e7e31b86b2644e0da69090bc37360c97 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 2026-07-11T21:14:20 UK GEMINI 2.3 WGS 84 ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – snow on ground (SCFG) from AATSR (2002 – 2012), version 1.0 2023-08-08T13:20:26 publication 2023-08-08T13:20:26 creation https://catalogue.ceda.ac.uk/uuid/e7e31b86b2644e0da69090bc37360c97 e7e31b86b2644e0da69090bc37360c97 NERC EDS Centre for Environmental Data Analysis 10.5285/e7e31b86b2644e0da69090bc37360c97 doi This dataset contains Daily Snow Cover Fraction (snow on ground) from AATSR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the transparency (“transmissivity”) of the forest canopy. The SCFG is given in percentage (%) per grid cell. The global SCFG product is available at 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included. The SCFG time series provides daily products for the period 2002 – 2012. The SCFG product is based on Advanced Along-Track Scanning Radiometer (AATSR) data aboard the Envisat satellite. The retrieval method of the snow_cci SCFG product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al. 2015), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud-free pixels which are clearly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include the usage of a global forest transmissivity map developed and created within snow_cci based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny 2019). The forest transmissivity map provides the local transparency of the forest canopy and is applied or estimating the fractional snow cover on the ground. 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 grid size of the SCFG product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable. The SCFG product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology. The Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFG product development and generation from AATSR data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation. There are a few days without any AATSR acquisitions in the years 2002, 2003, 2004, 2006, 2008, 2010 and 2012. Solberg, Rune Unavailable author Unavailable author Reksten, Jarle Hamar Unavailable author Unavailable author Salberg, Arnt-Børre Unavailable author Unavailable author Naegeli, Kathrin Unavailable author Unavailable author Wunderle, Stefan Unavailable author Unavailable author Schwaizer, Gabriele Unavailable author Unavailable author Nagler, Thomas 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 2002-05-20T00:00:00 2012-04-08T23:59:59 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/e7e31b86b2644e0da69090bc37360c97 CEDA Data Catalogue Page Detail and access information for the resource information http://data.ceda.ac.uk/neodc/esacci/snow/data/scfg/AATSR/v1.0 DOWNLOAD Download Data download https://climate.esa.int/en/projects/snow/snow-key-documents/ Snow CCI key documents 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 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, 13.04.2021. No further details. information http://earthenginepartners.appspot.com/science-2013-global-forest High-Resolution Global Maps of 21st-Century Forest Cover Change. No further details. information https://climate.esa.int/ ESA Climate Change Initiative website No further details. information https://science.sciencemag.org/content/342/6160/850 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 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 AATSR are based on the AATSR version 4.0 (2021) reprocessed dataset (which is conformant with SLSTR), provided by ESA. The snow_cci SCF processing chain for AATSR includes the masking of clouds, the identification of clearly snow-free areas, and the retrieval of snow cover fraction per grid cell for all remaining observed grid cells. Finally, 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. All SCFG products are prepared according to the CCI data standards. The processing chain was developed by Norsk Regnesentral (Norwegian Computing Center, NR), and the processing took place on the Fram supercomputer operated by UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education). Data were supplied for archiving at the Centre for Environmental Data Analysis (CEDA) as part of the ESA CCI Open Data Portal.