两个数据集，一个点文件FeatureCollection，另一个Landsat8影像数据ImageCollection，现在遍历点文件中的每一个点，提取这个点位上所有影像的栅格值（波段值），并导出为csv，适用。

代码：

var firstlyr = ee.FeatureCollection("users/points");

var fqshp = ee.FeatureCollection("users/boundary");

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);

var saturationMask = image.select('QA_RADSAT').eq(0);

// Apply the scaling factors to the appropriate bands.

var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2);

var thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0);

// 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);

}

// Map the function over one year of data.

var dataset = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')

.filterDate('2021-01-01', '2021-02-01')

.filterBounds(fqshp)

.map(maskL8sr);

print('images number:',dataset.size());

print(dataset);

var mapfunc = function(feat) {

var geom = feat.geometry();

var addProp = function(img, f) {

var newf = ee.Feature(f);

var values = img.reduceRegion({

reducer:ee.Reducer.first(),

geometry:geom,

scale:30,

maxPixels:1e11});

var values_array = ee.Algorithms.If(

values.get('SR_B1'),

values.toArray(['SR_B1','SR_B2','SR_B3','SR_B4','SR_B5','SR_B6','SR_B7']),

9999);

var fields_name = ee.String(img.get('system:index'));

return ee.Feature(newf.setMulti(ee.Dictionary.fromLists(ee.List([fields_name]),ee.List([values_array]))));

};

var newfeat = ee.Feature(dataset.iterate(addProp, feat));

return newfeat;

};

var newft = firstlyr.map(mapfunc);

Export.table.toDrive({

collection: newft,

description: 'newft',

fileFormat:'CSV',

folder:'newft',

fileNamePrefix:'newft'});

print('done!');

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