1简介

在本模块中，我们将讨论以下概念：

1. 了解常用于遥感影像的数据校正类型。
2. 如何直观地比较同一数据集中不同预处理级别的空间数据。
3. 如何在 Google Earth Engine for Landsat 8 表面反射率图像中执行云遮蔽和云遮蔽评估。

// Define simple cloud mask, based in values from the 'pixel_qa' band.
// Essentially, values of 322 = land and 324 = water.
var qa = sr.select('pixel_qa');
var mask = qa.eq(322).or(qa.eq(324));
var sr_cm = sr.updateMask(mask);// Add your new map for comparison to the cloudy image.
Map.addLayer(sr_cm, srvis, 'single_scene_masked');

来自俄勒冈州南部的原始 Landsat 8 图像。我们的图像以 Mt McLoughlin (2,893 m) 为中心，东面是 Upper Klamath Lake。如果缩小图像，则可以看到梅德福（西）和克拉马斯福尔斯（东）这两个城市。

// Define how your cloudMask function should work.
var cloudMask = function(image) {// var mask = image.select('pixel_qa').eq(322).or(image.select('pixel_qa').eq(324));var mask = image.select('pixel_qa').eq(322);return image.mask(mask);
};// This time we'll look at a more ecologically-focused example, using NDVI.
var collection = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")
.filterDate('2018-06-01','2018-09-30')
.filter(ee.Filter.calendarRange(140, 270)) // roughly, our potential growing season
.filterMetadata('WRS_PATH', 'equals', 27)
.filterMetadata('WRS_ROW', 'equals', 26)
.map(cloudMask)
.map(function NDVI(i){return i.addBands(i.normalizedDifference(['B5','B4']).rename('NDVI'))  // generates NDVI band
});var median = collection.median();// Red = lower values and darker green = higher values
var collectionVis = {bands: 'NDVI', min: 0.5, max: 0.95, palette: ['red','yellow','green','003300']};Map.addLayer(median, collectionVis, 'NDVI');

加载 TOA 集合后，由于对太阳效应的影响进行了一些校正，部分图像看起来更清晰。但是，似乎还有工作要做！

// Finally, source an image from Tier 1 Landsat 8 surface reflection.
var sr = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_045031_20180811');// Define the true-color vis params.
var srvis = {bands: ['B4','B3','B2'], min: 0, max: 3000, gamma: 1.4};// Add the surface reflectance image to your map.
Map.addLayer(sr, srvis, 'sr');

应用大气校正似乎大大提高了我们的图像清晰度，尤其是在上克拉马斯湖和 Mt McGloughlin 以西的农业区。

// Center our map on southern Oregon, USA.
Map.setCenter(-122.3158, 42.4494, 12);// Import Tier 1 Raw Landsat 8 scene.
var raw = ee.Image('LANDSAT/LC08/C01/T1/LC08_045031_20180811');// Define the true-color vis params.
var rawvis = {bands: ['B4','B3','B2'], min: 0.0, max: 30000.0, gamma: 1};// Add the raw image to your map.
Map.addLayer(raw, rawvis, 'raw');// To begin your comparison, bring in an image from Tier 1 Landsat 8 Top of Atmosphere.
var toa = ee.Image('LANDSAT/LC08/C01/T1_TOA/LC08_045031_20180811');// Define the true-color vis params.
var toavis = {bands: ['B4','B3','B2'], min: 0.0, max: 0.4};// Add the TOA image to your map.
Map.addLayer(toa, toavis, 'toa');// Finally, source an image from Tier 1 Landsat 8 surface reflection.
var sr = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_045031_20180811');// Define the true-color vis params.
var srvis = {bands: ['B4','B3','B2'], min: 0, max: 3000, gamma: 1.4};// Add the TOA image to your map.
Map.addLayer(sr, srvis, 'sr');

// Load an initial image to test view a cloudy image.
var sr = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_027026_20180829');// Define visparams for true-color image.
var srvis = {bands: ['B4', 'B3', 'B2'], min: 0, max: 3000, gamma: 1.4};// Add your cloudy image to the map.
Map.addLayer(sr, srvis, 'single_scene');
Map.setCenter(-91.8859, 48.8936, 8.81);

Quetico-Superior 国家上空多云的图像。

// Define simple cloud mask, based in values from the 'pixel_qa' band.
// Essentially, values of 322 = land and 324 = water.
var qa = sr.select('pixel_qa');
var mask = qa.eq(322).or(qa.eq(324));
var sr_cm = sr.updateMask(mask);// Add your new map for comparison to the cloudy image.
Map.addLayer(sr_cm, srvis, 'single_scene_masked');

成功移除云和云阴影，但生成的图像不会留下很多可用像素。

// Define how your cloudMask function should work.
var cloudMask = function(image) {// var mask = image.select('pixel_qa').eq(322).or(image.select('pixel_qa').eq(324));var mask = image.select('pixel_qa').eq(322);return image.mask(mask);
};// This time we'll look at a more ecologically-focused example, using NDVI.
var collection = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")
.filterDate('2018-06-01','2018-09-30')
.filter(ee.Filter.calendarRange(140, 270)) // roughly, our potential growing season
.filterMetadata('WRS_PATH', 'equals', 27)
.filterMetadata('WRS_ROW', 'equals', 26)
.map(cloudMask)
.map(function NDVI(i){return i.addBands(i.normalizedDifference(['B5','B4']).rename('NDVI'))  // generates NDVI band
});var median = collection.median();// Red = lower values and darker green = higher values
var collectionVis = {bands: 'NDVI', min: 0.5, max: 0.95, palette: ['red','yellow','green','003300']};Map.addLayer(median, collectionVis, 'NDVI');

可视化 2018 年整个生长季节的 NDVI 中值。随意切换不同的背景层。您的图像可能与此处显示的图像不完全相同。

// Create counts band.
var counts = collection.select('NDVI').count();// Find our potential maximum count by getting the size of the initial image collection.
print(collection.size());// In this palette, red = lower values and blue = higher values.
var countVis = {min: 0, max: 6, palette: ['b2182b','ef8a62','fddbc7', 'd1e5f0','67a9cf','2166ac']};Map.addLayer(counts, countVis, 'counts');

可视化用于计算每个像素中值的图像数量。较深的红色值较低，较深的蓝色值较高。

// Draw your own rectangle and try to cover most of the Landsat scene and 
// then we'll make a histogram of the distribution. Because the scene area 
// is so large  GEE requires us to perform some aggregating. But this will 
// give us a general sense of our distribution.
var histogram = ui.Chart.image.histogram(counts, geometry, 120);// Display the histogram.
print(histogram);

使用直方图函数量化来自计数层的值分布的数量。

// Load an initial image to test view a cloudy image.
var sr = ee.Image('LANDSAT/LC08/C01/T1_SR/LC08_027026_20180829');// Define visparams for true-color image.
var srvis = {bands: ['B4', 'B3', 'B2'], min: 0, max: 3000, gamma: 1.4};// Add your cloudy image to the map.
Map.addLayer(sr, srvis, 'single_scene')
Map.setCenter(-91.8859, 48.8936, 8.81);// Define simple cloud mask, based in values from the 'pixel_qa' band.
// Essentially, values of 322 = land and 324 = water.
var qa = sr.select('pixel_qa');
var mask = qa.eq(322).or(qa.eq(324));
var sr_cm = sr.updateMask(mask);// Add your new map for comparison to the cloudy image.
Map.addLayer(sr_cm, srvis, 'single_scene_masked');// Define how your cloudMask function should work.
var cloudMask = function(image) {// var mask = image.select('pixel_qa').eq(322).or(image.select('pixel_qa').eq(324));var mask = image.select('pixel_qa').eq(322);return image.mask(mask);
};// This time we'll look at a more ecologically-focused example, using NDVI.
var collection = ee.ImageCollection("LANDSAT/LC08/C01/T1_SR")
.filterDate('2018-06-01','2018-09-30')
.filter(ee.Filter.calendarRange(140, 270)) // roughly, our potential growing season
.filterMetadata('WRS_PATH', 'equals', 27)
.filterMetadata('WRS_ROW', 'equals', 26)
.map(cloudMask)
.map(function NDVI(i){return i.addBands(i.normalizedDifference(['B5','B4']).rename('NDVI'))  // generates NDVI band
});var median = collection.median();// Red = lower values and darker green = higher values.
var collectionVis = {bands: 'NDVI', min: 0.5, max: 0.95, palette: ['red','yellow','green','003300']};Map.addLayer(median, collectionVis, 'NDVI');// Create counts band.
var counts = collection.select('NDVI').count();// Find our potential maximum count by getting the size of the initial image collection.
print(collection.size());// In this palette, red = lower values and blue = higher values.
var countVis = {min: 0, max: 6, palette: ['b2182b','ef8a62','fddbc7', 'd1e5f0','67a9cf','2166ac']};Map.addLayer(counts, countVis, 'counts');// Draw your own rectangle and try to cover most of the Landsat scene and 
// then we'll make a histogram of the distribution. Because the scene area 
// is so large  GEE requires us to perform some aggregating. But this will 
// give us a general sense of our distribution.
var histogram = ui.Chart.image.histogram(counts, geometry, 120);// Display the histogram.
print(histogram);