GEE数据的白天day/夜晚night LST数据按照QC掩膜后的结果差异明显

var modisLST = ee.ImageCollection("MODIS/061/MOD11A2"),
    modisLST_Aqua = ee.ImageCollection("MODIS/061/MYD11A2");
var startYear = 2017
var endYear = 2021
var startDate = ee.Date.fromYMD(startYear, 1, 1)
var endDate = ee.Date.fromYMD(endYear, 12, 31)
var bitwiseExtract = function(input, fromBit, toBit) {
  var maskSize = ee.Number(1).add(toBit).subtract(fromBit)
  var mask = ee.Number(1).leftShift(maskSize).subtract(1)
  return input.rightShift(fromBit).bitwiseAnd(mask)
}
// Cloud Masking
var applyQaMask_day = function(image) {
  var lstDay = image.select('LST_Day_1km')
  var qcDay = image.select('QC_Day')
  var qaMask = bitwiseExtract(qcDay, 0, 1).lte(1)
  //var dataQualityMask = bitwiseExtract(qcDay, 2, 3).eq(0)
  var dataQualityMask = bitwiseExtract(qcDay, 2, 3).lte(1)
  var EmissivityerrorMask = bitwiseExtract(qcDay, 4, 5).lte(1)
  var lstErrorMask = bitwiseExtract(qcDay, 6, 7).eq(0)
  var mask = qaMask.and(dataQualityMask).and(lstErrorMask).and(EmissivityerrorMask)
  return lstDay.updateMask(mask)
}
var applyQaMask_night = function(image) {
  var lstNight = image.select('LST_Night_1km')
  var qcNight = image.select('QC_Night')
  var qaNightMask = bitwiseExtract(qcNight, 0, 1).lte(1)
  //var dataNightQualityMask = bitwiseExtract(qcNight, 2, 3).eq(0)
  var dataNightQualityMask = bitwiseExtract(qcNight, 2, 3).lte(1)
  var EmissivityNighterrorMask = bitwiseExtract(qcNight, 4, 5).lte(1)
  var lstNightErrorMask = bitwiseExtract(qcNight, 6, 7).eq(0)
  var Nightmask = qaNightMask.and(dataNightQualityMask).and(lstNightErrorMask).and(EmissivityNighterrorMask)
  return lstNight.updateMask(Nightmask)
}
// Apply the function to all images in the collection
var terra_day = modisLST_Aqua
  .filter(ee.Filter.date(startDate, endDate)).select('LST_Day_1km','QC_Day');
var terraMasked_day = terra_day.map(applyQaMask_day)
var terra_night = modisLST_Aqua
  .filter(ee.Filter.date(startDate, endDate)).select('LST_Night_1km','QC_Night');
var terraMasked_night = terra_night.map(applyQaMask_night)
// print(terraMasked_day.first())
// 月合成 Create NDVI composite for every month
var years = ee.List.sequence(startYear,endYear);
var months = ee.List.sequence(1, 12);
// Map over the years and create a monthly average collection
var monthlyImages = years.map(function(year) {
  return months.map(function(month) {
    var filtered = terraMasked_night
      .filter(ee.Filter.calendarRange(year, year, 'year'))
      .filter(ee.Filter.calendarRange(month, month, 'month'))
    var monthly = filtered.mean()
    return monthly.set({'month': month, 'year': year})
  })
}).flatten()
//print(monthlyImages)
// We now have 1 image per month for entire duratioon
var monthlyCol = ee.ImageCollection.fromImages(monthlyImages)
print(monthlyCol,'monthlyCol')
var visParams = {min:13000, max:16000, palette: ['green', 'yellow', 'red']}
Map.addLayer(terra_day.select('LST_Day_1km').first(), visParams, 'Original LSTday Image');
Map.addLayer(terraMasked_day.first(), visParams, 'LSTday Masked');
Map.addLayer(terra_night.select('LST_Night_1km').mean(), visParams, 'Original LSTnight Image');
Map.addLayer(terraMasked_night.mean(), visParams, 'LSTnight Masked');
Map.addLayer(terraMasked_night.min(), visParams, 'LSTnight Masked min');