EARTH SCIENCE>CLIMATE INDICATORS>CRYOSPHERIC INDICATORS>SNOW COVER
CEDA Data Catalogue Page - Detail and access information for the resource
Product User Guide - No further details.
ESA CCI data standards - No further details.
ESA CCI Snow project website - No further details.
ESA Climate Change Initiative Website - No further details.
MetsÃ¤mÃ¤ki, 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, 96â108 - No further details.
Global Land Cover Maps, Version 2.0.7. - No further details.
Copernicus Sentinel-3 documentation on SentiWiki Home Page - No further details.
Salminen, M., Pulliainen, J., MetsÃ¤mÃ¤ki, S., Ikonen, J., HeinilÃ¤, 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.
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) (MetsÃ¤mÃ¤ki et al., 2015). For all remaining pixels, the snow_cci SCFG retrieval method is applied, using spectral bands centred at about 0.55 Âµm and 1.6 Âµm, and an emissive band centred at about 11 Âµm. 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 â 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.
CEDA
ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from SLSTR (2020 - 2022), version 1.0