前言：此代码过于复杂，了解即可。

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1 LANDSAT/LC08/C01/T1_SR

• 该数据集是来自Landsat 8 OLI/TIRS传感器的经大气校正的表面反射率。这些图像包含5个可视和近红外（VNIR）波段和2个短波红外（SWIR）波段，分别处理为正射校正表面反射率，以及两个热红外（TIR）波段，处理为正入射校正亮度温度。
• 这些数据已经使用LaSRC进行了大气校正，包括使用CFMASK生成的云、阴影、水和雪掩模，以及每像素饱和度掩模。
• 使用标准化参考网格，将采集的数据条打包成重叠的“场景”，覆盖约170km x 183km。

2 构建FeatureCollection得到每月的温度数据

代码如下（以鹿邑县为例）

var geometry = ee.FeatureCollection('users/www1573979951/luyixian')
Map.centerObject(geometry,7)
var startYr = 2020;
var endYr = 2020; 
 
// year sequence 
var startDate = ee.Date.fromYMD(startYr,01,01);
var endDate = ee.Date.fromYMD(endYr+1,01,01);

//去云代码
function maskL8sr(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);
}
 
// Load the collection 
var l8lst = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR").filterDate(startDate, endDate).filterBounds(geometry).map(maskL8sr);
print(l8lst);
print(ui.Chart.image.series(l8lst, geometry, ee.Reducer.mean(), 1000));//打印成折线图
//** Visualization parameters
var vizParams = {
  bands: ['B6', 'B5', 'B4'],
  min: 0,
  max: 3200,
  gamma: 1.4,
};
Map.addLayer(l8lst.mean().clip(geometry),vizParams,'l8 imagery');


//--------------------------------------
 
//Projection of Landsat imagery,
//it shows an error if bands of the image don't have all the same
var l8proj = ee.Image(l8lst.first()).projection();
 
//** Calculate number of months withiin the specified time period
var dateDiff = endDate.difference(startDate,'month');
var noMonths = ee.Number(dateDiff).round().subtract(1);
 
//** Create list of dates, one for each month in the time period
var allMonths = ee.List.sequence(0,noMonths,1);
var dateList = allMonths.map(function(monthNo){
  return startDate.advance(monthNo,'month');
});
// print(dateList);
 
//** FUNCTION: for calculating monthly LST values
var calcMonthlyLST = function(date){
  
  //** Filter collection by month
  var start = ee.Date(date);
  var end = start.advance(1,'month');
  var collection = l8lst.filterDate(start,end);
  
  //** Create median composite for that month
  var monthMed = collection.median();
  
  //** Create NDVI and Thermal Band images from composite
  var ndvi = monthMed.normalizedDifference(['B5','B4']);
  var thermalImg = monthMed.select('B10').multiply(0.1);
  
  //*8 Calculate min and max NDVI
  var minNDVI = ee.Number(ndvi.reduceRegion({
  reducer: ee.Reducer.min(),
  geometry: geometry,
  scale: 30,
  maxPixels: 1e13
  }).values().get(0));
  
  var maxNDVI = ee.Number(ndvi.reduceRegion({
  reducer: ee.Reducer.max(),
  geometry: geometry,
  scale: 30,
  maxPixels: 1e13
  }).values().get(0));
  
  //** Fraction of vegetation (use 'if' statement so that where entire image is masked and
  //    minNDVI and maxNDVI are null, produce a null image)
  var fv = ee.Algorithms.If({
    condition: minNDVI && maxNDVI,
    trueCase: ndvi.subtract(minNDVI).divide(maxNDVI.subtract(minNDVI)).rename('FV'),
    falseCase: ee.Image()
  });
  //** Recast to ee.Image object
  fv = ee.Image(fv);
 
  //** Emissivity (again use 'if' statement for cases of null inputs)
  var a = ee.Number(0.004);
  var b = ee.Number(0.986);
  var EM = ee.Algorithms.If({
    condition: fv.bandNames(),
    trueCase: fv.multiply(a).add(b).rename('EMM'),
    falseCase: ee.Image()
  });
  //** Recast to ee.Image object
  EM = ee.Image(EM);
  
  //** Land surface temperature (again use 'if' statement for cases of null inputs)
  var LST = ee.Algorithms.If({
    condition: EM.bandNames(),
    trueCase: thermalImg.expression(
    '(Tb/(1 + (0.0010895* (Tb / 1.438))*log(Ep)))-273.15', {
      'Tb': thermalImg.select('B10'),
      'Ep': EM.select('EMM')
    }).rename('LST'),
    falseCase: ee.Image().rename('LST')
  });
 
  //**  Use reduceRegion() to extract per-region value for this month
  var meanVal = ee.Image(LST).reduceRegion({
    reducer: ee.Reducer.mean(),
    geometry: geometry,
    scale: 30,
  maxPixels: 1e13,
    crs: l8proj
  });
 
  //** Create feature with desired data/properties and empty geometry, for charting by feature
  var ft = ee.Feature(null, {
    'system:time_start': date,
    'date': date.format('YYYY-MM-dd'),
    'value': meanVal.get('LST')
  });
  return ft;
};
 
//** Create feature collection, one for each month, wherein a property 'status' is
//    added to indicate whether there is an empty collection for that month 
var dateFeats = dateList.map(function(date){
  var thisColl = l8lst.filterDate(ee.Date(date), ee.Date(date).advance(1,'month'));
  return ee.Algorithms.If({
    condition: thisColl.first(),
    trueCase: ee.Feature(null, {'date':date, 'status':1}),
    falseCase: ee.Feature(null, {'date':date, 'status':0})
  });
});
dateFeats = ee.FeatureCollection(dateFeats);
// print('dateFeats:', dateFeats);
 
//** Map over date features and create per-month/year mean LST composites
var perYrMonthlyLST = dateFeats.map(function(feature){
  var startDate = ee.Date(feature.get('date'));
  
  //** If month represented by this feature has imagery for it,
  //    calculate per-monnth LST; otherwise set LST to 0 (can't be 'null' for charting)
  var outFeat = ee.Algorithms.If({
    condition: ee.Number(feature.get('status')).eq(1),
    trueCase: ee.Feature(calcMonthlyLST(startDate)),
    falseCase: ee.Feature(null, {
      'system:time_start': startDate.millis(),
      'date': startDate,
      'value': 0
    })
  });
  return outFeat;
});
perYrMonthlyLST = ee.FeatureCollection(perYrMonthlyLST);
print(perYrMonthlyLST);
 
//** Create new graph for monthly temperatures over multiple years
var multiYrGraph = ui.Chart.feature.byFeature({
  features:perYrMonthlyLST,
  xProperty:'system:time_start',
  yProperties: 'value'});
 
//** Print graph to console
print(multiYrGraph.setChartType("ColumnChart").setOptions({vAxis: {title: 'LST [deg. C]'},hAxis: {title: 'Date'}}));
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3 通过生成月均值影像得到每月的温度数据

代码如下（以鹿邑县为例）：

var geometry = ee.FeatureCollection('users/www1573979951/luyixian')
Map.centerObject(geometry,7)


//cloud mask landsat7,landsat5, and landsat8 based on the pixel_qa band of Landsat SR data.
// function for cloud masking on three types of lan dsat

var LC8_BANDS = ['B4',  'B5', 'B10','pixel_qa']; //Landsat 8
var LC7_BANDS = ['B3',  'B4','B6','pixel_qa']; //Landsat 7
var LC5_BANDS = [ 'B3',  'B4','B6','pixel_qa']; //Llandsat 5
var STD_NAMES = ['red', 'nir', 'temp','qa'];

var cloudmasklandsat7and5and8= function(image){
    var Qlandsat5and7= image.select('qa');
    var cloudShadowBitMask = (1 << 3);
    var cloudsBitMask = (1 << 5);
    var mask5and7=Qlandsat5and7.clip(geometry).bitwiseAnd(cloudShadowBitMask).eq(0)
                                   .and(Qlandsat5and7.bitwiseAnd(cloudsBitMask).eq(0));
    return image.updateMask(mask5and7);
};




var landsat8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR').select(LC8_BANDS, STD_NAMES)
                  .filterDate('2013-01-01', '2021-12-31')
                  .filterBounds(geometry)
                  .filter(ee.Filter.lt('CLOUD_COVER', 25))
                 .map(cloudmasklandsat7and5and8);

var landsat7 = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR').select(LC7_BANDS, STD_NAMES)
                  .filterDate('1984-01-01', '2012-12-31')
                  .filterBounds(geometry)
                  .filter(ee.Filter.lt('CLOUD_COVER', 25))
                 .map(cloudmasklandsat7and5and8);

var landsat5 = ee.ImageCollection('LANDSAT/LT05/C01/T1_SR').select(LC7_BANDS, STD_NAMES)
                  .filterDate('1984-01-01', '2012-12-31')
                  .filterBounds(geometry)
                  .filter(ee.Filter.lt('CLOUD_COVER', 25))
                 .map(cloudmasklandsat7and5and8);

var landsatcolor={
  min: 0,
  max: 3000,
  gamma: 1.4,
};
//Map.addLayer(landsat8,landsatcolor, 'Landsat8' );
//Map.addLayer(landsat7,landsatcolor, 'Landsat7' );
//Map.addLayer(landsat5,landsatcolor, 'Landsat5' );
  
  
  // totall image collection
var landsatimage = ee.ImageCollection(landsat5.merge(landsat7).merge(landsat8));
Map.addLayer(landsatimage,landsatcolor, 'Landsat' );

 //    NDVI
 
 var setNdviMinMax=function (img) {
  var minMax = img
    .select('NDVI')
    .reduceRegion({
      reducer: ee.Reducer.minMax(),
      scale: 30,
      maxPixels: 1e13
    })
    ;
  return img.set({
    'NDVI_min': minMax.get('NDVI_min'),
    'NDVI_max': minMax.get('NDVI_max'),
  }).toFloat();
};
var NDVI=function(image){
  var ndvi = image.addBands(image.normalizedDifference(['nir', 'red']).rename('NDVI'));
  return setNdviMinMax(ndvi).clip(geometry).toFloat();
};

  var ndviParams = {
  min: -1,
  max: 1.0,
  palette: [
    'FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901',
    '66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01',
    '012E01', '011D01', '011301'
  ],
};

var landsatwithndvi=landsatimage.map(NDVI).filter(ee.Filter.notNull(['NDVI_min', 'NDVI_max']));
Map.addLayer(landsatwithndvi.select('NDVI').filter(ee.Filter.notNull(['NDVI_min', 'NDVI_max'])), ndviParams, 'landsatNdvi');

var addMinMaxBands=function (img) {
  var minMaxBands = ee.Image.constant([
    img.get('NDVI_min'),
    img.get('NDVI_max')])
    .rename(['NDVImin', 'NDVImax']);
  return img.clip(geometry).addBands(minMaxBands).toFloat();
};
 //2: map the min max function on collection
var landsatNdviminmax = landsatwithndvi.map(addMinMaxBands).filterBounds(geometry);

//fv 
var addFVband=function (img) {
  var ndvi = img.select('NDVI');
  var ndviMin = img.select('NDVImin');
  var ndviMax = img.select('NDVImax');
  var fvBand = ndvi
    .subtract(ndviMin)
    .divide(ndviMax.subtract(ndviMin))
    .rename('FV');
  return img.clip(geometry).addBands(fvBand).toFloat();
};
//4: fv COLLECTION FOR LANDSAT
var landsatfv= landsatNdviminmax.map(addFVband);

// Em

var addEMband=function (img){
  var FVb = img.select('FV');
  var a= ee.Number(0.004);
  var b= ee.Number(0.986);
  var EMBand = FVb
    .multiply(a).add(b).rename('EM');
  return img.addBands(EMBand).toFloat();
};

//5: EM COLLECTION FOR EACH LANDSAT
  
var landsatEM= landsatfv.map(addEMband);

//6: Thermal landsat 
var LST=function(image){
  var Thermal = image.addBands(image.select('temp').multiply(0.1).rename('Thermal'));
  return Thermal};

var landsatthermal= landsatEM.map(LST);

var LStfunction = function(image){
  var LSTEQ=image.expression(
    '(Tb/(1 + (0.0010895* (Tb / 1.438))*log(Ep)))-273.15', {
      'Tb': image.select('Thermal'),
      'Ep': image.select('EM')}).rename('LST');
 return  image.addBands(LSTEQ)};

var LST= landsatthermal.map(LStfunction);

Map.addLayer(LST.select('LST'),{min: -30, max: 32, palette: ['white','blue','green','yellow' ,'red']},'LST');

/// End of LST calculation for each image

/// Monthly mean LST

var years = ee.List.sequence(2016, 2020);
var months = ee.List.sequence(1, 12);
var monthlyLST =  ee.ImageCollection.fromImages(
  years.map(function (y) {
    return months.map(function(m) {
    return LST.filter(ee.Filter.calendarRange(y,y, 'year')).filter(ee.Filter.calendarRange(m, m, 'month')).mean().set('year', y).set('month', m).set('system:time_start', ee.Date.fromYMD(y, m, 1))
    .set('system:index',y+m);
    });
  }).flatten()
);
print(monthlyLST)
var monthlychart = ui.Chart.image.series(monthlyLST.select('LST'), geometry,ee.Reducer.mean(),500)
              .setChartType('LineChart').setOptions({
                interpolateNulls: true,
                title: 'LST monthly time series',
                hAxis: {title: 'Date'},
                vAxis: {title: 'LST',viewWindowMode: 'explicit', gridlines: {count: 10,}},
                trendlines: { 0: {title: 'LST_trend',type:'linear', showR2: true,  color:'red', visibleInLegend: true}}});
// Display.
print(monthlychart);
Map.addLayer(monthlyLST.select('LST'),{min: -30, max: 32, palette: ['white','blue','green','yellow' ,'red']},'LST_Monthly');
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