简介
本次的归一化教程，优化了数据去云，预处理等过程，同事将landsat 5/7/8集合分别进行了数据整合，也就是原始波段的处理，从而我们可以调用1985-至今任何一个时期的影像进行归一化处理。具体的原文介绍 请看原始的博客
原始博客
利用GEE（Google Earth Engine）在线处理NDVI、EVI、SAVI、NDMI等指数归一化教程！_gee计算ndwi-CSDN博客
代码
代码语言：javascript复制function maskL457sr(image) {
// Bit 0 - Fill
// Bit 1 - Dilated Cloud
// Bit 2 - Unused
// Bit 3 - Cloud
// Bit 4 - Cloud Shadow
var qaMask = image.select('QA_PIXEL').bitwiseAnd(parseInt('11111', 2)).eq(0);
var saturationMask = image.select('QA_RADSAT').eq(0);
// Apply the scaling factors to the appropriate bands.
var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2);
var thermalBand = image.select('ST_B6').multiply(0.00341802).add(149.0);
// Replace the original bands with the scaled ones and apply the masks.
return image.addBands(opticalBands, null, true)
.addBands(thermalBand, null, true)
.updateMask(qaMask)
.updateMask(saturationMask);
}
function maskL8sr(image) {
// Bit 0 - Fill
// Bit 1 - Dilated Cloud
// Bit 2 - Cirrus
// Bit 3 - Cloud
// Bit 4 - Cloud Shadow
var qaMask = image.select('QA_PIXEL').bitwiseAnd(parseInt('11111', 2)).eq(0);
var saturationMask = image.select('QA_RADSAT').eq(0);
// Apply the scaling factors to the appropriate bands.
var opticalBands = image.select('SR_B.').multiply(0.0000275).add(-0.2);
var thermalBands = image.select('ST_B.*').multiply(0.00341802).add(149.0);
// Replace the original bands with the scaled ones and apply the masks.
return image.addBands(opticalBands, null, true)
.addBands(thermalBands, null, true)
.updateMask(qaMask)
.updateMask(saturationMask);
}
function rename57 (image) {
return image.select(['SR_B1','SR_B2','SR_B3','SR_B4',"SR_B5","SR_B7"],['blue', 'green', 'red','nir',"swir1","swir2"]);
}
function rename89 (image) {
return image.select(['SR_B2','SR_B3','SR_B4',"SR_B5","SR_B6","SR_B7"],['blue', 'green', 'red','nir',"swir1","swir2"]);
}
//其他指数可以参照一下函数
//NDVI
function NDVI(image) {
return image.addBands(
image.normalizedDifference(["nir", "red"])
.rename("NDVI"));
}
//NDWI
function NDWI(image) {
return image.addBands(
image.normalizedDifference(["red", "swir1"])
.rename("NDWI"));
}
//NDBI
function NDBI(image) {
return image.addBands(
image.normalizedDifference(["swir2", "swir1"])
.rename("NDBI"));
}
function EVI(image) {
var evi = image.expression(
'2.5 * ((NIR - RED) / (NIR 6 * RED - 7.5 * BLUE 1))', {
'NIR': image.select('nir'),
'RED': image.select('red'),
'BLUE': image.select('blue')
});
return image.addBands(evi.rename('EVI'));
}
;
// Map the function over one year of data.
var collection1 = ee.ImageCollection('LANDSAT/LT05/C02/T1_L2').filterBounds(geometry)
.filterDate('1985-01-01', '2012-01-01')
.map(maskL457sr).map(rename57).map(NDVI).map(NDWI).map(NDBI).map(EVI);
var collection2 = ee.ImageCollection('LANDSAT/LE07/C02/T1_L2').filterBounds(geometry)
.filterDate('1985-01-01', '2012-01-01')
.map(maskL457sr).map(rename57).map(NDVI).map(NDWI).map(NDBI).map(EVI);
var collection3 = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2').filterBounds(geometry)
.filterDate('2013-01-01', '2024-01-01')
.map(maskL8sr).map(rename89).map(NDVI).map(NDWI).map(NDBI).map(EVI);
//如果需要可以进行融合
var col = collection1.merge(collection2).merge(collection3)
var image = col.first()
var scol_clip = image.clip(geometry)
function normalization(image,region,scale){
var mean_std = image.reduceRegion({
reducer: ee.Reducer.mean()
.combine(ee.Reducer.stdDev(),null, true),
geometry: region,
scale: scale,
maxPixels: 10e9,
// tileScale: 16
});
// use unit scale to normalize the pixel values
var unitScale = ee.ImageCollection.fromImages(
image.bandNames().map(function(name){
name = ee.String(name);
var band = image.select(name);
var mean=ee.Number(mean_std.get(name.cat('_mean')));
var std=ee.Number(mean_std.get(name.cat('_stdDev')));
var max=mean.add(std.multiply(3));
var min=mean.subtract(std.multiply(3));
var band1=ee.Image(min).multiply(band.lt(min)).add(ee.Image(max).multiply(band.gt(max)))
.add(band.multiply(ee.Image(1).subtract(band.lt(min)).subtract(band.gt(max))));
var result_band=band1.subtract(min).divide(max.subtract(min));
return result_band;
})).toBands().rename(image.bandNames());
return unitScale;
}
//归一化前的结果
var before_chart=ui.Chart.image.histogram(scol_clip.select(["nir","EVI"]), geometry, 1000);
print(before_chart);
//让影像进行归一化函数处理后的结果
var normal_image=normalization(scol_clip,geometry,1000)
print(normal_image)
//归一化后的结果
var after_chart=ui.Chart.image.histogram(normal_image.select(["nir","EVI"]), geometry, 1000)
print(after_chart)
print("image",image);
Map.centerObject(geometry, 3);结果
