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I figured one way ( Still open to other and better ways ) import rasterio from rasterio.plot import show src = rasterio.open("/content/Bareilyoutput.tif") show(src) Link to Notebook -> "https://colab.research.google.com/drive/1vAyUtpvG3ERw9ktGuMbzuM07x8RfmuZG"


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If I understand your process okay, the below script will work for the "calculating Emissivity " section. It is calculating and adding the "FV" band to each image of each Landsat collection based on image specific min and max values. Code Editor script // I want to calculate the mean annual land surface temp(LST) for my case study for (1984-2019). // ...


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Try providing trainData as the regions argument to ui.Chart.image.regions() instead of training. The .sampleRegions() step is unnecessary and it may be altering your original collection.


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You can create a function calculating LST for a single year (you already have all the code for the function), invoke it for every year, finally combine the results to a single multi-band image. function calculateLST(col) { // ... } var annualLST = ee.ImageCollection( years.map(function (year) { var colForYear = col.filter( ee.Filter....


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This article explains how to compute NDVI from sentinel 1 and also compare the results with NDVI computation from Landsat 8. https://www.researchgate.net/publication/333844118_Crop_NDVI_Monitoring_Based_on_Sentinel_1


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Have a look at OpenDroneMap. Drone Mapping Software Generate maps, point clouds, 3D models and DEMs from drone, balloon or kite images. Open Source Toolkit for Processing Aerial Imagery ODM turns simple point-and-shoot camera images into two and three dimensional geographic data that can be used in combination with other geographic ...


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