输出图像采集-陆地卫星-8表面温度(谷歌地球引擎)

Question

我一直在努力出口从Landsat-8得到的季度平均表面温度。我无意中发现了“适者生存”的批处理模块。然而，它不是为每个季度导出一个图像，而是输出多个空图像。有人能帮我弄清楚吗？

var batch = require('users/fitoprincipe/geetools:batch');

// This example demonstrates the use of the Landsat 8 Collection 2, Level 2
// QA_PIXEL band (CFMask) to mask unwanted pixels.

function maskL8sr(image) {
  // Bit 0 - Fill
  // Bit 1 - Dilated Cloud
  // Bit 2 - Cirrus
  // Bit 3 - Cloud
  // Bit 4 - Cloud Shadow
  var qaMask = image.select('QA_PIXEL').bitwiseAnd(parseInt('11111', 2)).eq(0).copyProperties(image, ['system:time_start']);
  var saturationMask = image.select('QA_RADSAT').eq(0).copyProperties(image, ['system:time_start']);

  // Apply the scaling factors to the appropriate bands.
  var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2).copyProperties(image, ['system:time_start']);
  var thermalBands = image.select('ST_B.*').multiply(0.00341802).add(-124.15).copyProperties(image, ['system:time_start']);

  // Replace the original bands with the scaled ones and apply the masks.
  return image.addBands(opticalBands, null, true)
      .addBands(thermalBands, null, true)
      .updateMask(qaMask)
      .updateMask(saturationMask)
      .copyProperties(image, ['system:time_start']);
}

// Map the function over one year of data.
var collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
                     .filterDate('2016-01-01', '2021-12-31')
                     .map(maskL8sr);

// Display the results.
Map.setCenter(120.9854, 14.5994,10);  // Metro Manila
Map.addLayer(composite.select('ST_B10').clip(table), {min: -10, max: 50, palette: 'blue,green,yellow,orange,red'});

var start = ee.Date('2016-01-01');
var end = ee.Date('2021-12-31');
var numbQuarters = end.difference(start, 'month').divide(3).ceil();

// make a composite image for every quarter
var quarterlyImages = ee.ImageCollection.fromImages(
            ee.List.sequence(1, numbQuarters).map(
              function(quarter){
              var startTemp = start.advance(
                              ee.Number(quarter).subtract(1).multiply(3), 'month');
              var endTemp = start.advance(ee.Number(quarter).multiply(3), 'month');
              var image = collection.filterDate(startTemp, endTemp)
                                    .filterBounds(table)
                                    .mean()
                                    .clip(table)
                                    .select('ST_B10');
              return image.set('system:time_start', startTemp.millis(),
                               'system:time_end', endTemp.millis());
            }));
print(quarterlyImages);


batch.Download.ImageCollection.toDrive(quarterlyImages, 'Quarterly Folder', 
                {name: 'LST_{system:index}',
                  scale: 30});

这是代码。

Answer 1

这是您的工作代码，图像不是空白的。

请选择您感兴趣的区域(此处命名为表)，并将其放在//下载图像集合中作为区域: table

这是你的工作代码。

我已将日期从2020-01-01改为2021-12-31，任务显示了8张要上传的图片以供驱动，因为两年将有8个季度。

​

​

var batch = require('users/fitoprincipe/geetools:batch');

// This example demonstrates the use of the Landsat 8 Collection 2, Level 2
// QA_PIXEL band (CFMask) to mask unwanted pixels.

function maskL8sr(image) {
  // Bit 0 - Fill
  // Bit 1 - Dilated Cloud
  // Bit 2 - Cirrus
  // Bit 3 - Cloud
  // Bit 4 - Cloud Shadow
  var qaMask = image.select('QA_PIXEL').bitwiseAnd(parseInt('11111', 2)).eq(0).copyProperties(image, ['system:time_start']);
  var saturationMask = image.select('QA_RADSAT').eq(0).copyProperties(image, ['system:time_start']);

  // Apply the scaling factors to the appropriate bands.
  var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2).copyProperties(image, ['system:time_start']);
  var thermalBands = image.select('ST_B.*').multiply(0.00341802).add(-124.15).copyProperties(image, ['system:time_start']);

  // Replace the original bands with the scaled ones and apply the masks.
  return image.addBands(opticalBands, null, true)
      .addBands(thermalBands, null, true)
      .updateMask(qaMask)
      .updateMask(saturationMask)
      .copyProperties(image, ['system:time_start']);
}

// Map the function over one year of data.
var collection = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')
                     .filterDate('2020-01-01', '2021-12-31')
                     .map(maskL8sr);


// Display the results.
Map.setCenter(120.9854, 14.5994,10);  // Metro Manila


var start = ee.Date('2020-01-01');
var end = ee.Date('2021-12-31');
var numbQuarters = end.difference(start, 'month').divide(3).ceil();

// make a composite image for every quarter
var quarterlyImages = ee.ImageCollection.fromImages(
            ee.List.sequence(1, numbQuarters).map(
              function(quarter){
              var startTemp = start.advance(
                              ee.Number(quarter).subtract(1).multiply(3), 'month');
              var endTemp = start.advance(ee.Number(quarter).multiply(3), 'month');
              var image = collection.filterDate(startTemp, endTemp)
                                    .filterBounds(table)
                                    .mean()
                                    .clip(table)
                                    .select('ST_B10');
              return image.set('system:time_start', startTemp.millis(),
                               'system:time_end', endTemp.millis());
            }));
print(quarterlyImages);

Map.addLayer(quarterlyImages.first(), {min: -10, max: 50, palette: 'blue,green,yellow,orange,red'});

//Download Image Collection 
batch.Download.ImageCollection.toDrive(quarterlyImages, 'Landsat8', {
  name: 'LST_{system:index}',
  type: 'float',
  scale: 30,
  region: table
});