前言：这次用到了LANDSAT/LT05/C01/T1_SR、LANDSAT/LE07/C01/T1_SR、LANDSAT/LC08/C01/T1_SR三个平台的数据，通过去云和计算NDVI的值，获取该地区20年NDVI值的变化。

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1 去云算法

去云LANDSAT5和7的算法代码：

//去云457的算法 
function cloudMaskL457(image) {
  var qa = image.select('pixel_qa');
  // If the cloud bit (5) is set and the cloud confidence (7) is high
  // or the cloud shadow bit is set (3), then it's a bad pixel.
  var cloud = qa.bitwiseAnd(1 << 5).and(qa.bitwiseAnd(1 << 7)).or(qa.bitwiseAnd(1 << 3));
  // Remove edge pixels that don't occur in all bands
  var mask2 = image.mask().reduce(ee.Reducer.min());
  return image.updateMask(cloud.not()).updateMask(mask2);
};
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去云LANDSAT8的算法代码：

//去云8的算法
function cloudMaskL8sr(image) {
  // Bits 3 and 5 are cloud shadow and cloud, respectively.
  var cloudShadowBitMask = (1 << 3);
  var cloudsBitMask = (1 << 5);
  // Get the pixel QA band.
  var qa = image.select('pixel_qa');
  // Both flags should be set to zero, indicating clear conditions.
  var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0)
                 .and(qa.bitwiseAnd(cloudsBitMask).eq(0));
  return image.updateMask(mask);
}
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2 NDVI的计算

LANDSAT5和7的算法代码：

//计算57NDVI的值 
function opNdvi57(image){
  var ndvi = image.normalizedDifference(['B4', 'B3']).rename('NDVI')
  return image.addBands(ndvi);
}
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LANDSAT8的算法代码：

//计算8NDVI的值 
function opNdvi8(image){
  var ndvi = image.normalizedDifference(['B5', 'B4']).rename('NDVI')
  return image.addBands(ndvi);
}
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3 L5、L7、L8综合计算某地区NDVI逐年时序变化

代码如下（以鹿邑县为例）：

var geometry = ee.FeatureCollection('users/www1573979951/luyixian')
Map.centerObject(geometry,6)

var L5_COL = getNdvi(2000, 2011, "LANDSAT/LT05/C01/T1_SR", cloudMaskL457, opNdvi57);

var L7_COL = getNdvi(2012, 2012, "LANDSAT/LE07/C01/T1_SR", cloudMaskL457, opNdvi57);

var L8_COL = getNdvi(2013, 2020, "LANDSAT/LC08/C01/T1_SR", cloudMaskL8sr, opNdvi8);

var data = ee.ImageCollection(L5_COL.merge(L7_COL).merge(L8_COL))

print(data);

var yearlychart = ui.Chart.image.series({
    imageCollection: data.select('NDVI_median'),
    region: geometry,
    reducer: ee.Reducer.mean(),
    scale: 500,
    xProperty:'Year'
    }).setOptions({
      interpolateNulls: true,
      lineWidth: 2,
      title: 'NDVI Daily Time Seires',
      vAxis: {title: 'NDVI', viewWindow: {max: 0.7,min: 0.2,}},
      hAxis: {title: 'Date'},
      trendlines: { 0: {title: 'NDVI_trend',type:'linear', showR2: true,  color:'red', visibleInLegend: true}}
    });
print(yearlychart);


function getNdvi(startYear, finishYear, name, cloudMask, op){
  var years = ee.List.sequence(startYear, finishYear);
  return ee.ImageCollection(years.map(function(year){
    var start = ee.Date.fromYMD(year, 1, 1);
    var end = start.advance(12, 'month');
    var ndvi = ee.ImageCollection(name).filterDate(start, end).map(cloudMask).map(op);
    return ndvi.reduce(ee.Reducer.median()).clip(geometry).set('Year',year);
}))
}


//去云457的算法 
function cloudMaskL457(image) {
  var qa = image.select('pixel_qa');
  // If the cloud bit (5) is set and the cloud confidence (7) is high
  // or the cloud shadow bit is set (3), then it's a bad pixel.
  var cloud = qa.bitwiseAnd(1 << 5).and(qa.bitwiseAnd(1 << 7)).or(qa.bitwiseAnd(1 << 3));
  // Remove edge pixels that don't occur in all bands
  var mask2 = image.mask().reduce(ee.Reducer.min());
  return image.updateMask(cloud.not()).updateMask(mask2);
};

//去云8的算法
function cloudMaskL8sr(image) {
  // Bits 3 and 5 are cloud shadow and cloud, respectively.
  var cloudShadowBitMask = (1 << 3);
  var cloudsBitMask = (1 << 5);
  // Get the pixel QA band.
  var qa = image.select('pixel_qa');
  // Both flags should be set to zero, indicating clear conditions.
  var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0)
                 .and(qa.bitwiseAnd(cloudsBitMask).eq(0));
  return image.updateMask(mask);
}


//计算57NDVI的值 
function opNdvi57(image){
  var ndvi = image.normalizedDifference(['B4', 'B3']).rename('NDVI')
  return image.addBands(ndvi);
}

//计算8NDVI的值 
function opNdvi8(image){
  var ndvi = image.normalizedDifference(['B5', 'B4']).rename('NDVI')
  return image.addBands(ndvi);
}
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21年的数据集截图：

表格变化趋势截图：

CSV数据截图：

4 通过L8计算某地区不同月份的NDVI变化情况

代码如下（以鹿邑县为例）：

var geometry = ee.FeatureCollection('users/www1573979951/luyixian')
Map.centerObject(geometry,11.5)

var landsat8 = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR").map(opNdvi8);

var image_ls8_8 = ee.Image(landsat8.filterBounds(geometry).filterDate('2020-07-01', '2020-08-31').sort('CLOUD_COVER').first().clip(geometry));
var image_ls8_10 = ee.Image(landsat8.filterBounds(geometry).filterDate('2020-10-01', '2020-11-30').sort('CLOUD_COVER').first().clip(geometry));

// 显示影像的真实颜色
var trueColor_ls8 = {bands: ['B4', 'B3', 'B2'], min: 0, max: 3000};
Map.addLayer(image_ls8_8, trueColor_ls8, 'Landsat8 yr2020_07_08 true color');
Map.addLayer(image_ls8_10, trueColor_ls8, 'Landsat8 yr2020_10_11 true color');

//计算8NDVI的值 
function opNdvi8(image){
  var ndvi = image.normalizedDifference(['B5', 'B4']).rename('NDVI')
  return image.addBands(ndvi);
}
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2020鹿邑县年7月8月遥感影像截图：

2020鹿邑县年10月11月遥感影像截图：

结果分析：虽然两张图片不是很全，但是也能得到7月8月的NDVI值比较高，植被比较丰富。10月11月的NDVI值比较低，植被比较稀少。原因：7月8月玉米生长阶段，10月11月玉米已经成熟且收获，田地里绿色植被很少。