ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1982 - 2019), version1.0

This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, 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 over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel.



The global SCFG 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 SCFG 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 SCFG 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 SCFG retrieval method is applied.



The following auxiliary data sets are used for product generation: i) 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; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.



The SCFG product is aimed to serve the needs of users working in 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 SCFG product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation.



The SCFG 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.

Simple

Date (Publication)
2021-05-10T15:22:28
Date (Creation)
2021-05-10T15:22:28
Citation identifier
https://catalogue.ceda.ac.uk/uuid/5484dc1392bc43c1ace73ba38a22ac56
Citation identifier
NERC EDS Centre for Environmental Data Analysis / 5484dc1392bc43c1ace73ba38a22ac56
Citation identifier
doi / 10.5285/5484dc1392bc43c1ace73ba38a22ac56
Point of contact
Organisation name Individual name Electronic mail address Role

Unavailable

Naegeli, Kathrin

Unavailable

 		Author

Unavailable

Neuhaus, Christoph

Unavailable

 		Author

Unavailable

Salberg, Arnt-Børre

Unavailable

 		Author

Unavailable

Schwaizer, Gabriele

Unavailable

 		Author

Unavailable

Wiesmann, Andreas

Unavailable

 		Author

Unavailable

Wunderle, Stefan

Unavailable

 		Author

Unavailable

Nagler, Thomas

Unavailable

 		Author

NERC EDS Centre for Environmental Data Analysis

support@ceda.ac.uk

Custodian

NERC EDS Centre for Environmental Data Analysis

support@ceda.ac.uk

Distributor

NERC EDS Centre for Environmental Data Analysis

support@ceda.ac.uk

pointofContact

NERC EDS Centre for Environmental Data Analysis

support@ceda.ac.uk

Publisher
Maintenance and update frequency
Not planned
Update scope
Dataset
Keywords

GEMET - INSPIRE themes, version 1.0

  • orthoimagery
Access constraints
Other restrictions
Other constraints
Public data: access to these data is available to both registered and non-registered users.
Use constraints
Other restrictions
Other constraints
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.
Spatial representation type
Grid
Language
English
Topic category
  • Imagery base maps earth cover
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Begin date
1995-08-01T00:00:00
End date
2003-06-22T23:59:59
Unique resource identifier
WGS 84
Distribution format
Name Version

Data are netCDF formatted
Distributor contact
Organisation name Individual name Electronic mail address Role

NERC EDS Centre for Environmental Data Analysis

support@ceda.ac.uk

Distributor
OnLine resource
Protocol Linkage Name
https://catalogue.ceda.ac.uk/uuid/5484dc1392bc43c1ace73ba38a22ac56

CEDA Data Catalogue Page
OnLine resource
Protocol Linkage Name
http://data.ceda.ac.uk/neodc/esacci/snow/data/scfg/AVHRR_MERGED/v1.0

DOWNLOAD
OnLine resource
Protocol Linkage Name
https://doi.org/10.5194/essd-12-41-2020

Stengel, M. et al. Cloud_cci Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) dataset version 3: 35-year climatology of global cloud and radiation properties. Earth Syst. Sci. Data 12, 41–60 (2020).

OnLine resource
Protocol Linkage Name
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).

OnLine resource
Protocol Linkage Name
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.

OnLine resource
Protocol Linkage Name
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

OnLine resource
Protocol Linkage Name
http://www.esa-cloud-cci.org/?q=documentation_v3

Cloud CCI v3.0 documents
OnLine resource
Protocol Linkage Name
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

OnLine resource
Protocol Linkage Name
https://climate.esa.int/projects/snow/snow-key-documents/

ESA CCI Snow key documents
OnLine resource
Protocol Linkage Name
https://climate.esa.int

ESA Climate Change Initiative website
OnLine resource
Protocol Linkage Name
https://climate.esa.int/documents/287/Snow_cci_D4.3_PUG_v2.0_r2MH0hU.pdf

Product User Guide
OnLine resource
Protocol Linkage Name
https://climate.esa.int/projects/snow/

ESA CCI Snow project website
Hierarchy level
Dataset

Conformance result

Title

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

Date (Publication)
2010-12-08
Statement

The snow_cci SCFG 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.



Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).

Metadata

File identifier
5484dc1392bc43c1ace73ba38a22ac56 XML
Metadata language
English
Character set
UTF8
Hierarchy level
Dataset
Date stamp
2025-06-09T02:20:35
Metadata standard name
UK GEMINI
Metadata standard version

2.3
Metadata author
Organisation name Individual name Electronic mail address Role

NERC EDS Centre for Environmental Data Analysis

support@ceda.ac.uk

Point of contact
 
 

Overviews

Spatial extent

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Keywords

GEMET - INSPIRE themes, version 1.0

orthoimagery


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