Google Earth Engine（GEE）计算热度（LST）

今天来分享一下如何在GEE中计算热度，用地表温度来反演LST,选择影像的时间为6到9月，还是以山西省为研究区
实现代码如下：

//导入自己的研究区，将其定义为roi
var roi = ee.FeatureCollection("users/lilei655123/shanxi");
var star_date = '2020-06-01'//定义起始时间
var end_date = '2020-09-30'//定义终止时间
function rmL8Cloud(image) { 
  var cloudShadowBitMask = (1 << 3); 
  var cloudsBitMask = (1 << 5); 
  var qa = image.select('pixel_qa'); 
  var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0) 
                 .and(qa.bitwiseAnd(cloudsBitMask).eq(0));
  return image.updateMask(mask)
              .copyProperties(image)
              .copyProperties(image, ["system:time_start",'system:time_end']);
}
 
var L8_ = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR").filterBounds(roi)
              .filterDate(star_date, end_date) 
              //按云量排序//云量过滤
              .map(rmL8Cloud)
              .mean()
              .clip(roi)
print(L8_)  
var img = L8_.clip(roi)
//ndvi
    var NDVI = img.expression('(NIR-R)/(NIR+R)',{
      
       'R': img.select(['B4']).multiply(0.0001),
       'NIR': img.select(['B5']).multiply(0.0001),
})
// 选择第Band10辐射亮度波段LST
  var L8_B10 = img.select('B10').multiply(0.1)

// 计算植被覆盖度pv，假设最低值为0.05，最大值为0.95
  var imgFVC = ((NDVI.subtract(ee.Image(0.05))).divide((ee.Image(0.95)).subtract(ee.Image(0.05)))).float();
  var FVC=((imgFVC.lt(0)).multiply(0))
  .add(((imgFVC.gt(0)).and(imgFVC.lte(1))).multiply(imgFVC))
  .add((imgFVC.gt(1)).multiply(1))
//Map.addLayer(pv, {'min':0,'max':1,'palette':['#A9A9A9','00FF00']}, 'ImgFVC')
// 计算比辐射率c
  var c = FVC.expression("0.004*FVC+0.986", {FVC: FVC})
// // 计算黑体辐射亮度
  var LST = L8_B10.expression(
'(L8_B10/(1 + (0.00109* (L8_B10 / 1.438))*log(c)))-273.15', {
 L8_B10: L8_B10,
    c: c,
})
var mean = LST.reduceRegion({ 
  reducer: ee.Reducer.mean(), 
  geometry:roi, 
  scale: 30, 
  maxPixels: 1e13
  
});
  print(mean,'平均值')
var rsei_std = LST.reduceRegion({ 
    reducer: ee.Reducer.stdDev(), 
    geometry:roi, 
    scale: 30, 
    maxPixels: 10e13
    
  });
    print(rsei_std,'标准差')// 
    var max = LST.reduceRegion({ 
    reducer: ee.Reducer.max(),// 
    geometry:roi,// 
    scale: 30,// 
    maxPixels: 10e13// 
    });
    print(max,'max')
    var min = LST.reduceRegion({ 
  reducer: ee.Reducer.min(), 
  geometry:roi, 
  scale: 30, 
  maxPixels: 10e13
 
});
print(min,'min')  
Map.centerObject(roi,7) 
var styling = {color:"red",fillColor:"00000000"};
Map.addLayer(roi.style(styling),{},"geometry")
Map.addLayer(LST, {'min':2,'max':49,'palette':["eff3ff","c6dbef","9ecae1","6baed6","4292c6","2171b5","084594",
"fff5f0","fee0d2","fcbba1","fc9272","fb6a4a","ef3b2c","cb181d","99000d"]}, 'LST')

计算结果显示：

其中红色代表地表温度高，蓝色代表地表温度低
统计结果：

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声明：仅供学习使用！

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