Snow Cover algorithm extracts the snow cover extent from a Sentinel-2 image and the relative cloud mask generated by Fmask algorithm, based on the Normalized Difference Snow Index (NDSI) in combination with a Digital Elevation Model (DEM), used to define a snowline elevation below which the presence of snow is excluded. It is the core module of the revised-Let-It-Snow workflow, which foresees pre-processing steps through the Sen2Cor and Fmask modules, also available on VLab, whose outputs are inputs for the snow cover module. The model requires four inputs: 1) a Sentinel-2 L2A (bottom-of-atmosphere reflectance) product, which can be provided either as a link to a zip file (e.g. a product generated by the user) or as a valid identifier of a Sentinel-2 product, 2) a cloud mask generated by Fmask, 3) a DEM - named after "DEM.tif", and, 4) optionally, a zipped shapefile of the area of interest - named after "aoi.zip". The input DEM will be pre-processed in the workflow to meet the following conditions: the same CRS of the S2 image and the same extent and resolution as the SWIR band (20 m), therefore, to decrease the processing time, it is useful to provide a DEM already re-projected and resampled, and to provide an area of interest, on which to focus the analysis. The final output is a 20 m resolution raster mask with following classification: 0 - No snow, 100 - Snow, 205 - Cloud, 254 - No Data.
Full description: A Revised Snow Cover Algorithm to Improve Discrimination between Snow and Clouds: A Case Study in Gran Paradiso National Park
VLab Module: VLab (Freely accessible from: Home > Workflow > Search "Snow cover" > Press "Create New Experiment")