70061acca284432ca31fd8a5cbd604d0
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
2024-03-28T19:23:00
UK GEMINI
2.3
WGS 84
ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – viewable snow (SCFV) from ATSR-2 (1995 – 2003), version 1.0
2023-08-08T13:19:00
publication
2023-08-08T13:19:00
creation
http://catalogue.ceda.ac.uk/uuid/70061acca284432ca31fd8a5cbd604d0
70061acca284432ca31fd8a5cbd604d0
NERC EDS Centre for Environmental Data Analysis
10.5285/70061acca284432ca31fd8a5cbd604d0
doi
This dataset contains Daily Snow Cover Fraction of viewable snow from ATSR-2, 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 all 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 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included.
The SCFV time series provides daily products for the period 1995 – 2003.
The SCFV product is based on Along-Track Scanning Radiometer 2 (ATSR-2) data aboard the ERS-2 satellite.
The retrieval method of the snow_cci SCFV product from ATSR-2 data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. 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), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 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 clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV.
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 grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.
The SCFV product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.
The Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFV product development and generation from ATSR-2 data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.
There are a few days without any ATSR-2 acquisitions in the years 1995, 1996, 1999, 2000, 2001, 2002 and 2003.
Solberg, Rune
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author
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author
Reksten, Jarle Hamar
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author
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author
Salberg, Arnt-Børre
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author
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author
Naegeli, Kathrin
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author
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author
Wunderle, Stefan
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author
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author
Schwaizer, Gabriele
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author
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author
Nagler, Thomas
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author
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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
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
Use of these data is covered by the following licence: http://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf . 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
1995-08-01T00:00:00
2003-06-22T23:59:59
NetCDF
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
http://catalogue.ceda.ac.uk/uuid/70061acca284432ca31fd8a5cbd604d0
CEDA Data Catalogue Page
Detail and access information for the resource
information
http://data.ceda.ac.uk/neodc/esacci/snow/data/scfv/ATSR-2/v1.0
DOWNLOAD
Download Data
download
https://climate.esa.int/en/projects/snow/snow-key-documents/
Snow CCI key documents
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://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://www.science.org/doi/10.1126/science.1244693
Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53.
No further details.
information
http://earthenginepartners.appspot.com/science-2013-global-forest
High-Resolution Global Maps of 21st-Century Forest Cover Change.
No further details.
information
https://climate.esa.int
ESA Climate Change Initiative 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 products from ATSR-2 are based on the ATSR-2 version 3.0 dataset provided by ESA.
The snow_cci SCF processing chain for ATSR-2 includes the masking of clouds, the identification of clearly snow-free areas, and the retrieval of snow cover fraction per grid cell for all remaining observed grid cells. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFV products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. All SCFV products are prepared according to the CCI data standards.
The processing chain was developed by Norsk Regnesentral (Norwegian Computing Center, NR), and the processing took place on the Fram supercomputer operated by UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education).
Data were supplied for archiving at the Centre for Environmental Data Analysis (CEDA) as part of the ESA CCI Open Data Portal.