Google Earth Engine（GEE）——S2影像异常值

在计算索引并生成高质量马赛克时，有一个明显的图像是异常值。我已经确定了图像，问题在于 B5 波段的值非常低，这使得索引具有非常高的值。

编号：哥白尼/S2_SR/20200829T174909_20200829T175522_T13UFP

代码：

  

   

    

     

    
    

     

      var imageCollection = ee.ImageCollection("COPERNICUS/S2_SR"),
     
    
   

    

     

    
    

     

          geometry = 
     
    
   

    

     

    
    

     
    /* color: #98ff00 */
     
    
   

    

     

    
    

     
    /* shown: false */
     
    
   

    

     

    
    

     
    /* displayProperties: [
     
    
   

    

     

    
    

     

       {
     
    
   

    

     

    
    

     

       "type": "rectangle"
     
    
   

    

     

    
    

     

       }
     
    
   

    

     

    
    

     

       ] */
     
    
   

    

     

    
    

     

          ee.Geometry.Polygon(
     
    
   

    

     

    
    

     

              [[[-103.99506389984352, 48.962358795597474],
     
    
   

    

     

    
    

     

                [-103.99506389984352, 46.88665586989701],
     
    
   

    

     

    
    

     

                [-99.68842327484352, 46.88665586989701],
     
    
   

    

     

    
    

     

                [-99.68842327484352, 48.962358795597474]]], null, false),
     
    
   

    

     

    
    

     

          geometry2 = /* color: #0b4a8b */ee.Geometry.Point([-102.87049298795543, 48.22935423511586]),
     
    
   

    

     

    
    

     

          geometry3 = /* color: #00ffff */ee.Geometry.Point([-101.57471425064051, 48.22316284019277]);
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      //get veg bare soil water
     
    
   

    

     

    
    

     

      function maskSCL(image) {
     
    
   

    

     

    
    

     
  var scl = image.select('SCL');
     
    
   

    

     

    
    

     
  var mask = scl.gte(4).and(scl.lte(6));
     
    
   

    

     

    
    

     
  return image.updateMask(mask);//.divide(10000);
     
    
   

    

     

    
    

     

      }
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      //calculate red edge chlorophyll index
     
    
   

    

     

    
    

     

      var getIndices = function(img){
     
    
   

    

     

    
    

     
  var im = img.divide(10000);
     
    
   

    

     

    
    

     
  var CIre=im.select('B7').divide(im.select('B5')).subtract(1).rename("CIre");
     
    
   

    

     

    
    

     
  return im.addBands(CIre).copyProperties(img,['system:time_start']);
     
    
   

    

     

    
    

     

      }
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      function addDOY(im){
     
    
   

    

     

    
    

     
  var doy = im.date().getRelative('day', 'year');
     
    
   

    

     

    
    

     
  var doyBand = ee.Image.constant(doy).uint16().rename('doy')
     
    
   

    

     

    
    

     

        doyBand = doyBand.updateMask(im.select('B8').mask())
     
    
   

    

     

    
    

     
  
     
    
   

    

     

    
    

     
  var yr = im.date().get('year')
     
    
   

    

     

    
    

     
  var yrBand = ee.Image.constant(yr).uint16().rename('year')
     
    
   

    

     

    
    

     

        yrBand = yrBand.updateMask(im.select('B8').mask())
     
    
   

    

     

    
    

     
  
     
    
   

    

     

    
    

     
  return im.addBands(doyBand).addBands(yrBand)
     
    
   

    

     

    
    

     

      }
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      //max CIre from July-Sept
     
    
   

    

     

    
    

     

      var dataset1 = imageCollection
     
    
   

    

     

    
    

     

      .filterBounds(geometry)
     
    
   

    

     

    
    

     

      .filter(ee.Filter.calendarRange(2018, 2021, 'year'))
     
    
   

    

     

    
    

     

      .filter(ee.Filter.calendarRange(7, 9, 'month'))
     
    
   

    

     

    
    

     

      .map(maskSCL)
     
    
   

    

     

    
    

     

      .map(getIndices)
     
    
   

    

     

    
    

     

      .map(addDOY)
     
    
   

    

     

    
    

     

      .select('CIre', 'doy', 'year');
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      var CIre_max = dataset1.qualityMosaic('CIre');
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      Map.addLayer(CIre_max.select('CIre'),
     
    
   

    

     

    
    

     

      {min:0,max:20,palette:['black','indigo','cyan','limegreen','yellow']},'CIre')
     
    
   

    

     

    
    

     

      Map.addLayer(CIre_max.select('doy'),
     
    
   

    

     

    
    

     

      {min:183,max:274,palette:['black','indigo','cyan','limegreen','yellow']},'doy')
     
    
   

    

     

    
    

     

      Map.addLayer(CIre_max.select('year'),
     
    
   

    

     

    
    

     

      {min:2018,max:2021,palette:['indigo','cyan','limegreen','yellow']},'year')
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      // var index = dataset1
     
    
   

    

     

    
    

     

      // .select('CIre')
     
    
   

    

     

    
    

     

      // .reduce(ee.Reducer.percentile([98]))
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      // //calculate band to use for quality mosaic of 98th percentile values
     
    
   

    

     

    
    

     

      // // loop over image collection
     
    
   

    

     

    
    

     

      // //get absolute difference from 98th percentile
     
    
   

    

     

    
    

     

      // //convert to 1/abs dif + .1 to use as band for quality mosaic
     
    
   

    

     

    
    

     

      // var forQM = dataset1.map(function(x){ 
     
    
   

    

     

    
    

     

      // // get absolute difference of the percentiles with the CI
     
    
   

    

     

    
    

     

      // var selector = x.select('CIre').subtract(index).abs();
     
    
   

    

     

    
    

     

      // var selector_fixed = selector.eq(0).multiply(0.1).add(selector)
     
    
   

    

     

    
    

     

      // // invert to let the min diff be the largest value
     
    
   

    

     

    
    

     

      // var invsel = ee.Image.constant(1).divide(selector_fixed);
     
    
   

    

     

    
    

     

      // // add inverted difference band to the image and name properly
     
    
   

    

     

    
    

     

      // return x.addBands(invsel.rename('selector_CIre')) 
     
    
   

    

     

    
    

     

      // });
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      // // // // use the new selectors to perform the quality mosaic and add to map
     
    
   

    

     

    
    

     

      // var CIre_p98QM = forQM
     
    
   

    

     

    
    

     

      // .qualityMosaic('selector_CIre')
     
    
   

    

     

    
    

     

      // .select(['CIre', 'doy'])
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      // Map.addLayer(CIre_p98QM.select('CIre'),
     
    
   

    

     

    
    

     

      // {min:0,max:20,palette:['black','indigo','cyan','limegreen','yellow']},'CIre_p98')
     
    
   

    

     

    
    

     

      // Map.addLayer(CIre_p98QM.select('doy'),
     
    
   

    

     

    
    

     

      // {min:183,max:274,palette:['black','indigo','cyan','limegreen','yellow']},'doy_p98')
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      ///print trouble image
     
    
   

    

     

    
    

     

      //ID COPERNICUS/S2_SR/20200829T174909_20200829T175522_T13UFP
     
    
   

    

     

    
    

     

      //Think B5 is not right
     
    
   

    

     

    
    

     

      var checkit = imageCollection
     
    
   

    

     

    
    

     

      .filterBounds(geometry2)
     
    
   

    

     

    
    

     

      .filter(ee.Filter.calendarRange(2020, 2020, 'year'))
     
    
   

    

     

    
    

     

      .filter(ee.Filter.calendarRange(240, 242, 'day_of_year'))
     
    
   

    

     

    
    

     

      print(checkit)
     
    
   

    

     

    
    

     

      Map.addLayer(checkit.max(),{bands:['B4','B3','B2'], min:100,max:2000},'trouble_image')
     
    
   

    

     

    
    

     

      Map.addLayer(checkit.max(),{bands:['B5'], min:100,max:2000},'trouble_b5')
     
    
   

    

     

    
    

     
 
     
    
   

    

     

    
    

     

      ///nearby image same time period...B5 better
     
    
   

    

     

    
    

     

      var checkit = imageCollection
     
    
   

    

     

    
    

     

      .filterBounds(geometry3)
     
    
   

    

     

    
    

     

      .filter(ee.Filter.calendarRange(2020, 2020, 'year'))
     
    
   

    

     

    
    

     

      .filter(ee.Filter.calendarRange(240, 242, 'day_of_year'))
     
    
   

    

     

    
    

     

      print(checkit)
     
    
   

    

     

    
    

     

      Map.addLayer(checkit.max(),{bands:['B4','B3','B2'], min:100,max:2000},'nearby_image')
     
    
   

    

     

    
    

     

      Map.addLayer(checkit.max(),{bands:['B5'], min:100,max:2000},'nearby_b5')
     
    
  
 

文章来源: blog.csdn.net，作者：此星光明，版权归原作者所有，如需转载，请联系作者。

原文链接：blog.csdn.net/qq_31988139/article/details/125689381