所以我试图将一层（NDVI）剪辑到安大略省玉米田的边界。这是我到目前为止的代码，但它似乎不起作用。我不确定你是否真的可以将图层剪辑到其他图层，我知道你可以预先收集图像，但是关于如何解决这个问题的一些输入会很棒。谢谢您的帮助。
var landcover_crops = ee.ImageCollection("AAFC/ACI")
// Load a collection of Landsat TOA reflectance images.
var landsatCollection = ee.ImageCollection('LANDSAT/LC08/C01/T1_TOA');
// The dependent variable we are modeling.
var dependent = 'NDVI';
// The number of cycles per year to model.
var harmonics = 1;
// Make a list of harmonic frequencies to model.
// These also serve as band name suffixes.
var harmonicFrequencies = ee.List.sequence(1, harmonics);
// Function to get a sequence of band names for harmonic terms.
var constructBandNames = function(base, list) {
return ee.List(list).map(function(i) {
return ee.String(base).cat(ee.Number(i).int());
});
};
// Construct lists of names for the harmonic terms.
var cosNames = constructBandNames('cos_', harmonicFrequencies);
var sinNames = constructBandNames('sin_', harmonicFrequencies);
// Independent variables.
var independents = ee.List(['constant', 't'])
.cat(cosNames).cat(sinNames);
// Function to mask clouds in Landsat 8 imagery.
var maskClouds = function(image) {
var score = ee.Algorithms.Landsat.simpleCloudScore(image).select('cloud');
var mask = score.lt(10);
return image.updateMask(mask);
};
// Function to add an NDVI band, the dependent variable.
var addNDVI = function(image) {
return image
.addBands(image.normalizedDifference(['B5', 'B4'])
.rename('NDVI'))
.float();
};
// Function to add a time band.
var addDependents = function(image) {
// Compute time in fractional years since the epoch.
var years = image.date().difference('2017-01-01', 'year');
var timeRadians = ee.Image(years.multiply(2 * Math.PI)).rename('t');
var constant = ee.Image(1);
return image.addBands(constant).addBands(timeRadians.float());
};
// Function to compute the specified number of harmonics
// and add them as bands. Assumes the time band is present.
var addHarmonics = function(freqs) {
return function(image) {
// Make an image of frequencies.
var frequencies = ee.Image.constant(freqs);
// This band should represent time in radians.
var time = ee.Image(image).select('t');
// Get the cosine terms.
var cosines = time.multiply(frequencies).cos().rename(cosNames);
// Get the sin terms.
var sines = time.multiply(frequencies).sin().rename(sinNames);
return image.addBands(cosines).addBands(sines);
};
};
// Filter to the area of interest, mask clouds, add variables.
var harmonicLandsat = landsatCollection
.filterBounds(geometry2)
.map(maskClouds)
.map(addNDVI)
.map(addDependents)
.map(addHarmonics(harmonicFrequencies));
// The output of the regression reduction is a 4x1 array image.
var harmonicTrend = harmonicLandsat
.select(independents.add(dependent))
.reduce(ee.Reducer.linearRegression(independents.length(), 1));
// Turn the array image into a multi-band image of coefficients.
var harmonicTrendCoefficients = harmonicTrend.select('coefficients')
.arrayProject([0])
.arrayFlatten([independents]);
// Compute fitted values.
var fittedHarmonic = harmonicLandsat.map(function(image) {
return image.addBands(
image.select(independents)
.multiply(harmonicTrendCoefficients)
.reduce('sum')
.rename('fitted'));
});
// Plot the fitted model and the original data at the ROI.
print(ui.Chart.image.series(fittedHarmonic.select(['fitted','NDVI']),
geometry2, ee.Reducer.mean(), 100)
.setOptions({
title: 'Harmonic model: original and fitted values',
lineWidth: 1,
pointSize: 3,
}));
// Pull out the three bands we're going to visualize.
var sin = harmonicTrendCoefficients.select('sin_1');
var cos = harmonicTrendCoefficients.select('cos_1');
// Do some math to turn the first-order Fourier model into
// hue, saturation, and value in the range[0,1].
var magnitude = cos.hypot(sin).multiply(5);
var phase = sin.atan2(cos).unitScale(-Math.PI, Math.PI);
var val = harmonicLandsat.select('NDVI').reduce('mean');
// Turn the HSV data into an RGB image and add it to the map.
var seasonality = ee.Image.cat(phase, magnitude, val).hsvToRgb();
Map.centerObject(geometry2, 11);
Map.addLayer(seasonality, {}, 'Seasonality');
Map.addLayer(geometry2, {}, 'corn_ndvi');
//need to change the image collection into a single image;
var crop2017 = landcover_crops
.filter(ee.Filter.date('2017-01-01', '2017-12-31'))//select for 2017 data
.first(); //collapses the data
//find what band names are, so we can filter the data on the right one
var bandNames = crop2017.bandNames();
print('Band names: ',bandNames);
//then need to select the band of interest...here there's only one band called
landcover
var crop2017_data=crop2017.select('landcover');
//then create various masks by selecting on the land cover value
var urban_mask = crop2017_data.eq(34); //creating the mask
var urban = crop2017_data.mask(urban_mask); //masking the data
var corn_mask = crop2017_data.eq(147);
var corn = crop2017_data.mask(corn_mask);
var soy_mask = crop2017_data.eq(158);
var soy = crop2017_data.mask(soy_mask);
var hay_mask = crop2017_data.eq(122);
var hay = crop2017_data.mask(hay_mask);
var grassland_mask = crop2017_data.eq(110);
var grassland = crop2017_data.mask(grassland_mask);
//Finally, add the masks to the map to make sure they are right
Map.addLayer(urban,undefined,'Urban');
Map.addLayer(corn,undefined,'Corn');
Map.addLayer(soy,undefined,'Soy');
Map.addLayer(hay,undefined,'Hay');
Map.addLayer(grassland,undefined,'Grassland');
//Can clip the mask to show just ontario
var crop2017_ontario = crop2017.clip(ontario);
var corn_ontario = corn.clip(ontario);
var soy_ontario = soy.clip(ontario);
var urban_ontario = urban.clip(ontario);
Map.addLayer(crop2017_ontario,undefined,'All Crops Ontario');
Map.addLayer(corn_ontario,undefined,"Corn Ontario");
Map.addLayer(soy_ontario,undefined,'Soy Ontario');
Map.addLayer(urban_ontario,undefined,'Urban Ontario');
// Composite an image collection and clip it to a boundary.
// Clip to the output image to the Nevada and Arizona state boundaries.
var clipped = seasonality.clipToCollection(corn_ontario);
// Display the result.
Map.setCenter(-80.24, 43.54);
Map.addLayer(clipped, 'clipped composite');