使用 Google Earth Engine 计算每个地区、月份和年份的 NDVI？

我想计算每个区域（管理级别 3，也称为 Wearda）、月份和年份的平均 NDVI。所以我的最终结果看起来像这样：

regions    year    month   NDVI   
---------------------------------
region_1     2010       1     0.5  
region_1     2010       2    -0.6  
region_1     2010       3     0.7  
region_1     2010       4    -0.3  
region_1     2010       5     0.4  
region_1     2010       6    -0.5  
region_1     2010       7     0.5  
region_1     2010       8    -0.7  
region_1     2010       9     0.8  
region_1     2010       10   -0.55  
region_1     2010       11   -0.3  
region_1     2010       12   -0.2  
region_2     2010       1     0.5  
region_2     2010       2    -0.6  
region_2     2010       3     0.7  
region_2     2010       4    -0.3  
region_2     2010       5     0.4  
region_2     2010       6    -0.5  
region_2     2010       7     0.5  
region_2     2010       8    -0.7  
region_2     2010       9     0.8  
region_2     2010       10   -0.55  
region_2     2010       11   -0.3  
region_2     2010       12   -0.2  
...          ...       ...    ...

我的代码基本上是针对 var modisNDVI 中的预定区域执行此操作。不过，我希望我的代码能够在 2010 年到 2015 年每个地区的每个月执行此操作。

我怎样才能做到这一点而不编写更多的 for 循环（迭代年和月）？

我应该使用 reduceRegion 或 .map() 来跳过（所有）for 循环吗？

我尝试过使用reduceRegions但未能将其应用于图像集合。

// import data
var region = ee.FeatureCollection("ft:1zRUOJL1LYCPJj-mjP6ZRx8sxYKNH8EwDw3EPP66K"),
modisNDVI = ee.ImageCollection("MODIS/MCD43A4_006_NDVI");

// Get NDVI 
var modisNDVI = ee.ImageCollection(modisNDVI.filterDate('2015-01-01', '2015-06-01'));
var woredaNames = region.aggregate_array("HRpcode")

// do something so I can get monthly data for each year (2010-2015) for earch woreda (690)
// I don't want to write another for loop for the year and month what is a more optimized way?

// Processing all the 690 takes long, for this example I've used 10 woreda's
for (var woreda=0; woreda < 10 ;woreda++){

    // Focus on one region:
    var focusRegion = region.filter(ee.Filter.eq('system:index', String(woreda)));

    // Clip modis image on focused region:
    var focus_NDVI_clip = modisNDVI.mean().clip(focusRegion);

    // aggregate mean over geometry from focused region:
    var mean_dict = focus_NDVI_clip.reduceRegion({
    reducer: ee.Reducer.mean(),
    geometry: focusRegion.geometry(),
    scale: 500,
    });

    // Append index to mean_dictionary and print it (eventually this should turn into a list):
    var woreda_code = ee.List(woredaNames).get(woreda);
    mean_dict = mean_dict.set('Woreda_code', ee.String(woreda_code));
    print(mean_dict);}

首先，您应该不惜一切代价避免在 Earth Engine 上使用 for 循环，它只会使系统陷入困境，并且对每个人都不利（请参阅本节的循环部分）page https://developers.google.com/earth-engine/client_server）。您可以使用嵌套映射 https://stackoverflow.com/questions/47102922/double-loop-with-map-function-in-google-earth-engine循环遍历特征集合，然后遍历所有时间段以提取您需要的信息：

// import data
var region = ee.FeatureCollection("ft:1zRUOJL1LYCPJj-mjP6ZRx8sxYKNH8EwDw3EPP66K"),
modisNDVI = ee.ImageCollection("MODIS/MCD43A4_006_NDVI");

var startDate = ee.Date('2010-01-01'); // set analysis start time
var endDate = ee.Date('2010-12-31'); // set analysis end time

// calculate the number of months to process
var nMonths = ee.Number(endDate.difference(startDate,'month')).round();

var result = region.map(function(feature){
  // map over each month
  var timeDict = ee.List.sequence(0,nMonths).map(function (n){
    // calculate the offset from startDate
    var ini = startDate.advance(n,'month');
    // advance just one month
    var end = ini.advance(1,'month');
    // filter and reduce
    var data = modisNDVI.filterDate(ini,end).mean().reduceRegion({
      reducer: ee.Reducer.mean(),
      geometry: feature.geometry(),
      scale: 1000
    });
    // return zonal mean with a time key
    return data.combine(ee.Dictionary({'time':ini}));
  });
  // return feature with a timeseries property and results
  return feature.set('timeseries',timeDict);
});

// print to see if it is doing what we expect...
print(result.select(["HRpcode",'timeseries']));

// Export the data to a table for further analysis
Export.table.toDrive({
  collection:result,
  description:"tester",
  fileFormat:"CSV",
  selectors:["HRpcode","timeseries"]
})

代码链接：https://code.earthengine.google.com/abf5eeb5c203310c11bf45c6714ae731 https://code.earthengine.google.com/abf5eeb5c203310c11bf45c6714ae731

在这个实现中，结果格式可能有点奇怪，结果是一个以字典作为属性的特征集合，而不是数组或表......但是，希望这可以为您提供您需要的东西，或者为您提供一种获得您想要的东西的方法。需要。