ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1982 - 2018), version 2.0

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 1982-2018.

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- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.630 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-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. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale.

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 37 years.

Date (Publication): 2022-03-17T16:44:03

Date (Creation): 2022-03-17T16:44:03

Citation identifier: https://catalogue.ceda.ac.uk/uuid/763eb87e0682446cafa8c74488dd5fb8

Citation identifier: NERC EDS Centre for Environmental Data Analysis / 763eb87e0682446cafa8c74488dd5fb8

Citation identifier: doi / 10.5285/763eb87e0682446cafa8c74488dd5fb8

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	Weber, Helga	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

ESA
CCI
Snow
Snow Cover Fraction

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

Begin date: 1982-01-01T00:00:00

End date: 2018-12-30T23:59:59

Unique resource identifier: WGS 84

Distribution format

Name	Version
Data are in NetCDF format

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/763eb87e0682446cafa8c74488dd5fb8	CEDA Data Catalogue Page

OnLine resource

Protocol	Linkage	Name
	http://data.ceda.ac.uk/neodc/esacci/snow/data/scfv/AVHRR_MERGED/v2.0/	DOWNLOAD

OnLine resource

Protocol	Linkage	Name
	https://climate.esa.int/projects/cloud/?q=documentation_v3	Cloud CCI v3.0 documents

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
	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://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://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://climate.esa.int	ESA Climate Change Initiative website

OnLine resource

Protocol	Linkage	Name
	https://climate.esa.int/projects/snow/	ESA CCI Snow project website

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://doi.org/10.5194/tc-15-4261-2021	Wu, Xiaodan; Naegeli, Kathrin; Premier, Valentina; Marin, Carlo; Ma, Dujuan; Wang, Jingping; Wunderle, Stefan (2021). Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas. The Cryosphere, 15(9), pp. 4261-4279. Copernicus Publications

OnLine resource

Protocol	Linkage	Name
	https://climate.esa.int/media/documents/Snow_cci_D4.3_PUG_v3.1.pdf	Product User Guide

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 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.

Metadata

File identifier: 763eb87e0682446cafa8c74488dd5fb8 XML

Metadata language: English

Character set: UTF8

Hierarchy level: Dataset

Date stamp: 2026-03-01T03:21:41

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

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