date - 在不同的日期使用不同的算法取消屏蔽像素（谷歌地球引擎）

Question

一般来说，我想获得像素，这些像素可以指示水稻幼苗移栽到田间之前稻田的淹水和移栽阶段。为此，我有我感兴趣的区域，即一个点周围的缓冲区，我想在该处取消屏蔽可能的大米像素。

可通过以下标准检测泛滥和移栽阶段： LSWI - NDVI > 0 || LSWI - EVI > 0

一旦此标准对日期 x 的像素有效，则以下标准必须对日期 x + 50 到 60 天的同一像素有效：LSWI - NDVI < 0 || LSWI - EVI < 0

我使用我命名为“l8”的 Landsat 表面反射收集。

// Create a new point geometry with the centre coordinates 
var point = ee.Geometry.Point([120.92557043089494,15.71666782285468])

// Create a buffer arount the point
var buff = point.buffer(5000)

//////////////////////////// Cloud mask /////////////////////////////////////////
// This example demonstrates the use of the pixel QA band to mask
// clouds in surface reflectance (SR) data.  It is suitable
// for use with any of the Landsat SR datasets.

// Function to cloud mask from the pixel_qa band of Landsat 8 SR data.
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 mask1 = qa.bitwiseAnd(cloudShadowBitMask).eq(0)
      .and(qa.bitwiseAnd(cloudsBitMask).eq(0));

  var mask2 = image.select('B.*').gt(0).reduce('min');
  
  // Mask out water using the pixel qa band
  // Problem: Also rice paddy pixels were masked out!!
  //var mask3 = image.select('pixel_qa').bitwiseAnd(2).neq(0);

  // Return the masked image, scaled to TOA reflectance, without the QA bands.
  return image.updateMask(mask1.and(mask2)).divide(10000) // in the first brackets: .and(mask3))
      .select("B[0-9]*")
      .copyProperties(image, ["system:time_start"]);
}

// Function to mask further clouds using B2, blue band, threshold
var maskclouds = function(image) {
  var B2 = image.select(['B2']);
  var bin = B2.gt(0.2);
return image.updateMask(bin.lt(1));
};

//////////////////////////// Filter the time frame /////////////////////////////////////////

// Prepare the date range filter for the dry season
var dS18_19Filter = ee.Filter.date('2018-12-01','2019-11-30');
var dS19_20Filter = ee.Filter.date('2019-12-01','2020-11-30');

var dSallFilter = ee.Filter.or(dS18_19Filter, dS19_20Filter)

//////////////////////////// Calculate indices ////////////////////////////////////

// Calculate NDVI 
var addNDVI = function(image) {
  var ndvi = image.normalizedDifference(['B5', 'B4']).rename('NDVI');
  return image.addBands(ndvi);
};

// Calculate LSWI
var addLSWI = function(image) {
  var lswi = image.normalizedDifference(['B5', 'B6']).rename('LSWI');
  return image.addBands(lswi);
};

// Compute EVI using an expression
var addEVI = function(image) {
  var evi = image.expression(
    '2.5 * ((NIR - RED) / (NIR + 6 * RED - 7.5 * BLUE + 1))', 
    {
      'NIR': image.select('B5'),
      'RED': image.select('B4'),
      'BLUE': image.select('B2')
  }).rename('EVI').float();
  return image.addBands(evi);
};

////////////////////////////// Image Collection //////////////////////////////////
// Load Landsat 8 image collection and filter study area bounds and date
// cloud mask, select all bands 
var l8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
         .filterBounds(buff)
         .filter(dSallFilter)
         // apply the cloud mask for the whole image collection
         .map(maskL8sr)
         .map(maskclouds)
         .select('B[2-7]','B10','B11')
         .map(addNDVI)
         .map(addLSWI)
         .map(addEVI)
         .sort('system:index');
        
// Print filtered image collection to verify if the filtering has worked
print('Landsat8', l8);

//////////////////////////////////// Paddy Rice ///////////////////////////////////////

// LSWI - NDVI/EVI > 0

var image = l8.first()

// Paddy rice by expression
var paddyRice = function(image) {
  var paddy = image.expression(
      'LSWI - NDVI > 0 || LSWI - EVI > 0', 
     {
      'LSWI': image.select('LSWI'),
      'NDVI': image.select('NDVI'),
      'EVI': image.select('EVI')
  }).rename('flTr');
    return image.addBands(paddy);
};

var paddyMask = function(image) {
  var pMask = image.expression(
      'LSWI - NDVI > 0 || LSWI - EVI > 0', 
     {
      'LSWI': image.select('LSWI'),
      'NDVI': image.select('NDVI'),
      'EVI': image.select('EVI')
  }); 
  return image.updateMask(pMask);
};

// Mask out pixels where
// algorithm at date x applies
// AND another alogrithm applies at date x + 50 - 60 days (image between time period)
// for the same pixel

var l8 = l8.map(paddyRice).map(paddyMask)

// Clarify if rice grows for pixels identified as rice
var rice = function(image){
 var rice2 = image.expression(
    'LSWI - NDVI < 0 || LSWI - EVI < 0', // after 50 to 60 days
    {
      'LSWI': image.select('LSWI'),
      'NDVI': image.select('NDVI'),
      'EVI': image.select('EVI')
});
  return image.updateMask(rice2)
};

对于最后一个函数，我错过了这个简短算法在不同时间针对相同像素的那个。关于帮助和提示，我将不胜感激。

标签： datemaskindicesgoogle-earth-engine