十五

le: \''Regular NDVI Time Series\'',

interpolateNulls: false,

vAxis: {title: \''NDVI\'', viewWindow: {min: 0, max: 1}},

hAxis: {title: \''\'', format: \''YYYY-MM\''},

lineWidth: 1,

pointSize: 4,

series: {

0: {color: \''#238b45\''},

},

})

print(chart);

print(\''Interpolated Collection\'', interpolatedCol)

//第五步：把插出来的数据进行SG滤波。

var oeel=require(\''users/OEEL/lib:loadAll\'');

// https://www.open-geocomputing.org/OpenEarthEngineLibrary/#.ImageCollection.SavatskyGolayFilter

//进行sg滤波处理

var maxDiffFilter = ee.Filter.maxDifference({

difference: millis,

leftField: \''system:time_start\'',

rightField: \''system:time_start\''

})

var distanceFunction = function(infromedImage, estimationImage) {

return ee.Image.constant(

ee.Number(infromedImage.get(\''system:time_start\''))

.subtract(

ee.Number(estimationImage.get(\''system:time_start\'')))

);

}

// 把SG滤波函数设置为三次函数

var order = 3;

var smoothed = oeel.ImageCollection.SavatskyGolayFilter(

interpolatedCol,

maxDiffFilter,

distanceFunction,

order)

//选择d_0_ndvi波段并重命名

var smoothed = smoothed.select([\''d_0_ndvi\''], [\''smoothed\''])

//第六步：数据可视化

//定义曲线标题

var title = \''Savitsky-Golay smoothing\'' +

\''(order = \''+ order + \'', window_size = \'' + days + \'')\''

// 把原始的和经过SG滤波的数据NDVI曲线画出来

var chart = ui.Chart.image.series({

imageCollection: ndviCol.merge(smoothed),

region: geometry,

reducer: ee.Reducer.mean(),

scale: 20

}).setOptions({

lineWidth: 1,

title: title,

interpolateNulls: true,

vAxis: {title: \''NDVI\''},

hAxis: {title: \''\'', format: \''YYYY-MMM\''},

series: {

0: {color: \''blue\'', lineWidth: 1,

l