38a71d034b5c4097821de29ee3bc2498 English 8-bit variable size UCS Transfer Format, based on ISO/IEC 10646 dataset NERC EDS Centre for Environmental Data Analysis 01235446432 RAL Space STFC Rutherford Appleton Laboratory, Harwell Campus Didcot OX11 0QX United Kingdom support@ceda.ac.uk pointOfContact 2026-10-17T18:34:42 UK GEMINI 2.3 WGS 84 ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from SLSTR (2020 - 2022), version 1.0 2025-12-03T18:05:29 publication 2025-12-03T18:05:29 creation https://catalogue.ceda.ac.uk/uuid/38a71d034b5c4097821de29ee3bc2498 38a71d034b5c4097821de29ee3bc2498 NERC EDS Centre for Environmental Data Analysis 10.5285/38a71d034b5c4097821de29ee3bc2498 doi This dataset provides daily Snow Cover Fraction on Ground (SCFG) derived from Sentinel-3A&B SLSTR observations, produced within the ESA Climate Change Initiative Snow project. SCFG expresses the proportion of land area within each about 1 km x 1 km pixel that is covered by snow. In forested areas, the masking effect of the forest canopy is corrected to estimate the SCFG. The SCFG is given in percentage (%) per pixel. The SCFG product is available at about 1 km pixel size for global land areas except the Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. The SCFG time series spans 01 September 2020 to 31 December 2022. The time series is extended within the Copernicus Climate Change Service (C3S) for Cryosphere from 1 January 2023 onwards. The SCFG product is based on Sea and Land Surface Temperature Radiometer (SLSTR) data on-board the Sentinel-3A and Sentinel-3B satellites. For the SCFG 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 SCFG 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 algorithm is a two-step approach that first identifies pixels which are assessed as snow free, followed by SCFG retrieval for remaining pixels. Permanent snow/ice and water bodies are masked using the Land Cover CCI 2000 dataset, supplemented by a manually mapped salt-lake mask. Per-pixel uncertainty is provided in the ancillary variable as an unbiased Root Mean Square Error (RMSE) for all observed land pixels. The retrieval approach used for the SLSTR based SCFG CRDP (Climate Research Data Package) v1.0 is the same as the one used for the SCFG CRDP v4.0 from Moderate resolution Imaging Spectroradiometer (MODIS) on board of the Terra satellite, covering the period 2000 – 2023 (https://catalogue.ceda.ac.uk/uuid/ 375ffdb8f0a445e380b4b9548655f5f9). The SCFG product is aimed to support cryosphere and climate research applications, including variability and trend analyses, climate modelling and studies in hydrology, meteorology, and ecology. ENVEO leads the SCFG product development and product generation from SLSTR data, with contributions on the product development from Syke. Schwaizer, Gabriele Unavailable author Unavailable author Nagler, Thomas Unavailable author Unavailable author Fasching, Ursula Unavailable author Unavailable author Hetzenecker, Markus Unavailable author Unavailable author Heinrich, Maria Unavailable author Unavailable author Mölg, Nico Unavailable author Unavailable author Nemec, Johanna Unavailable author Unavailable author Haslinger, Beat Unavailable author Unavailable author Scolamiero, Andrea Unavailable author Unavailable author Malcher, Petra Unavailable author Unavailable author Metsämäki, Sari Unavailable author Unavailable author NERC EDS Centre for Environmental Data Analysis custodian 01235446432 RAL Space STFC Rutherford Appleton Laboratory, Harwell Campus Didcot OX11 0QX United Kingdom support@ceda.ac.uk custodian NERC EDS Centre for Environmental Data Analysis distributor 01235446432 RAL Space STFC Rutherford Appleton Laboratory, Harwell Campus Didcot OX11 0QX United Kingdom support@ceda.ac.uk distributor NERC EDS Centre for Environmental Data Analysis point_of_contact 01235446432 RAL Space STFC Rutherford Appleton Laboratory, Harwell Campus Didcot OX11 0QX United Kingdom support@ceda.ac.uk pointOfContact NERC EDS Centre for Environmental Data Analysis publisher 01235446432 RAL Space STFC Rutherford Appleton Laboratory, Harwell Campus Didcot OX11 0QX United Kingdom support@ceda.ac.uk publisher notPlanned dataset ESA CCI Snow Snow Cover Fraction orthoimagery theme GEMET - INSPIRE themes, version 1.0 2008-06-01 publication otherRestrictions Public data: access to these data is available to both registered and non-registered users. otherRestrictions Under the following licence https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf, appropriate use of these data may fall under any use. This message is intended as guidance, always read the full licence. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record. grid English imageryBaseMapsEarthCover -180.0 180.0 -90.0 90.0 2020-09-01T00:00:00 2022-12-31T23:59:59 NetCDF NERC EDS Centre for Environmental Data Analysis Data Center Contact 01235446432 RAL Space STFC Rutherford Appleton Laboratory, Harwell Campus Didcot OX11 0QX United Kingdom support@ceda.ac.uk distributor https://catalogue.ceda.ac.uk/uuid/38a71d034b5c4097821de29ee3bc2498 CEDA Data Catalogue Page Detail and access information for the resource information http://data.ceda.ac.uk/neodc/esacci/snow/data/scfg/SLSTR/v1.0 DOWNLOAD Download Data download https://climate.esa.int/media/documents/Snow_cci_D4.3_PUG_v5.1.pdf Product User Guide No further details. information https://climate.esa.int/media/documents/CCI_DataStandards_v2-3.pdf ESA CCI data standards No further details. information https://climate.esa.int/en/projects/snow ESA CCI Snow project website No further details. information https://climate.esa.int ESA Climate Change Initiative Website No further details. information https://doi.org/10.1016/j.rse.2014.09.018 Metsämäki, S., Pulliainen, J., Salminen, M., Luojus, K., Wiesmann, A., Solberg R. and Ripper, E. 2015. Introduction to GlobSnow Snow Extent products with considerations for accuracy assessment. Remote Sensing of Environment, 156, 96–108 No further details. information https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c Global Land Cover Maps, Version 2.0.7. No further details. information https://sentiwiki.copernicus.eu/web/sentinel-3 Copernicus Sentinel-3 documentation on SentiWiki Home Page No further details. information https://doi.org/10.1016/j.rse.2018.04.038 Salminen, M., Pulliainen, J., Metsämäki, S., Ikonen, J., Heinilä, K. (2018). Determination of uncertainty characteristics for the satellite-based estimation of fractional snow cover. Remote Sensing Environ., 212, 103-113. https://doi.org/10.1016/j.rse.2018.04.038 No further details. information dataset Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 The snow_cci SCFG products from SLSTR are based on the Sentinel-3A&B SLSTR Level-1B product (SL_1_RBT), providing radiances and brightness temperatures for each pixel in a regular image grid for each view and SLSTR channel. The nadir view observations from Non-Time Critical (NTC) data products of baseline collection 4 are used as input, provided by Copernicus and ESA as frames for every 3 minutes. The snow_cci SCF processing chain for SLSTR includes the masking of clouds, the pre-classification of largely snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Permanent snow and ice areas as well as water bodies are masked in the SCFG products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. Salt lakes are masked based on a manual delineation of such areas from Terra MODIS data. The same water, permanent snow and ice area and salt lake mask as for the Terra MODIS based SCFG CRDP v4.0 (https://catalogue.ceda.ac.uk/uuid/375ffdb8f0a445e380b4b9548655f5f9/) is used to ensure consistency between the SCFG products across the different sensors and time series. SCFG products from individual frames are merged into daily global SCFG products. All SCFG products are prepared according to the CCI data standards. An automated and a manual quality check was performed on the full time series.