d9df331e346f4a50b18bcf41a64b98c7 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 2026-04-27T02:21:33 UK GEMINI 2.3 WGS 84 ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1982 - 2019), version 1.0 2021-05-10T10:59:37 publication 2021-05-10T10:59:37 creation https://catalogue.ceda.ac.uk/uuid/d9df331e346f4a50b18bcf41a64b98c7 d9df331e346f4a50b18bcf41a64b98c7 NERC EDS Centre for Environmental Data Analysis 10.5285/d9df331e346f4a50b18bcf41a64b98c7 doi This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. The global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included. The SCFV time series provides daily products for the period 1982-2019. The product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. The retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 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 certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. The following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 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. The SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology. The Remote Sensing Research Group of the University of Bern is responsible for the SCFV product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation. The SCFV AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 38 years. Naegeli, Kathrin Unavailable author Unavailable author Neuhaus, Christoph Unavailable author Unavailable author Salberg, Arnt-Børre Unavailable author Unavailable author Schwaizer, Gabriele Unavailable author Unavailable author Wiesmann, Andreas Unavailable author Unavailable author Wunderle, Stefan 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 NOAA-11 NOAA-14 NOAA-16 NOAA-18 NOAA-19 NOAA-7 NOAA-9 AVHRR-2 AVHRR-3 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 1982-01-01T00:00:00 2019-06-30T23:59:59 Data are netCDF formatted. 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/d9df331e346f4a50b18bcf41a64b98c7 CEDA Data Catalogue Page Detail and access information for the resource information http://data.ceda.ac.uk/neodc/esacci/snow/data/scfv/AVHRR_MERGED/v1.0 DOWNLOAD Download Data download https://doi.org/10.7289/V5R78CFR Devasthale, A. et al. PyGac: An open-source, community-driven Python interface to preprocess nearly 40-year AVHRR Global Area Coverage (GAC) data record. Quarterly 11, 3–5 (2017). 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://science.sciencemag.org/content/342/6160/850 Hansen, M. C. et al. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available online from http://earthenginepartners.appspot.com/science-2013-global-forest 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 https://climate.esa.int/projects/cloud/?q=documentation_v3 Cloud CCI v3.0 documents No further details. information https://climate.esa.int ESA Climate Change Initiative website No further details. information https://climate.esa.int/documents/287/Snow_cci_D4.3_PUG_v2.0_r2MH0hU.pdf Product User Guide No further details. information https://climate.esa.int/projects/snow/snow-key-documents/ ESA CCI Snow key documents No further details. information https://climate.esa.int/projects/snow/ ESA CCI Snow project website 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 product based on AVHRR was developed and processed at the University of Bern in the frame of ESA CCI+ Snow project. The AVHRR baseline FCDR was pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project (Devasthale et al. 2017, Stengel et al. 2020). The final product is quality checked.