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What you are seeing is not an anomaly at all. The bright areas are affected by specular reflections between the sun and the sensor. In the dark area, something is changing the surface of the water, causing the it to not have the specular reflection. This could be a (natural) oil slick, or a break in the wind field, causing the capillary waves to behave ...


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sentinelloader is a Python package that attempts to tackle this problem: With this utility you can specify the desired polygon, image resolution, band name and aproximate dates and it will do the best effort to find all tiles needed to satisfy your requirement. Then it will download minimal data by selecting just the needed .jp2 files inside Products, ...


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The entire S2 image archive is still being uploaded onto the google earth engine servers which explains the missing S2 data. However, the data is available to download through the Scihub portal if you need access to it https://scihub.copernicus.eu/dhus/#/home.


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http://www.gisandbeers.com/generar-imagenes-satelite-sin-nubes/ Landsat and sentinel Cloudness mosaic Cloudness mask is similar as obtained with Principe`s Script, but using median instead min().


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Sentinel-2 L2A products already include a cloud mask (in the "SCL file), produced by Sen2cor (but quite far from perfection). You do not need to apply Sen2cor again.


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I am quite new to Google Earth Engine but here is the solution I came up nonetheless. Instead of using min() function to composite image, I use percentile. This should reduce shadow captured using min() function while the overall image should not be too choppy (If in the time interval, there is a section where there is no clouds) One thing I want to improve ...


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There is a limitation with how many images you can download at a time. This is 2. But i don't believe there is a limitation overall. However, if you're using Python, try the "sentinelsat" API. Example code follows: #First, import this library (sentinelsat) from sentinelsat import SentinelAPI, read_geojson, geojson_to_wkt from datetime import date api = ...


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This method below could work for you. It assumes you want to overlap some tiles to fill the black gaps, you know the pixel value of the areas the orbit has missed, and there is no zero-value data in the imagery. Run the QGIS Raster Calculator (Menu > Raster > Raster Calculator) on each tile using a formula like the below. This will set the areas where there ...


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Merging tif images into a single GeoTiff with QGIS this should solve your question!


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