ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2020), version 2.0

This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, 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 on 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 1 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 2000 – 2020.

The SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite.

The retrieval method of the Snow_cci SCFG 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 SCFG 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 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: 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 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 usage of spatially variable background reflectance and forest reflectance maps instead of global constant values for snow free land and forest, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the global forest canopy transmissivity based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) to assure in forested areas consistency of the SCFG and the SCFV CRDP v2.0 from MODIS data ( https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach.

Improvements of the Snow_cci SCFG version 2.0 compared to the Snow_cci version 1.0 include (i) the utilisation of an updated background reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest canopy transmissivity map, 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 SCFG 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.

The SCFG 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.

ENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development.

There are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2018. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps.

Date (Publication): 2022-03-17T09:58:00

Date (Creation): 2022-03-17T09:58:00

Citation identifier: https://catalogue.ceda.ac.uk/uuid/8847a05eeda646a29da58b42bdf2a87c

Citation identifier: NERC EDS Centre for Environmental Data Analysis / 8847a05eeda646a29da58b42bdf2a87c

Citation identifier: doi / 10.5285/8847a05eeda646a29da58b42bdf2a87c

Point of contact

Organisation name	Individual name	Electronic mail address	Role
Unavailable	Nagler, Thomas	Unavailable	Author
Unavailable	Schwaizer, Gabriele	Unavailable	Author
Unavailable	Mölg, Nico	Unavailable	Author
Unavailable	Keuris, Lars	Unavailable	Author
Unavailable	Hetzenecker, Markus	Unavailable	Author
Unavailable	Metsämäki, Sari	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: 2000-02-24T00:00:00

End date: 2020-12-31T23:59:59

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/8847a05eeda646a29da58b42bdf2a87c	CEDA Data Catalogue Page

OnLine resource

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

OnLine resource

Protocol	Linkage	Name
	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

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/snow-key-documents/	ESA CCI Snow key documents

OnLine resource

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

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
	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

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://www.sciencedirect.com/science/article/abs/pii/S0034425718301950	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

OnLine resource

Protocol	Linkage	Name
	https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c	CCI Global Land Cover Maps, Version 2.0.7

OnLine resource

Protocol	Linkage	Name
	https://climate.esa.int/en/explore/esa-cci-data-standards/	ESA CCI Data Standards

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 SCFG 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 identification of certainly snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFG products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All SCFG 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.

Metadata

File identifier: 8847a05eeda646a29da58b42bdf2a87c XML

Metadata language: English

Character set: UTF8

Hierarchy level: Dataset

Date stamp: 2025-07-04T02:21:28

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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