我正在尝试将1980年代以来的所有Landsat传感器（L4-l8）合并到Google Earth Engine中，并计算NDVI指数的时间序列（在去除云层之后）
我试图找到一种解决方法，方法是通过添加来自另一个问与答（Google Earth Engine: mask clouds and map a function over an image collection of different sensors）的代码来解决L8对NIR和RED使用与L4-L7不同的频段的问题。
我得到一个图像集合，其中包含具有单个“ NDVI”波段的所有陆地卫星数据集。 但是，当我随后添加用于创建时间序列图的代码时，出现以下错误：
“错误生成图表：没有要素包含“ system：time_start”的非空值”
我想知道是否是因为收集是从不同的传感器数据集中进行的，但不确定如何解决。
有人可以帮我吗？
谢谢。
下面是代码[已编辑，可复制代码]：
//Define a region of interest - Baringo county, kenya
var Baringo2 = /* color: #98ff00 */ee.Geometry.Polygon(
[[[35.69382363692023, 1.4034169899773616],
[35.69382363692023, 1.2606333558875118],
[35.61691934004523, 1.0079975313237526],
[35.58945351973273, 0.6509798625215468],
[35.71030312910773, 0.35436075019447294],
[35.72128945723273, 0.18956774160826206],
[35.61691934004523, 0.18407460674896256],
[35.58945351973273, 0.13463632293582842],
[35.71030312910773, 0.04125265421470341],
[35.68283730879523, -0.0466379620709295],
[35.74875527754523, -0.18945988757796725],
[35.96848184004523, 0.05223897866641199],
[36.09482461348273, 0.002800509340276178],
[36.27060586348273, 0.2719645271288622],
[36.23215371504523, 0.45872822561768967],
[36.32004434004523, 0.6509798625215468],
[36.47934609785773, 0.8651943843139164],
[36.32004434004523, 0.9915205478901427],
[36.18271523848273, 1.1672705367627716],
[36.08933144942023, 1.1892385469740003],
[35.79270059004523, 1.6944479915417494]]]);
//print (Baringo2);
//define land cover categories and sample points:
//DEG, 'Open Access Grazing'
var DEG2 = /* color: #d63000 */ee.Geometry.MultiPoint(
[[35.981082916259766, 0.44974818893112445],
[35.98460465669632, 0.4510302452289453],
[35.987091064453125, 0.451808061701256],
[35.98616033792496, 0.4483427545719104],
[35.987573862075806, 0.4297931417762091],
[35.9996223449707, 0.45043481325254864],
[36.06963872909546, 0.3470539601321629],
[36.06938123703003, 0.3475260202469071],
[36.069630682468414, 0.34738386578302555],
[36.070003509521484, 0.3475903920789125],
[36.06644153594971, 0.35022933035465353],
[36.05863630771637, 0.3589468796649346],
[36.05862021446228, 0.3589039651627689],
[35.98088979721069, 0.4486537280584078],
[36.07082426548004, 0.37428880110513496],
[36.07011079788208, 0.3772229960862265],
[36.032023429870605, 0.627012774441952],
[36.03208780288696, 0.6266319235542508],
[36.03143334388733, 0.6271683332472826],
[35.98740756511688, 0.4303348413873794],
[35.997237861156464, 0.44501955275654603]]);
//GM, 'Managed Grazing'
var GM2 = /* color: #8b1062 */ee.Geometry.MultiPoint(
[[36.030564308166504, 0.6268519274914895],
[36.03069305419922, 0.6269592094299815],
[36.03039264678955, 0.6268948402671484],
[36.03148698806763, 0.6315292660507418],
[36.03124022483826, 0.6309284876929263],
[36.03038191795349, 0.630005863651221],
[36.03060722351074, 0.6335886212975799],
[35.99500894513767, 0.5747552592254358],
[36.02037191456475, 0.5758710045457109],
[36.01653099125542, 0.5782526853495346],
[36.017239094435354, 0.5785959906083284],
[36.017346383450786, 0.5729096907628695],
[36.015372277615825, 0.5705065511240786],
[36.04236602652236, 0.5423771737070896],
[36.040499209047994, 0.539373233463203],
[36.04056358337402, 0.38151398068151043],
[36.03893280029297, 0.3785528870665109]]);
//REF, Nature Conservancy
var REF2 = /* color: #98ff00 */ee.Geometry.MultiPoint(
[[36.094207763671875, 0.22436084629270164],
[36.0926628112793, 0.2264207668774221],
[36.090946197509766, 0.23088392713673156],
[36.089229583740234, 0.23672036535773863],
[36.09781265258789, 0.6451750427539986],
[36.096739768981934, 0.6450033922571318],
[36.097726821899414, 0.6443597028423275],
[36.09734058380127, 0.6434156248868509],
[36.0975980758667, 0.642686109983424],
[36.123390197753906, 0.6421282455751846],
[36.124634742736816, 0.6414845557977334]]);
//CRO, 'Crops'
var CRO2 = /* color: #5980ff */ee.Geometry.MultiPoint(
[[36.044254302978516, 0.5421843671379806],
[36.04090690481826, 0.47892844903645376],
[36.041936873079976, 0.4832198321871975],
[36.03189468252822, 0.48802617809656],
[36.02786064016982, 0.4869962471182337],
[36.02193832266494, 0.4859663159825207],
[36.014471052767476, 0.47772686127176456],
[36.02193832266494, 0.47437957996379887],
[36.018505095125875, 0.4748087186837464],
[36.00992202627822, 0.47746937815196017],
[36.00906371939345, 0.4767827564520509],
[36.00382804739638, 0.47626779013219045],
[36.003398893954, 0.4747228909418917],
[36.010007856966695, 0.4700881913046016],
[36.014471052767476, 0.47128978039605285],
[36.01962089407607, 0.4713756081803616],
[36.02485656607314, 0.47970089820257816],
[36.02511405813857, 0.4857088331740545],
[36.04159355032607, 0.4773835504432214],
[36.032752989412984, 0.47377878571117404],
[36.00447177886963, 0.42708894038807443],
[36.002798080444336, 0.4288055062751536],
[36.00245475769043, 0.42777556678899614],
[36.00425720214844, 0.4239562066570169],
[36.00627422332764, 0.42588734403892814],
[36.00099563598633, 0.4293204759663003],
[36.00425720214844, 0.4286767638469664],
[36.00425720214844, 0.4327965204722641],
[36.008548736572266, 0.4323244651387205],
[36.01219654083252, 0.42425660583700076]]);
//mask clouds for L8, and then L5-7
// Function to cloud mask Landsat 8.
var maskL8SR = function(image) {
// Bits 3 and 5 are cloud shadow and cloud, respectively.
var cloudShadowBitMask = ee.Number(2).pow(3).int();
var cloudsBitMask = ee.Number(2).pow(5).int();
// Get the QA band.
var qa = image.select('pixel_qa');
// Both flags should be set to zero, indicating clear conditions.
var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0).and(
qa.bitwiseAnd(cloudsBitMask).eq(0));
return image
// Scale the data to reflectance and temperature.
.select(['B4', 'B5'], ['Red', 'NIR']).multiply(0.0001)
.updateMask(mask);
};
// Function to cloud mask Landsats 4-7
var maskL57SR = function(image) {
var qa = image.select('pixel_qa');
// Second bit must be zero, meaning none to low cloud confidence.
var mask1 = qa.bitwiseAnd(ee.Number(2).pow(7).int()).eq(0).and(
qa.bitwiseAnd(ee.Number(2).pow(3).int()).lte(0)); // cloud shadow
// This gets rid of noise at the edge of the images.
var mask2 = image.select('B.*').gt(0).reduce('min');
return image
.select(['B3', 'B4'], ['Red', 'NIR']).multiply(0.0001)
.updateMask(mask1.and(mask2));
};
//NDVI functions
//NDVI functions for l8
var addNDVI_l8 = function(image) {
var ndvi = image.normalizedDifference(['NIR', 'Red']).rename('NDVI');
return image.addBands(ndvi);
};
//NDVI functions for l4-7
var addNDVI_l457 = function(image) {
var ndvi = image.normalizedDifference(['NIR', 'Red']).rename('NDVI');
return image.addBands(ndvi);
};
// find all data and filter them by date and add NDVI
var lst5 = ee.ImageCollection('LANDSAT/LT05/C01/T1_SR')
.filterDate('1984-10-01', '2011-10-01')
.filterBounds(Baringo2) // filter to area-of-interest;
.map(maskL57SR)
.map(addNDVI_l457).select('NDVI');
var lst7 = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR')
.filterDate('2011-10-01', '2013-04-07')
.filterBounds(Baringo2) // filter to area-of-interest;
.map(maskL57SR)
.map(addNDVI_l457).select('NDVI');
var lst8 = ee.ImageCollection('LANDSAT/LC08/C01/T1_SR')
.filterDate('2013-04-07', '2018-05-01')
.filterBounds(Baringo2) // filter to area-of-interest;
.map(maskL8SR)
.map(addNDVI_l8).select('NDVI');
var lst7_08 = ee.ImageCollection('LANDSAT/LE07/C01/T1_SR')
.filterDate('2007-12-01', '2008-02-01')
.filterBounds(Baringo2) // filter to area-of-interest;
.map(maskL57SR)
.map(addNDVI_l457).select('NDVI');
var lst7_92 = ee.ImageCollection('LANDSAT/LT04/C01/T1_SR')
.filterDate('1992-01-02', '1992-04-01')
.filterBounds(Baringo2) // filter to area-of-interest;
.map(maskL57SR)
.map(addNDVI_l457).select('NDVI');
// Combine all landsat data, 1985 through 2015
var everything = ee.ImageCollection(lst5.merge(lst7));
everything = everything.merge(lst8);
everything = everything.merge(lst7_08);
everything = everything.merge(lst7_92);
print(everything);
//NDVI time-series
// Create a time series chart for different land cover catergories.
// Define and display a FeatureCollection of three known locations.
var points = ee.FeatureCollection([
ee.Feature(DEG2, {'label': 'Open Access Grazing'}),
ee.Feature(GM2, {'label': 'Managed Grazing'}),
ee.Feature(REF2, {'label': 'Nature Conservancy'}),
ee.Feature(CRO2, {'label': 'Crops'})
]);
// Create a time series chart.
var NDVITimeSeriesAll = ui.Chart.image.seriesByRegion(
everything, points, ee.Reducer.mean(), 'NDVI', 30, 'system:time_start','label')
.setChartType('ScatterChart')
.setOptions({
title: 'Mean NDVI, plotted ~monthly, from 1984 to 2017 in 4 LC categories - Baringo Kenya',
hAxis: {title: ' Date'},
vAxis: {title: 'NDVI value (-1 to +1)', minValue: -0.0, maxValue: 0.8},
lineWidth: 1,
pointSize: 4,
series: {
0: {color: 'FF0000'}, // Unmanaged Grazing
1: {color: '00FF00'}, // Managed Grazing
2: {color: '0000FF'}, // Nature Conservancy
3: {color: '00FFFF'} // Cropland
}});
// Display.
print(NDVITimeSeriesAll);
答案 0 :(得分：0)
system:time_start属性在掩蔽阶段丢失了。该属性对于绘制时间序列是必需的，因为它用于构造时间轴。更改这两个函数以将该属性重新分配给函数返回的图像，如：
var maskL8SR = function(image) {
// Bits 3 and 5 are cloud shadow and cloud, respectively.
var cloudShadowBitMask = ee.Number(2).pow(3).int();
var cloudsBitMask = ee.Number(2).pow(5).int();
// Get the QA band.
var qa = image.select('pixel_qa');
// Both flags should be set to zero, indicating clear conditions.
var mask = qa.bitwiseAnd(cloudShadowBitMask).eq(0)
.and(qa.bitwiseAnd(cloudsBitMask).eq(0));
return image
// Scale the data to reflectance and temperature.
.select(['B4', 'B5'], ['Red', 'NIR']).multiply(0.0001)
.updateMask(mask)
.set('system:time_start', image.get('system:time_start'));
};
最后一个set()函数从原始输入图像中获取system:time_start属性，并将其分配给输出图像，确保输出ImageCollection中的所有图像都包含原始时间戳。借助两个屏蔽函数中都包含的set()函数，我可以生成用您的代码编写的时间序列图。