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ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from SLSTR (2020 - 2022), version 1.0

Update Frequency: Not Planned
Status: Underdevelopment
Publication State: Preview
Publication Date:


This dataset contains Daily Snow Cover Fraction of snow on ground from the SLSTR satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme.

Citable as:  [ PROVISIONAL ] Nagler, T.; Schwaizer, G.; Mölg, N.; Keuris, L.; Hetzenecker, M.; Metsämäki, S. (9999): ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from SLSTR (2020 - 2022), version 1.0. NERC EDS Centre for Environmental Data Analysis, date of citation.
Abbreviation: Not defined
Keywords: ESA, CCI, Snow, Snow Cover Fraction


Previous Info:
No news update for this record
Previously used record identifiers:
No related previous identifiers.
Access rules:
Public data: access to these data is available to both registered and non-registered users.
Use of these data is covered by the following licence: When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data Quality:
The unbiased root mean square error of snow cover fraction adapted from the approach of Salminen et al. (2018) is added as an uncertainty layer in each product. The MODIS based SCFV products match the CCI data standards version 2.3, released in July 2021. For further information on the data quality, see the Snow_cci documentation..
File Format:
Not defined

Process overview

This dataset was generated by a combination of instruments deployed on platforms and computations as detailed below.

Computation Element: 1

Title ESA Snow Climate Change Initiative: Derivation of SCFV MODIS v2.0 product.
Abstract The retrieval method of the Snow_cci SCFV product from MODIS 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 developed by ENVEO. 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 version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm 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: 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 main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the adaptation of the retrieval method using of a spatially variable ground reflectance instead of global constant values for snow free land, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data to assure in forested areas consistency of the SCFV and the SCFG CRDP v2.0 from MODIS data ( using the same retrieval approach. Improvements of the Snow_cci SCFV version 2.0 compared to the Snow_cci version 1.0 include (i) the utilisation of an updated ground reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest mask used for the transmissivity estimation, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm. 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 pixel spacing of the SCFV product. Water areas are masked if more than 30 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 product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.
Input Description None
Output Description None
Software Reference None
Output Description


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