如何降低 Google Earth Engine 中图像集的空间分辨率？

Question

美好的一天

请您协助我完成以下工作。

我正在尝试使用 Landsat 7 和 8 为特定感兴趣区域 (ROI) 生成 NDVI 估计的时间序列。然后，我想将这些估计值与从 MODIS 16 天 NDVI 复合数据中获得的 NDVI 值进行比较。虽然，我熟悉如何在 Landsat 空间分辨率（30 m）下获取我的 ROI 的平均 Landsat NDVI 值，但我想将此分辨率的像素聚合到 MODIS NDVI 产品分辨率（500 m）。

我已尝试根据https://developers.google.com/earth-engine/guides/resample 提供的示例在下面提供的代码中执行此操作，但我的尝试没有成功。我收到错误“ndvi.reduceResolution 不是函数”

我是否需要为此创建一个函数并将其映射到图像集合上，还是应该只指定在绘制图表或将数据导出为 csv 时执行平均的比例？

非常感谢。

///Add region of interest
var ROI = ROI
Map.addLayer(ROI, {}, 'ROI')
Map.centerObject(ROI, 10)

//Define time of interest
// Ensure that the first image that is collected possesses data to calculate NDVI otherwise the script will not work as required
var startdate = '2013-01-01' 
var enddate = '2021-01-01' 

var years = ee.List.sequence(ee.Date(startdate).get('year'), ee.Date(enddate).get('year'));

///Create functions to mask clouds
/// see: https://landsat.usgs.gov/sites/default/files/documents/landsat_QA_tools_userguide.pdf

///This function masks clouds in Landsat 7 imagery.
function maskL7(im) {
  var qa = im.select('BQA');
  var mask = qa.eq(672);
  return im.updateMask(mask).copyProperties(im);
}

///This function masks clouds in Landsat 8 imagery.
function maskL8(im) {
  var qa = im.select('BQA');
  var mask = qa.eq(2720);
  return im.updateMask(mask).copyProperties(im);
}

///Import image collections, filter by date and ROI, apply cloud mask and clip to ROI

///Landsat 7 Collection 1 Tier 1 calibrated top-of-atmosphere (TOA) reflectance
var ls7toa = ee.ImageCollection('LANDSAT/LE07/C01/T1_TOA')
  .filterBounds(ROI)
    .filterDate(startdate, enddate)
      .map(function(im) {return maskL7(im)})
        .map(function(image){return image.clip(ROI)})

///Landsat 8 Collection 1 Tier 1 calibrated top-of-atmosphere (TOA) reflectance
var ls8toa = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA')
.filterBounds(ROI)
  .filterDate(startdate, enddate)
    .map(function(im) {return maskL8(im)})
      .map(function(image){return image.clip(ROI)})

///Create function to calculate NDVI using Landsat data
    
///Calculate NDVI for Landsat 7
var ls7_ndvi = ls7toa.map(function(image) {
  var ndvi = image.normalizedDifference(['B4', 'B3']).rename('ndvi');
  return image.addBands(ndvi);
});

///Calculate NDVI for Landsat 8
var ls8_ndvi = ls8toa.map(function(image) {
  var ndvi = image.normalizedDifference(['B5', 'B4']).rename('ndvi');
  return image.addBands(ndvi);
});

///Merge the image collections into one and only select the NDVI data
var landsat = ee.ImageCollection(ls7_ndvi.merge(ls8_ndvi));
var ndvi = landsat.select(['ndvi'])
print(ndvi, 'ndvi')

// Load a MODIS NDVI Collection
var modis = ee.Image(ee.ImageCollection("MODIS/006/MOD13A1")
            .first()
              .select('NDVI'))
                    
// Get information about the MODIS projection.
var modisProjection = modis.projection();
print('MODIS projection:', modisProjection);

// Get the Landsat NDVI collection at MODIS scale and projection.
var ndviMean = ndvi
    // Force the next reprojection to aggregate instead of resampling.
    .reduceResolution({
      reducer: ee.Reducer.mean(),
    })
    // Request the data at the scale and projection of the MODIS image.
    .reproject({
      crs: modisProjection
    });
                    
///Create a timer-series plot of NDVI
var chart = ui.Chart.image.series({
    imageCollection: ndviMean,
    region: ROI,
    reducer: ee.Reducer.mean(),
    scale: 500,
})

print(chart, "ndvi") 

Answer 1

确实，在 ImageCollection 上映射一个函数就可以了：

///Add region of interest
var ROI = ROI
Map.addLayer(ROI, {}, 'ROI')
Map.centerObject(ROI, 10)

//Define time of interest
// Ensure that the first image that is collected possesses data to calculate NDVI otherwise the script will not work as required
var startdate = '2013-01-01' 
var enddate = '2021-01-01' 

var years = ee.List.sequence(ee.Date(startdate).get('year'), ee.Date(enddate).get('year'));

///Create functions to mask clouds
/// see: https://landsat.usgs.gov/sites/default/files/documents/landsat_QA_tools_userguide.pdf

///This function masks clouds in Landsat 7 imagery.
function maskL7(im) {
  var qa = im.select('BQA');
  var mask = qa.eq(672);
  return im.updateMask(mask).copyProperties(im);
}

///This function masks clouds in Landsat 8 imagery.
function maskL8(im) {
  var qa = im.select('BQA');
  var mask = qa.eq(2720);
  return im.updateMask(mask).copyProperties(im);
}

///Import image collections, filter by date and ROI, apply cloud mask and clip to ROI

///Landsat 7 Collection 1 Tier 1 calibrated top-of-atmosphere (TOA) reflectance
var ls7toa = ee.ImageCollection('LANDSAT/LE07/C01/T1_TOA')
  .filterBounds(ROI)
    .filterDate(startdate, enddate)
      .map(function(im) {return maskL7(im)})
        .map(function(image){return image.clip(ROI)})

///Landsat 8 Collection 1 Tier 1 calibrated top-of-atmosphere (TOA) reflectance
var ls8toa = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA')
.filterBounds(ROI)
  .filterDate(startdate, enddate)
    .map(function(im) {return maskL8(im)})
      .map(function(image){return image.clip(ROI)})

///Create function to calculate NDVI using Landsat data
    
///Calculate NDVI for Landsat 7
var ls7_ndvi = ls7toa.map(function(image) {
  var ndvi = image.normalizedDifference(['B4', 'B3']).rename('ndvi');
  return image.addBands(ndvi);
});

///Calculate NDVI for Landsat 8
var ls8_ndvi = ls8toa.map(function(image) {
  var ndvi = image.normalizedDifference(['B5', 'B4']).rename('ndvi');
  return image.addBands(ndvi);
});

///Merge the image collections into one and only select the NDVI data
var landsat = ee.ImageCollection(ls7_ndvi.merge(ls8_ndvi));
var ndvi = landsat.select(['ndvi'])
print(ndvi, 'ndvi')

// Load a MODIS NDVI Collection
var modis = ee.Image(ee.ImageCollection("MODIS/006/MOD13A1")
            .first()
              .select('NDVI'))
                    
// Get information about the MODIS projection.
var modisProjection = modis.projection();
print('MODIS projection:', modisProjection);

// Get the Landsat NDVI collection at MODIS scale and projection.
var landsat_pro = ndvi.first().projection();    
var CopyScale = landsat_pro.nominalScale();
print(CopyScale, 'original scale Landsat (m)')

var landsat_resample = function(image){
  return image.reproject(landsat_pro, null, 500) // insert here the desired scale in meters

    // Force the next reprojection to aggregate instead of resampling.
    .reduceResolution({
      reducer: ee.Reducer.mean(),
      maxPixels: 1024
    })
  .copyProperties(image)
}

var ndviResample = ndvi.map(landsat_resample)
                    
///Create a timer-series plot of NDVI
var chart = ui.Chart.image.series({
    imageCollection: ndviResample,
    region: ROI,
    reducer: ee.Reducer.mean(),
    scale: 500,
})

print(chart, "ndvi")