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Here is a histogram equalization solution that is working better than linear stretch, but some images are still a little washed out, not as saturated as I'd like. I would love to see if anyone else has a solution. # Get RGB bands from Landsat image with rasterio.open(landsat_path, 'r') as f: red, green, blue = f.read(4), f.read(3), f.read(2) red[red ...


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try using remap function to convert the class values from string to integers


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You can create your 1984-2018 images and classify each of them using the classifier you trained on the 2019 data: var classified1984 = image1984.select(bands).classify(classifier) If there's a lot of training data and/or there goes a lot of processing into your images, you might be better off exporting them as assets before training/classifying. If you ...


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If you want to use the pixel_qa band, you could allow for low-confidence cloud pixels. Of course, it will not be as efficient at at masking real clouds. function maskL8sr(image) { // Bits 3 and 5 are cloud shadow and cloud, respectively. var cloudShadowBitMask = (1 << 3); var cloudsBitMask = (1 << 5); // Get the pixel QA band. var qa =...


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You will have to export each image in the collection separately. You can trigger the exports with your script, but there will be a lot of manual "Run" button clicks. collectYear .aggregate_array('system:index') .evaluate(function (indexes) { indexes.forEach(function (index) { var image = collectYear .filterMetadata('system:index', '...


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The line: var val = ee.Number(data.get(['ndvi'])) needs to be changed to var val = ee.Number(data.get('NDVI')) since you have renamed 'ndvi' to 'NDVI' in your code.


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The imagery in your collection have originally had different projections, because it is built from Landsat scenes of different WRS paths/rows. Subsequently, you will need to provide a scale in the arguments of the ui.Chart function to tell GEE specifically how you want to aggregate pixels within the geometry. Use for example a scale of 30 (in meters), which ...


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The main problem was for the joining part of script, I added the NDVI,FV,EM, an the calculated LST to the main collection and used it for other calculation without using ee.list and joining.


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The problem is solved by adding (to float ) and filtering the null data.


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Why not making the image collection in one go by mapping over the list of years. THen you can also immediately calculate the RUE as you have both images for a year. var byYear = ee.ImageCollection.fromImages( years.map(function (y) { var start = ee.Date.fromYMD(y, 07, 1); var stop = start.advance(1, 'year'); var sumVeg = smoothed.filterDate(start, ...


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