我正在使用一个名为Google Earth Engine的平台，该平台可以对云中的卫星图像进行大量分析。 我已经用Javascript编写了一个代码，该代码需要两个图像，在其中使用一个带，然后假设它会创建散点图。 我对散点图中难以理解的部分有疑问。
任何时候我运行此部分：
// Convert the band data to plot on the y-axis to arrays.
var x= ee.Array(imageNDVIcor.get('NDVI'));
var y = ee.Array(SARreproject.get('VH'));
// Make a band correlation chart.
var chart = ui.Chart.array.values(y, 0, x)
.setSeriesNames(['SAR vs NDVI'])
.setOptions({
title: 'NDVI vs SAR VH',
hAxis: {'title': 'SAR VH'},
vAxis: {'title': 'NDVI'},
pointSize: 3,
});
// Print the chart.
print(chart);
我收到以下错误：
数组：参数'values'是必需的
我不知道丢失了哪些值，或者丢失的方式和时间。我将完整的代码放在这里，对理解价值贬低的地方会有所帮助。
//STEP 1:NDVI
/**
* Function to mask clouds using the Sentinel-2 QA band
* @param {ee.Image} image Sentinel-2 image
* @return {ee.Image} cloud masked Sentinel-2 image
*/
function maskS2clouds(image) {
var qa = image.select('QA60');
// Bits 10 and 11 are clouds and cirrus, respectively.
var cloudBitMask = 1 << 10;
var cirrusBitMask = 1 << 11;
// Both flags should be set to zero, indicating clear conditions.
var mask = qa.bitwiseAnd(cloudBitMask).eq(0)
.and(qa.bitwiseAnd(cirrusBitMask).eq(0));
return image.updateMask(mask).divide(10000)
.copyProperties(image, ['system:time_start']);
}
// Map the function over one year of data and take the median.
// Load Sentinel-2 TOA reflectance data.
var dataset = ee.ImageCollection('COPERNICUS/S2')
.filterDate('2019-01-01', '2019-11-12')
// Pre-filter to get less cloudy granules.
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))
.select('B2','B3','B4','B8','QA60')
.filterBounds(geometry)
.map(maskS2clouds);
var clippedCol=dataset.map(function(im){
return im.clip(geometry);
});
// Get the number of images.
var count = dataset.size();
print('Count: ',count);
// print(clippedCol);//here I get the error messege "collection query aborted after accumulation over 5000 elements
// print(dataset,'dataset');//the same error here
//function to calculate NDVI
var addNDVI = function(image) {
var ndvi = image.normalizedDifference(['B8', 'B4'])
.rename('NDVI')
.copyProperties(image,['system:time_start']);
return image.addBands(ndvi);
};
//NDVI to the clipped image collection
var withNDVI = clippedCol.map(addNDVI).select('NDVI');
var NDVIcolor = {
min: 0,
max:1,
palette: ['FFFFFF', 'CE7E45', 'DF923D', 'F1B555', 'FCD163', '99B718', '74A901',
'66A000', '529400', '3E8601', '207401', '056201', '004C00', '023B01',
'012E01', '011D01', '011301'],
};
//Filter according to number of pixels
var ndviWithCount = withNDVI.map(function(image){
var countpixels = ee.Number(image.reduceRegion({
reducer: ee.Reducer.count(),
geometry: geometry,
crs: 'EPSG:4326',
scale: 20,
}).get('NDVI'));
return image.set('count', countpixels);
});
print(ndviWithCount, 'ndviWithCount');
var max = ndviWithCount.reduceColumns(ee.Reducer.max(), ["count"]);
print('Number of pixels max:',max.get('max'));
//filter between a range
var filterNDVI = ndviWithCount.filter(ee.Filter.rangeContains(
'count', 98258, 98258));
print('Filtered NDVI:', filterNDVI);
var listOfImages =(filterNDVI.toList(filterNDVI.size()));
var listOfNumbers = [5]
for (var i in listOfNumbers) {
var image = ee.Image(listOfImages.get(listOfNumbers[i]));
var toexport=image.visualize(NDVIcolor).addBands(image);
// do what ever you need with image
Map.addLayer(image, NDVIcolor, i);
// Export.image.toDrive({
// image: toexport.toFloat(),
// description: i,
// scale:20,
// crs:'EPSG:4326',
// maxPixels:1310361348,
// region:geometry.geometry().bounds()
// });
}
Map.centerObject(geometry);
//STEP2: SAR
// Filter the collection for the VH product from the descending track
//var geometry=MITR;
var Sentinel1 = ee.ImageCollection('COPERNICUS/S1_GRD')
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
.filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))
.filter(ee.Filter.eq('instrumentMode', 'IW'))
.select('VH')
.filterDate('2019-01-01','2019-11-12')
.filterBounds(geometry);
var clippedVH= Sentinel1.map(function(im){
return im.clip(geometry);
});
var clippedVHsize=clippedVH.size();
print('SAR Size:',clippedVHsize);
print('SAR images data:',clippedVH)
var listOfImagesSAR =(clippedVH.toList(clippedVH.size()));
var listOfNumbersSAR = [3];
for (var i in listOfNumbersSAR) {
var image = ee.Image(listOfImagesSAR.get(listOfNumbersSAR[i]));
var toexport=image.visualize({min: -30, max: 1}).addBands(image);
// do what ever you need with image
Map.addLayer(image,{min: -30, max: 1}, i);
// Export.image.toDrive({
// image: toexport.toFloat(),
// description: i,
// scale:10,
// crs:'EPSG:4326',
// maxPixels:1310361348,
// region:geometry.geometry().bounds()
// });
}
//print(ui.Chart.image.series(filterNDVI, geometry, ee.Reducer.mean(), 20));
//(ui.Chart.image.series(clippedVH, geometry, ee.Reducer.mean(), 10));
//select the images for scatter plot
//select NDVI
var imageNDVIcor=ee.Image(listOfImages.get(5));
var imageSARcor=ee.Image(listOfImagesSAR.get(3));
// Get information about the projection.
var sar1Projection = imageSARcor.projection();
print('SAR projection:', sar1Projection);
var NDVIProjection = imageNDVIcor.projection();
print('NDVI projection:', NDVIProjection);
//resample SAR image to NDVI image
var SARreproject=imageSARcor.reduceResolution({reducer: ee.Reducer.mean()}).reproject({crs: NDVIProjection});
Map.addLayer(SARreproject,{min: -30, max: 1},'Reproject SAR');
// print(imageNDVIcor)
// print(imageSARcor)
//Map.addLayer(imageNDVIcor,NDVIcolor,'NDVI select');
//Map.addLayer(imageSARcor,{min: -30, max: 1},'SAR select');
// Convert the band data to plot on the y-axis to arrays.
var x= ee.Array(imageNDVIcor.get('NDVI'));
var y = ee.Array(SARreproject.get('VH'));
// Make a band correlation chart.
var chart = ui.Chart.array.values(y, 0, x)
.setSeriesNames(['SAR vs NDVI'])
.setOptions({
title: 'NDVI vs SAR VH',
hAxis: {'title': 'SAR VH'},
vAxis: {'title': 'NDVI'},
pointSize: 3,
});
// Print the chart.
print(chart);
答案 0 :(得分：1)
不确定要完成的任务。但是，如果您要比较NDVI和VH之间的值，则可以采用这种方法。单击几何图形内的任何点，然后在控制台选项卡中绘制时间序列中该点的值。
var geometry =
ee.Geometry.Polygon(
[[[-122.56145019531249, 37.93899992220671],
[-122.56145019531249, 37.37365054197817],
[-121.80888671874999, 37.37365054197817],
[-121.80888671874999, 37.93899992220671]]], null, false);
Map.centerObject(geometry, 10);
function maskS2clouds(image) {
var qa = image.select('QA60');
var cloudBitMask = 1 << 10;
var cirrusBitMask = 1 << 11;
var mask = qa.bitwiseAnd(cloudBitMask).eq(0).and(qa.bitwiseAnd(cirrusBitMask).eq(0));
return image.updateMask(mask).divide(10000).copyProperties(image, ['system:time_start']);
}
// Sentinel 2
var sentinel2 = ee.ImageCollection('COPERNICUS/S2')
.filterDate('2019-01-01', '2019-12-31')
.filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20))
.select('B2','B3','B4','B8','QA60').filterBounds(geometry).map(maskS2clouds);
// NDVI
var addNDVI = function(image) {
var ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI').copyProperties(image,['system:time_start']);
return image.addBands(ndvi);
};
var sentinel2NDVI = sentinel2.map(addNDVI).select('NDVI');
// SAR
var sentinel1 = ee.ImageCollection('COPERNICUS/S1_GRD')
.filter(ee.Filter.listContains('transmitterReceiverPolarisation', 'VH'))
.filter(ee.Filter.eq('orbitProperties_pass', 'DESCENDING'))
.filter(ee.Filter.eq('instrumentMode', 'IW'))
.select('VH').filterDate('2019-01-01','2019-12-31').filterBounds(geometry);
var list1 = sentinel2NDVI.toList(sentinel2NDVI.size());
var list2 = sentinel1.toList(sentinel1.size());
var list = list1.cat(list2);
var combinedData = ee.ImageCollection(list);
// chart
var generateChart = function (coords) {
print('-----------------------');
var lat = coords.lat;
var lon = coords.long;
var point = ee.Geometry.Point(coords.lon, coords.lat);
var dot = ui.Map.Layer(point, {color: '000000'}, 'clicked location');
var chart = ui.Chart.image.series(combinedData, point, ee.Reducer.mean(), 30);
chart.setOptions({
title: 'VH vs NDVI',
hAxis: {'title': 'Time'},
vAxis: {'title': 'SAR VH'},
pointSize: 3,
});
print(chart);
};
Map.onClick(generateChart);
Map.style().set('cursor', 'crosshair');
此外，还可以从here获取GEE链接。