList)
  32.                    .map(function(image) {
  33.                       var bands = image.bandNames();
  34.                       var hasBands = bands.size().gt(0);
  35.                       return ee.Algorithms.If(hasBands, image, null);
  36.                     },true);
  37.  
  38. print('Valid_Images', Valid_Imgs);

3. 计算各个指数（NDVI、WET、NDBSI、LST）并去水体

  1. // data process
  2. // to define watermask function
  3. function waterMask(image) {
  4.   var ndwi = image.normalizedDifference(['SR_B3', 'SR_B5']);
  5.   var mask = ndwi.lt(0.1);
  6.   return image.updateMask(mask);
  7. }
  8.  
  9. // to define ndvi function
  10. function getNDVI(image) {
  11.   var ndvi = image.normalizedDifference(['SR_B5', 'SR_B4']);
  12.   return image.addBands(ndvi.rename('NDVI'));
  13. }
  14.  
  15. // to define wet function
  16. function getWET(image) {
  17.   var WET = image.expression('B*(0.1509) + G*(0.1973) + R*(0.3279) + NIR*(0.3406) + SWIR1*(-0.7112) + SWIR2*(-0.4572)', {
  18.     'B': image.select('SR_B2'),
  19.     'G': image.select('SR_B3'),
  20.     'R': image.select('SR_B4'),
  21.     'NIR': image.select('SR_B5'),
  22.     'SWIR1': image.select('SR_B6'),
  23.     'SWIR2': image.select('SR_B7')
  24.   });
  25.   return image.addBands(WET.rename('WET'));
  26. }
  27.  
  28. // to define ndbsi function
  29. function getNDBSI(image) {
  30.   // NDBSI = ( IBI + SI ) / 2
  31.   var IBI = image.expression('(2 * SWIR1 / (SWIR1 + NIR) - (NIR / (NIR + RED) + GREEN / (GREEN + SWIR1))) / (2 * SWIR1 / (SWIR1 + NIR) + (NIR / (NIR + RED) + GREEN / (GREEN + SWIR1)))', {
  32.     'SWIR1': image.select('SR_B6'),
  33.     'NIR': image.select('SR_B5'),
  34.     'RED': image.select('SR_B4'),
  35.     'GREEN': image.select('SR_B3')
  36.   });
  37.   var SI = image.expression('((SWIR1 + RED) - (NIR + BLUE)) / ((SWIR1 + RED) + (NIR + BLUE))', {
  38.     'SWIR1': image.select('SR_B6'),
  39.     'NIR': image.select('SR_B5'),
  40.     'RED': image.select('SR_B4'),
  41.     'BLUE': image.select('SR_B2')
  42.   });
  43.   var NDBSI = (IBI.add(SI)).divide(2);
  44.   return image.addBands(NDBSI.rename('NDBSI'));
  45. }
  46.  
  47. // to dedfine lst function
  48. function getLST(image) {
  49.   var LST = image.expression('(LST - 273.15)', {
  50.     LST: image.select('ST_B10')
  51.   });
  52.   return image.addBands(LST.rename('LST'));
  53. }
  54.  
  55. // to get NDVI, WET, NDBSI, and LST
  56. var Indexed_Imgs = Valid_Imgs.map(waterMask)
  57.                              .map(getNDVI)
  58.                              .map(getWET)
  59.                              .map(getNDBSI)
  60.                              .map(getLST);

4. 将各个指数分别归一化

  1. var bandNames = ['NDVI', 'WET', 'NDBSI', 'LST'];
  2. var Prepared_Imgs = Indexed_Imgs.select(bandNames);
  3.  
  4. // to define norm function
  5. function getNorm(image) {
  6.   var minMax = image.reduceRegion({
  7.     reducer: ee.Reducer.minMax(),
  8.     geometry: roi,
  9.     scale: 30,
  10.     crs: 'EPSG:4326',
  11.     bestEffort: true,
  12.     maxPixels: 1e13
  13.   });
  14.   var yearmonth = image.get('yearmonth');
  15.   var normalize  = ee.ImageCollection.fromImages(
  16.       image.bandNames().map(function(name){
  17.             name = ee.String(name);
  18.             var band = image.select(name);
  19.             return band.unitScale(ee.Number(minMax.get(name.cat('_min'))), ee.Number(minMax.get(name.cat('_max'))));
  20.         })
  21.     ).toBands().set('yearmonth', yearmonth);
  22.     return normalize;
  23. }
  24.  
  25. var Normed_Imgs = Prepared_Imgs.map(getNorm);
  26.  
  27. print('Normed_Images', Normed_Imgs);

5. 定义PCA函数并计算归一化后的RSEI

  1. // to define PCA function
  2. var getPCA = function(image){
  3.   
  4.   var bandNames = image.bandNames();
  5.   var region = roi;
  6.   var yearmonth = image.get('yearmonth');
  7.   // Mean center the data to enable a faster covariance reducer
  8.   // and an SD stretch of the principal components.
  9.   var meanDict = image.reduceRegion({
  10.         reducer:  ee.Reducer.mean(),
  11.         geometry: region,
  12.         scale: 30,
  13.         maxPixels: 1e13
  14.   });
  15.   var means = ee.Image.constant(meanDict.values(bandNames));
  16.   var centered = image.subtract(means).set('yearmonth', yearmonth);
  17.   
  18.   
  19.   // This helper function returns a list of new band names.
  20.   var getNewBandNames = function(prefix, bandNames){
  21.     var seq = ee.List.sequence(1, 4);
  22.     //var seq = ee.List.sequence(1, bandNames.length());
  23.     return seq.map(function(n){
  24.       return ee.String(prefix).cat(ee.Number(n).int());
  25.     });      
  26.   };
  27.   
  28.   // This function accepts mean centered imagery, a scale and
  29.   // a region in which to perform the analysis.  It returns the
  30.   // Principal Components (PC) in the region as a new image.
  31.   var getPrincipalComponents = function(centered, scale, region){
  32.     var year = centered.get('yearmonth');
  33.     var arrays = centered.toArray();
  34.     
  35.     // Compute the covariance of the bands within the region.
  36.     var covar = arrays.reduceRegion({
  37.       reducer: ee.Reducer.centeredCovariance(),
  38.       geometry: region,
  39.       scale: scale,
  40.       bestEffort:true,
  41.       maxPixels: 1e13
  42.     });
  43.         
  44.     // Get the 'array' covariance result and cast to an array.
  45.     // This represents the band-to-band covariance within the region.
  46.     var covarArray = ee.Array(covar.get('array'));
  47.         
  48.     // Perform an eigen analysis and slice apart the values and vectors.
  49.     var eigens = covarArray.eigen();
  50.  
  51.     // This is a P-length vector of Eigenvalues.
  52.     var eigenValues = eigens.slice(1, 0, 1);
  53.     // This is a PxP matrix with eigenvectors in rows.
  54.     var eigenVectors = eigens.slice(1, 1);
  55.  
  56.     // Convert the array image to 2D arrays for matrix computations.
  57.     var arrayImage = arrays.toArray(1);
  58.     // Left multiply the image array by the matrix of eigenvectors.
  59.     var principalComponents = ee.Image(eigenVectors).matrixMultiply(arrayImage);
  60.  
  61.     // Turn the square roots of the Eigenvalues into a P-band image.
  62.     var sdImage = ee.Image(eigenValues.sqrt())
  63.     .arrayProject([0]).arrayFlatten([getNewBandNames('SD',bandNames)]);
  64.  
  65.     // Turn the PCs into a P-band image, normalized by SD.
  66.     return principalComponents
  67.     // Throw out an an unneeded dimension, [[]] -> [].
  68.     .arrayProject([0])
  69.     // Make the one band array image a multi-band image, [] -> image.
  70.     .arrayFlatten([getNewBandNames('PC', bandNames)])
  71.     // Normalize the PCs by their SDs.
  72.     .divide(sdImage)
  73.     .set('yearmonth', yearmonth);
  74.   };
  75.     
  76.   // Get the PCs at the specified scale and in the specified region
  77.   image = getPrincipalComponents(centered, 1000, region);
  78.   return image;
  79. };
  80.   
  81. var PCA_Imgcol = Normed_Imgs.map(getPCA);
  82. print('PCA_Imgcol', PCA_Imgcol);
  83.  
  84. // to compute RSEI
  85. var Normed_RSEI = PCA_Imgcol.map(function(image){
  86.   // image = image.addBands(ee.Image(1).rename('constant'));
  87.   // var rsei = image.expression('constant - pc1' , {
  88.   //     constant: image.select('constant'),
  89.   //     pc1: image.select('PC1')
  90.   // });
  91.   var rsei = image.select('PC1');
  92.   var RSEI = getNorm(rsei);
  93.   return image.addBands(RSEI.rename('RSEI'));
  94. });
  95.  
  96. print('Normed_RSEI', Normed_RSEI);

6. 批量导出RSEI影像

  1. // data download
  2. // 批量下载函数
  3. function exportImage(image, fileName) {  
  4.     Export.image.toDrive({  
  5.       image: image.select('RSEI'),
  6.       description: 'RSEI-' + fileName,
  7.       folder: 'GEE_export',
  8.       region: roi,
  9.       scale: 30,
  10.       crs: 'EPSG:4326',
  11.       maxPixels: 1e13  //最大像元素
  12.   });  
  13. }
  14.  
  15. // 生成列表，迭代下载
  16. var indexList = Normed_RSEI.reduceColumns(ee.Reducer.toList(), ['system:index']).get('list'); 
  17. indexList.evaluate(function(indexs) {
  18.   for (var i = 0; i < indexs.length; i++) { 
  19.     var image = ee.Image(Normed_RSEI.filter(ee.Filter.eq('system:index', indexs[i])).first());
  20.     var name = 2013 + i + '-8';
  21.     exportImage(image, name);
  22.   }
  23. });

7. 批量RUN

  1. // 运行结果出来后，将代码粘贴到网页控制台
  2.  
  3. runTasks = function() {
  4.     const evt = new MouseEvent('click', {bubbles: true, cancelable: true, ctrlKey: true})
  5.     $$('.run-button' ,$$('ee-task-pane')[0].shadowRoot).forEach(function(e) {
  6.         e.dispatchEvent(evt)
  7.     })
  8. }
  9. runTasks()

8. RSEI运行结果

9. 完整代码

  1. // 定义成自己的研究区
  2. var roi = table;
  3.  
  4. // 在图层上显示
  5. Map.addLayer(roi, {}, 'yanjiuqu');
  6.  
  7. // 定义SR数据的去云函数
  8. function maskL8sr(image) {
  9.   var qaMask = image.select('QA_PIXEL').bitwiseAnd(parseInt('11111', 2)).eq(0);
  10.   var saturationMask = image.select('QA_RADSAT').eq(0);
  11.  
  12.   // Apply the scaling factors to the appropriate bands.
  13.   var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2);
  14.   var thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0);
  15.  
  16.   // Replace the original bands with the scaled ones and apply the masks.
  17.   return image.addBands(opticalBands, null, true)
  18.               .addBands(thermalBands, null, true)
  19.               .updateMask(qaMask)
  20.               .updateMask(saturationMask);
  21. }
  22.  
  23. //_______________________________________________________________________________________
  24. // 定义SR数据集并进行去云处理
  25. var Imagecol = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
  26.                  .filterBounds(roi)
  27.                  .filterDate('2013-04-08', '2022-12-31')
  28.                  .filterMetadata('CLOUD_COVER', 'less_than', 50) // 根据需要替换阈值
  29.                  .sort('CLOUD_COVER', true)
  30.                  .map(function(image){
  31.                    return image.set('year', ee.Image(image).date().get('year')).set('month', ee.Image(image).date().get('month'));
  32.                  })
  33.                  .map(maskL8sr);
  34.  
  35. print('CloudRemoved_Images', Imagecol);
  36.  
  37. // 筛选想要的月份数据并进行裁剪
  38. var imgList = ee.List([]);
  39. for (var year = 2013; year < 2023; year++) {
  40.   for (var month = 8; month < 9; month++) { // 根据需要替换月份
  41.     var img = Imagecol.filterMetadata('year', 'equals', year)
  42.                       .filterMetadata('month', 'equals', month)
  43.                       .mosaic()
  44.                       .clip(roi);
  45.     var yearmonth = ee.String(ee.Number(year)).cat('-').cat(ee.String(ee.Number(month)));
  46.     var image = img.set('yearmonth', yearmonth);
  47.     var imgList = imgList.add(image);
  48.   }
  49. }
  50.  
  51. print('Filtered_Images', imgList);
  52.  
  53. // 筛选出有效的数据集
  54. var Valid_Imgs = ee.ImageCollection(imgList)
  55.                    .map(function(image) {
  56.                     var bands = image.bandNames();
  57.                     var hasBands = bands.size().gt(0);
  58.                     return ee.Algorithms.If(hasBands, image, null);
  59.                    },true);
  60.  
  61. print('Valid_Images', Valid_Imgs);
  62.  
  63. //_______________________________________________________________________________________
  64. // 数据处理
  65. // 定义去水体函数
  66. function waterMask(image) {
  67.   var ndwi = image.normalizedDifference(['SR_B3', 'SR_B5']);
  68.   var mask = ndwi.lt(0.1);
  69.   return image.updateMask(mask);
  70. }
  71.  
  72. // 定义NDVI函数
  73. function getNDVI(image) {
  74.   var ndvi = image.normalizedDifference(['SR_B5', 'SR_B4']);
  75.   return image.addBands(ndvi.rename('NDVI'));
  76. }
  77.  
  78. // 定义WET函数
  79. function getWET(image) {
  80.   var WET = image.expression('B*(0.1509) + G*(0.1973) + R*(0.3279) + NIR*(0.3406) + SWIR1*(-0.7112) + SWIR2*(-0.4572)', {
  81.     'B': image.select('SR_B2'),
  82.     'G': image.select('SR_B3'),
  83.     'R': image.select('SR_B4'),
  84.     'NIR': image.select('SR_B5'),
  85.     'SWIR1': image.select('SR_B6'),
  86.     'SWIR2': image.select('SR_B7')
  87.   });
  88.   return image.addBands(WET.rename('WET'));
  89. }
  90.  
  91. // 定义NDBSI函数
  92. function getNDBSI(image) {
  93.   // NDBSI = ( IBI + SI ) / 2
  94.   var IBI = image.expression('(2 * SWIR1 / (SWIR1 + NIR) - (NIR / (NIR + RED) + GREEN / (GREEN + SWIR1))) / (2 * SWIR1 / (SWIR1 + NIR) + (NIR / (NIR + RED) + GREEN / (GREEN + SWIR1)))', {
  95.     'SWIR1': image.select('SR_B6'),
  96.     'NIR': image.select('SR_B5'),
  97.     'RED': image.select('SR_B4'),
  98.     'GREEN': image.select('SR_B3')
  99.   });
  100.   var SI = image.expression('((SWIR1 + RED) - (NIR + BLUE)) / ((SWIR1 + RED) + (NIR + BLUE))', {
  101.     'SWIR1': image.select('SR_B6'),
  102.     'NIR': image.select('SR_B5'),
  103.     'RED': image.select('SR_B4'),
  104.     'BLUE': image.select('SR_B2')
  105.   });
  106.   var NDBSI = (IBI.add(SI)).divide(2);
  107.   return image.addBands(NDBSI.rename('NDBSI'));
  108. }
  109.  
  110. // 定义LST函数
  111. function getLST(image) {
  112.   var LST = image.expression('(LST - 273.15)', {
  113.     LST: image.select('ST_B10')
  114.   });
  115.   return image.addBands(LST.rename('LST'));
  116. }
  117.  
  118. // 计算4个指数并去水体
  119. var Indexed_Imgs = Valid_Imgs.map(waterMask)
  120.                              .map(getNDVI)
  121.                              .map(getWET)
  122.                              .map(getNDBSI)
  123.                              .map(getLST);
  124.  
  125. //_______________________________________________________________________________________
  126. // 筛选出所需波段
  127. var bandNames = ['NDVI', 'WET', 'NDBSI', 'LST'];
  128. var Prepared_Imgs = Indexed_Imgs.select(bandNames);
  129.  
  130. // 定义归一化函数
  131. function getNorm(image) {
  132.   var minMax = image.reduceRegion({
  133.     reducer: ee.Reducer.minMax(),
  134.     geometry: roi,
  135.     scale: 30,
  136.     crs: 'EPSG:4326',
  137.     bestEffort: true,
  138.     maxPixels: 1e13
  139.   });
  140.   var yearmonth = image.get('yearmonth');
  141.   var normalize  = ee.ImageCollection.fromImages(
  142.       image.bandNames().map(function(name){
  143.             name = ee.String(name);
  144.             var band = image.select(name);
  145.             return band.unitScale(ee.Number(minMax.get(name.cat('_min'))), ee.Number(minMax.get(name.cat('_max'))));
  146.         })
  147.     ).toBands().set('yearmonth', yearmonth);
  148.     return normalize;
  149. }
  150.  
  151. // 对指数分别归一化
  152. var Normed_Imgs = Prepared_Imgs.map(getNorm);
  153.  
  154. print('Normed_Images', Normed_Imgs);
  155.  
  156. //_______________________________________________________________________________________
  157. // 定义PCA函数
  158. var getPCA = function(image){
  159.   
  160.   var bandNames = image.bandNames();
  161.   var region = roi;
  162.   var yearmonth = image.get('yearmonth');
  163.   // Mean center the data to enable a faster covariance reducer
  164.   // and an SD stretch of the principal components.
  165.   var meanDict = image.reduceRegion({
  166.         reducer:  ee.Reducer.mean(),
  167.         geometry: region,
  168.         scale: 30,
  169.         maxPixels: 1e13
  170.   });
  171.   var means = ee.Image.constant(meanDict.values(bandNames));
  172.   var centered = image.subtract(means).set('yearmonth', yearmonth);
  173.   
  174.   
  175.   // This helper function returns a list of new band names.
  176.   var getNewBandNames = function(prefix, bandNames){
  177.     var seq = ee.List.sequence(1, 4);
  178.     //var seq = ee.List.sequence(1, bandNames.length());
  179.     return seq.map(function(n){
  180.       return ee.String(prefix).cat(ee.Number(n).int());
  181.     });      
  182.   };
  183.   
  184.   // This function accepts mean centered imagery, a scale and
  185.   // a region in which to perform the analysis.  It returns the
  186.   // Principal Components (PC) in the region as a new image.
  187.   var getPrincipalComponents = function(centered, scale, region){
  188.     var year = centered.get('yearmonth');
  189.     var arrays = centered.toArray();
  190.     
  191.     // Compute the covariance of the bands within the region.
  192.     var covar = arrays.reduceRegion({
  193.       reducer: ee.Reducer.centeredCovariance(),
  194.       geometry: region,
  195.       scale: scale,
  196.       bestEffort:true,
  197.       maxPixels: 1e13
  198.     });
  199.         
  200.     // Get the 'array' covariance result and cast to an array.
  201.     // This represents the band-to-band covariance within the region.
  202.     var covarArray = ee.Array(covar.get('array'));
  203.         
  204.     // Perform an eigen analysis and slice apart the values and vectors.
  205.     var eigens = covarArray.eigen();
  206.  
  207.     // This is a P-length vector of Eigenvalues.
  208.     var eigenValues = eigens.slice(1, 0, 1);
  209.     // This is a PxP matrix with eigenvectors in rows.
  210.     var eigenVectors = eigens.slice(1, 1);
  211.  
  212.     // Convert the array image to 2D arrays for matrix computations.
  213.     var arrayImage = arrays.toArray(1);
  214.     // Left multiply the image array by the matrix of eigenvectors.
  215.     var principalComponents = ee.Image(eigenVectors).matrixMultiply(arrayImage);
  216.  
  217.     // Turn the square roots of the Eigenvalues into a P-band image.
  218.     var sdImage = ee.Image(eigenValues.sqrt())
  219.     .arrayProject([0]).arrayFlatten([getNewBandNames('SD',bandNames)]);
  220.  
  221.     // Turn the PCs into a P-band image, normalized by SD.
  222.     return principalComponents
  223.     // Throw out an an unneeded dimension, [[]] -> [].
  224.     .arrayProject([0])
  225.     // Make the one band array image a multi-band image, [] -> image.
  226.     .arrayFlatten([getNewBandNames('PC', bandNames)])
  227.     // Normalize the PCs by their SDs.
  228.     .divide(sdImage)
  229.     .set('yearmonth', yearmonth);
  230.   };
  231.     
  232.   // Get the PCs at the specified scale and in the specified region
  233.   image = getPrincipalComponents(centered, 1000, region);
  234.   return image;
  235. };
  236.   
  237. var PCA_Imgcol = Normed_Imgs.map(getPCA);
  238. print('PCA_Imgcol', PCA_Imgcol);
  239.  
  240. // 计算归一化的RSEI
  241. var Normed_RSEI = PCA_Imgcol.map(function(image){
  242.  
  243.   // 利用'1-PC1'表示RSEI
  244.   // image = image.addBands(ee.Image(1).rename('constant'));
  245.   // var rsei = image.expression('constant - pc1' , {
  246.   //     constant: image.select('constant'),
  247.   //     pc1: image.select('PC1')
  248.   // });
  249.   
  250.   // 利用'PC1'表示RSEI
  251.   var rsei = image.select('PC1');
  252.   var RSEI = getNorm(rsei);
  253.   return image.addBands(RSEI.rename('RSEI'));
  254. });
  255.  
  256. print('Normed_RSEI', Normed_RSEI);
  257.  
  258. //_______________________________________________________________________________________
  259. // 数据下载
  260. // 批量下载函数
  261. function exportImage(image, fileName) {  
  262.     Export.image.toDrive({  
  263.       image: image.select('RSEI'),
  264.       description: 'RSEI-' + fileName,
  265.       folder: 'GEE_export',
  266.       region: roi,
  267.       scale: 30,
  268.       crs: 'EPSG:4326',
  269.       maxPixels: 1e13  //最大像元素
  270.   });  
  271. }
  272.  
  273. // 生成列表，迭代下载
  274. var indexList = Normed_RSEI.reduceColumns(ee.Reducer.toList(), ['system:index']).get('list'); 
  275. indexList.evaluate(function(indexs) {
  276.   for (var i = 0; i < indexs.length; i++) { 
  277.     var image = ee.Image(Normed_RSEI.filter(ee.Filter.eq('system:index', indexs[i])).first());
  278.     var name = 2013 + i + '-8';
  279.     exportImage(image, name);
  280.   }
  281. });

参考了以下文章：

PCA函数参考：利用GEE计算城市遥感生态指数(RSEI)——Landsat 8为例_gee计算rsei_何处惹尘埃？的博客-CSDN博客GEE，RSEI，城市生态状况快速监测与评价_gee计算rseihttps://blog.csdn.net/qq_35490698/article/details/125751534

批量导出参考：GEE学习笔记001-ImageCollection（影像集合）的批量裁剪和批量导出_gee导出imagecollection_小郭学地信的博客-CSDN博客在GEE（Google Earth Engine）中，遥感影像可以是单张的Image，也可以是多张影像组成的影像集合（ImageCollection）。其中，Image的裁剪很简单，直接调用clip函数即可，但是针对ImageCollection，不能直接使用clip函数。_gee导出imagecollectionhttps://blog.csdn.net/guoshihui123/article/details/129242873批量RUN参考：新版GEE批量导出点run代码_P0级推荐算法工程师的博客-CSDN博客偶然在GEE的论坛上看到杨志强老师写的新版GEE批量导出点run代码，分享给大家，杨志强老师还实现了LandTrendr算法（膜拜大神），论坛讨论链接在这儿了。Exporting images without clicking on RUN button (google.com)当导出run的列表全部显示后，打开浏览器的console命令行运行窗口，复制粘贴杨老师的批量点run代码，再enter，耐心等待即可runTasks = function() { const evt = new Mhttps://blog.csdn.net/SunStrongInChina/article/details/120707460

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