More accurate and flexible cloud masking for Sentinel-2 imagesīy Justin Braaten, Kurt Schwehr, and Simon Ilyushchenko on behalf of the Earth Engine Data teamĬlouds are wonderful to look at if you’re taking a leisurely walk and staring at the sky. But if you’re studying conditions on the ground using satellite imagery, clouds tend to get in the way of your work. They can interfere with machine learning tools you’re using to identify everything from fields to forests and prevent you from easily assembling cloud-free composites. Is there a way to apply the maxcc parameter at pixel scale (rather than getting it from the tile metadata) If I set maxcc to 100., then I get all the scenes, but if possible I would like to apply it at pixel scale. an output image covers the geographical extent requested by bbox (if geometry is requested we calcualte its bbox) and the size of an output image (i.e number of pixels in x and y) is calculated based on the requested resolution, e.g. To help solve this problem when using Sentinel-2 imagery in Earth Engine, we have rolled out a new collection of cloud probability images calculated with the s2cloudless algorithm. I found the perfect explanation for this in an older forum post. By uniting Sentinel Hub’s algorithm with Google’s computing resources, we have calculated per-pixel cloud probability for the entire Sentinel-2 archive at 10 m scale each new image added to the Earth Engine catalog is also accompanied by an s2cloudless image. Explore home / Explore It is no secret that we love to observe our beautiful home planet from above. The s2cloudless dataset provides a flexible method to accurately mask cloudy pixels in Level 1C (TOA) and 2A (SR) imagery for generating cloud-free composites and running classification procedures.Ĭloud masking is an essential step in our work to map the world the s2cloudless dataset is making it easier for us to include Sentinel-2 in these efforts. We hope you’ll find it just as beneficial in your own work. The areas over which the data is available were divided into overlapping (2.4km × 2.4km) EOPatches containing: Sentinel-2 bands (R, G, B, NIR) at 10m resolution for the period between May and. The image above shows data only for the pixels inside of. Mowing has been performed on the majority of pixels, in some cases more than once. The rest should be more or less the same.Read on to learn more about s2cloudless and how you can start working with it. Pixel-level mowing shown in a counts-per-pixel style. orthorectification) applied, In addition to this: Minimal cost of a request is. In short, the one from Copernicus should be better as they use better digital elevation model than available publicly. One processing unit (PU) is defined as a request for: an output (image) size of 512 x 512 pixels, 3 collection input bands, one data sample per pixel (see sample ), an output (image) format not exceeding 16 bits per pixel, without additional processing (e.g. Is there any difference in the L2A scene generated manually by sen2cor vs downloaded L2A scene from copernicus web? If you choose to use SCL at 100 meter resolution, you might notice inaccuracies with SCL data due to JP2 wavelet transfor protocols. So when using SCL, you should use it at 10 meter resolution (20 and 60 should work correctly as well). for full Senitnel-2 resolution it would be 10 meters. With Sentinel Hub services you can define, which resolution you would like to use - e.g. This is only relevant if you are using Sentinel Hub services (which you say you do not). You should also use NEAREST upsampling/downsampling setting". One thing i didn’t understand what is the meaning of " it makes sense to use this layer only on full resolution as any interpolation based on classification codelist will not produce reasonable results. All the advantages of using Sentinel Hub, such as automatic resampling to. So, I can crop my area of interest from original L2A scene and as you mentioned i can use the “SCL” layer to compute the % of cloud present in the scene, right? We have trained s2cloudless on 10 m × 10 m Sentinel-2 pixels, but in production apply it on 160 m × 160 m pixels.
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