The SCFV product is based on Sea and Land Surface Temperature Radiometer (SLSTR) data on-board the Sentinel-3A and Sentinel-3B satellites.

The retrieval method of the snow_cci SCFV product from SLSTR data has been developed and improved by ENVEO (ENVironmental Earth Observation IT GmbH) to provide consistent snow cover fraction estimations with the Snow Cover Fraction on Ground (SCFG) product (https://catalogue.ceda.ac.uk/uuid/38a71d034b5c4097821de29ee3bc2498/) which is based on an enhanced version of the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from SLSTR, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm (SCDA) (Metsämäki et al., 2015). For all remaining pixels, the snow_cci SCFV retrieval method is applied, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping method is a two-step approach that first identifies pixels that are largely snow free, followed by SCFV retrieval for remaining pixels.

Spatially variable background reflectance and forest reflectance maps and a constant value for the spectral reflectance of wet snow are used for the SCFV retrieval approach. In non-forested areas, the SCFV and SCFG estimations from SLSTR data are the same.

Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. Salt lakes are masked based on manual delineation from Terra MODIS data.

The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.

SCFV products and associated layers from individual SLSTR frames from Sentinel-3A and Sentinel-3B satellites are merged into daily global SCFV products.

Each daily product contains additionally the sensor zenith angle per pixel in degree, and the acquisition time per pixel referring to the scan line time of the SLSTR frame used for the classification.

Input Description:
• Sentinel-3A SLSTR L1B and Sentinel-3B SLSTR L1B data (SL_1_RBT), NTC products, baseline collection 4.
• ESA Land Cover CCI project team; Defourny, P. (2019): ESA Land Cover Climate Change Initiative (Land_Cover_cci): Global Land Cover Maps, Version 2.0.7. Centre for Environmental Data Analysis, 14.11.2025. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c
• Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest.
• Global auxiliary layers prepared by ENVEO:
• permanent snow and ice area and water mask based on Land Cover map v2.0.7 from 2000 and salt lake mask manually mapped from MODIS data (v2.0, 2025-04-03)
• spectral reflectance layers for snow free ground (v4, 2025-05-23) and snow free forest (v4, 2025-05-23),
• Normalized Difference Snow Index (NDSI) threshold map (v5.0, 2025-03-19),
• transmissivity map based on tree canopy cover v1.4 for year 2000 (Hansen et al., 2013) and Land Cover map v2.0.7 for year 2000 (v01, 2021-10-01)

Output Description:
Daily global Snow Cover Fraction products including uncertainty estimation, sensor zenith angle and acquisition time per pixel