bc13bb02a958449aac139853c4638f32 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 2026-08-15T01:24:20 UK GEMINI 2.3 WGS 84 ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2023), version 4.0 2025-12-03T17:12:36 publication 2025-12-03T17:12:36 creation https://catalogue.ceda.ac.uk/uuid/bc13bb02a958449aac139853c4638f32 bc13bb02a958449aac139853c4638f32 NERC EDS Centre for Environmental Data Analysis 10.5285/bc13bb02a958449aac139853c4638f32 doi This dataset provides daily Snow Cover Fraction Viewable from above (SCFV) derived from Terra MODIS observations, produced within the ESA Climate Change Initiative Snow project. SCFV expresses the proportion of land area within each about 1 km x 1 km pixel that is covered by snow. SCFV represents snow viewable from above, whether on the forest canopy or on the ground in clear-cut or non-forested areas. The SCFV is given in percentage (%) per pixel. This SCFV 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 SCFV time series spans 24 February 2000 to 31 December 2023. This SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. For the SCFV product generation from MODIS, 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 algorithm is a two-step approach that first identifies pixels which are assessed as snow free, followed by SCFV 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. Compared with SCFV CRDP v3.0 (https://catalogue.ceda.ac.uk/uuid/e955813b0e1a4eb7af971f923010b4a3/), the SCFV CRDP v4.0 includes the following improvements: • more permissive pre-classification allowing more pixels to enter the SCFV retrieval; • correction function applied to spectral reflectance for improved SCFV retrieval at low solar illumination conditions; • updated spectral reflectance layers for snow free ground and snow free forest to improve SCFV retrieval; • updated uncertainty estimation to account for the changes in the SCFV retrieval; • improved merging method for generating daily global SCFV products; • updated salt lake mask; • extended time series, to December 2023. There are several days with no MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. In addition, on multiple days between 2000 and 2006 and in 2023, as well as on single days in 2012, 2015 and 2016, 2018, and 2020, the available MODIS data exhibit either limited spatial coverage, or corruption during data download. SCFV products are provided for all of these days, but they contain data gaps. The SCFV 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 SCFV product development and product generation from MODIS data, with contributions on the product development from Syke. Schwaizer, Gabriele Unavailable author Unavailable author Nagler, Thomas 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 Fasching, Ursula 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 2000-02-24T00:00:00 2023-12-31T23:59:59 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/bc13bb02a958449aac139853c4638f32 CEDA Data Catalogue Page Detail and access information for the resource information http://data.ceda.ac.uk/neodc/esacci/snow/data/scfv/MODIS/v4.0/ DOWNLOAD Download Data download 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 http://dx.doi.org/10.5067/MODIS/MOD03.006 MODIS Characterization Support Team, MODIS Adaptive Processing System, 2012. Level 1 and Atmosphere Archive and Distribution System (LAADS): MOD03 No further details. information https://climate.esa.int/en/projects/snow/ ESA CCI Snow project website No further details. information https://climate.esa.int/media/documents/Snow_cci_D4.2_CRDP_v4.0.pdf Climate Research Data Package No further details. information http://dx.doi.org/10.5067/MODIS/MOD021KM.006 MODIS Characterization Support Team, MODIS Adaptive Processing System, 2012. Level 1 and Atmosphere Archive and Distribution System (LAADS): MOD021KM 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 https://climate.esa.int ESA Climate Change Initiative Website No further details. information 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://snow-cci.enveo.at/ Snow CCI ENVEO homepage 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 SCFV products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA. The snow_cci SCF processing chain for MODIS 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 SCFV 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. SCFV products from individual tiles are merged into daily global SCFV products. All SCFV products are prepared according to the CCI data standards. An automated and a manual quality check was performed on the full time series. We acknowledge Norsk Regnesentral (Norwegian Computing Center, NR) for downloading the MODIS data from NASA, and UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education) for providing the processing infrastructure for the CRDP generation from MODIS.