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I don't think it would make sense to get a cloud score from an imageCollection that has been reduced to a single median image. Instead, by removing the cloud mask, median reducer, and sorting step when you assign your imageCollection, you can map the getCloudScores function to all images in the collection. Note that I include a different district because ...


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You do not need to do any further atmospheric correction with Landsat OLI/TIRS Level-2 data products as they are already corrected to surface reflectance. These data will be sufficient for time-series analysis as well as work involving multiple scenes. However, make sure any older imagery (i.e. earlier than Landsat 8 OLI/TIRS) is also corrected to surface ...


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For clarification, filterBounds does not clip images, it only removes all images not intersect with the bound. If you want to clip every image in your collection, just use map and clip function as suggested in the first snippet of code suggested by Mathieu Gravey. For exporting all NDVI images (there should be 71 of them, I guess), Mathieu Gravey's answer ...


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if you want to add the mean to each object of the collection you can do: var colWithMeans=joined.map(function(ft){ ft.set('requestedMean',ee.FeatureCollection(ee.List(ft.get('matches'))).aggregate_mean('LANDSAT')); }); And if you want the global mean (mean of means) : var globalMean= colWithMeans.aggregate_mean('requestedMean')


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to clip your collection you can do something like that var clippedCol=col.map(function(im){ return im.clip(myGeometry); }); but if it's to export all the images you can simply try something like that: Export.image.toDrive({image:col.toBands(), region:myGeometry, scale:30}); Where you specify your geometry for the ...


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