GEEer成长日记四：Landsat8_SR计算NDVI并时间序列分析

　　前两次我们介绍了MODIS_NDVI和LANDSAT8_NDVI产品的时间序列，它们都是基于Landsat8_TOA影像制成的。实际工作中我们还需要通过LANDSAT8_SR影像进行NDVI时间序列分析，那么该怎么开展工作呢？本期我们就来介绍介绍。

　　下一期我们将介绍Sentinel-2数据在时间序列方面的研究。

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LANDSAT/LC08/C01/T1_SR (deprecated)

 如果想深入了解这两个数据集，可以登录:

Landsat Surface Reflectance | U.S. Geological Survey

　　官方介绍：This dataset is the atmospherically corrected surface reflectance from the Landsat 8 OLI/TIRS sensors. These images contain 5 visible and near-infrared (VNIR) bands and 2 short-wave infrared (SWIR) bands processed to orthorectified surface reflectance, and two thermal infrared (TIR) bands processed to orthorectified brightness temperature

　　These data have been atmospherically corrected using LaSRC and includes a cloud, shadow, water and snow mask produced using CFMASK, as well as a per-pixel saturation mask.

　　Strips of collected data are packaged into overlapping "scenes" covering approximately 170km x 183km using a standardized reference grid.

　　See also the USGS page on SR QA bands.

　　SR can only be produced for Landsat assets processed to the L1TP level

　　Data provider notes:

• Although Surface Reflectance can be processed only from the Operational Land Imager (OLI) bands, SR requires combined OLI/Thermal Infrared Sensor (TIRS) product (LC8) input in order to generate the accompanying cloud mask. Therefore, OLI only (LO8), and TIRS only (LT8) data products cannot be calculated to SR.

• SR is not run for a scene with a solar zenith angle greater than 76°.

• Users are cautioned to avoid using SR for data acquired over high latitudes (> 65°).

• The panchromatic band (ETM+ Band 7, OLI Band 8) is not processed to Surface Reflectance.

• Efficacy of SR correction will be likely reduced in areas where atmospheric correction is affected by adverse conditions:

  • Hyper-arid or snow-covered regions

  • Low sun angle conditions

  • Coastal regions where land area is small relative to adjacent water

  • Areas with extensive cloud contamination

　　This product is generated by Google using a Docker image supplied by USGS.

　　分辨率：30m

　　波段介绍：

　　首先在计算NDVI之前，我们先了解几种NDVI计算的方式，感谢大佬的总结（https://blog.csdn.net/weixin_43360896/article/details/108344915）。我就直接搬用了。

//方法一：普通方式，通过将数学公式翻译为代码直接计算
function NDVI_V1(img) {
 var nir = img.select("B5");
 var red = img.select("B4");
 var ndvi = nir.subtract(red).divide(nir.add(red));
 return ndvi;
}

//方法二：将计算公式直接带入，通过解析字符串实现计算。这种方式更加灵活，在某些特殊情况下非常好用，而且非常直观。
//在这里多插一嘴，这个img.expression在复杂公式计算的时候，可谓真香！屡试不爽
function NDVI_V2(img) {
 var nir = img.select("B5");
 var red = img.select("B4");
 var ndvi = img.expression(
   "(B5 - B4)/(B5 + B4)",
   {
     "B5": nir,
     "B4": red
   });
 return ndvi;
}

//方法三：GEE将计算公式封装为一个方法可以直接调用
function NDVI_V3(img) {
 var ndvi = img.normalizedDifference(["B5","B4"]);
 return ndvi;
}

 　　好了，介绍完几种NDVI的计算方法后，我们使用第三种方法开始进行时间序列分析。

//还是老样子哈，以广东省2020年为目标
var geometry = ee.FeatureCollection('users/ZhengkunWang/guangdongsheng')
Map.centerObject(geometry,6)

var colorizedVis = {
  min: -0.8,
  max: 0.8,
  palette: ['blue', 'white', 'green'],
};
//去云的方法照搬就可以，想深入了解的同学可以去看看波段介绍
//cloud mask
function maskL8sr(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);
}

var col = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
.map(maskL8sr)
.filterDate('2020-01-01','2020-12-31')
.filterBounds(geometry)
.map(function(image){
  var ndvi = image.normalizedDifference(['B5', 'B4']).rename('NDVI');
  return image.addBands(ndvi)
})
.select('NDVI');

Map.addLayer(col.mean().clip(geometry), colorizedVis, 'col');

　　结果如图：

print(ui.Chart.image.series(col, geometry, ee.Reducer.mean(), 500));

结果如图：

　　其实看完时间序列，还是会发现，有好多异常值。对我们的研究来说肯定是不适用的。

　　我们先研究研究它的变化趋势吧。

var landsat8trendline = Chart.image.series(col, geometry, ee.Reducer.mean(), 500);
landsat8trendline = landsat8trendline
    .setOptions({
        title: 'Landsat 8 SR NDVI',
        hAxis: {title: 'Date', gridlines: {count: 10}},
        vAxis: {title: 'NDVI',viewWindowMode: 'explicit', viewWindow: {max: 1,min: -0.25,},gridlines: {count: 5,}},
        interpolateNulls: true, 
        lineWidth: 1,
    pointSize: 1,
    trendlines: { 0: {title: 'NDVI_trend',type:'linear', showR2: true,  color:'red', visibleInLegend: true}}
    });
print(landsat8trendline)

　　这次我们在趋势线分析的时候，还加入了趋势线的相关系数R2进行分析，虽然结果0.033，但是看起来还算可以。强行接受

　　既然结果不太美观，我们在进行月平均分析一波吧。

var years = ee.List.sequence(2020, 2020);
var months = ee.List.sequence(1, 12);
var landsat8monthlymeanNDVI =  ee.ImageCollection.fromImages(
  years.map(function (y) {
    return months.map(function(m) {
    return col.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));
    });
  }).flatten()
);
print(ui.Chart.image.series(landsat8monthlymeanNDVI, geometry, ee.Reducer.mean(), 500));

 　　稍微平滑了一点点吧算是。再加入趋势线看看相关系数

var monthlymeantrendline = Chart.image.series(landsat8monthlymeanNDVI, geometry, ee.Reducer.mean(), 500);
monthlymeantrendline = monthlymeantrendline
    .setOptions({
        title: 'Landsat 8 SR NDVI',
        hAxis: {title: 'Date', gridlines: {count: 10}},
        vAxis: {title: 'NDVI',viewWindowMode: 'explicit', viewWindow: {max: 1,min: -0.25,},gridlines: {count: 5,}},
        interpolateNulls: true, 
        lineWidth: 1,
    pointSize: 1,
    trendlines: { 0: {title: 'NDVI_trend',type:'linear', showR2: true,  color:'red', visibleInLegend: true}}
    });
print(monthlymeantrendline)

 　　哈哈，相关系数翻了10倍。其实趋势没有变，只是月平均NDVI更加平滑罢了。不过月平均分析还是挺管用的。

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