3f034f4a08854eb59d58e1fa92d207b6
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
2024-03-28T12:39:41
UK GEMINI
2.3
WGS 84
ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1982 - 2018), version 2.0
2022-03-17T09:58:00
publication
2022-03-17T09:58:00
creation
http://catalogue.ceda.ac.uk/uuid/3f034f4a08854eb59d58e1fa92d207b6
3f034f4a08854eb59d58e1fa92d207b6
NERC EDS Centre for Environmental Data Analysis
10.5285/3f034f4a08854eb59d58e1fa92d207b6
doi
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 1982-2018.
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 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 0.63 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 SCFG 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 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 37 years.
Naegeli, Kathrin
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author
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author
Neuhaus, Christoph
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author
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author
Salberg, Arnt-Børre
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author
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author
Schwaizer, Gabriele
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author
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author
Weber, Helga
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author
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author
Wiesmann, Andreas
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author
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author
Wunderle, Stefan
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author
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Nagler, Thomas
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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
1982-01-01T00:00:00
2018-12-30T23:59:59
Data are in NetCDF format
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/3f034f4a08854eb59d58e1fa92d207b6
CEDA Data Catalogue Page
Detail and access information for the resource
information
http://data.ceda.ac.uk/neodc/esacci/snow/data/scfg/AVHRR_MERGED/v2.0/
DOWNLOAD
Download Data
download
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
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://climate.esa.int
ESA Climate Change Initiative website
No further details.
information
https://climate.esa.int/projects/snow/
ESA CCI Snow project website
No further details.
information
https://climate.esa.int/projects/snow/snow-key-documents/
ESA CCI Snow key documents
No further details.
information
https://climate.esa.int/media/documents/Snow_cci_D4.3_PUG_v3.1.pdf
Product User Guide
No further details.
information
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).
No further details.
information
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).
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://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
No further details.
information
https://climate.esa.int/projects/cloud/key-documents
Cloud CCI v3.0 documents
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 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).