8847a05eeda646a29da58b42bdf2a87c
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
2024-03-29T11:49:12
UK GEMINI
2.3
WGS 84
ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2020), version 2.0
2022-03-17T09:58:00
publication
2022-03-17T09:58:00
creation
http://catalogue.ceda.ac.uk/uuid/8847a05eeda646a29da58b42bdf2a87c
8847a05eeda646a29da58b42bdf2a87c
NERC EDS Centre for Environmental Data Analysis
10.5285/8847a05eeda646a29da58b42bdf2a87c
doi
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.
Nagler, Thomas
Unavailable
author
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author
Schwaizer, Gabriele
Unavailable
author
Unavailable
author
Mölg, Nico
Unavailable
author
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author
Keuris, Lars
Unavailable
author
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author
Hetzenecker, Markus
Unavailable
author
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author
Metsämäki, Sari
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author
Unavailable
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
2000-02-24T00:00:00
2020-12-31T23:59:59
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/8847a05eeda646a29da58b42bdf2a87c
CEDA Data Catalogue Page
Detail and access information for the resource
information
http://data.ceda.ac.uk/neodc/esacci/snow/data/scfg/MODIS/v2.0/
DOWNLOAD
Download Data
download
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
No further details.
information
https://climate.esa.int/
ESA Climate Change Initiative 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/projects/snow
ESA CCI Snow project website
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
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
No further details.
information
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://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
No further details.
information
https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c
CCI Global Land Cover Maps, Version 2.0.7
No further details.
information
https://climate.esa.int/en/explore/esa-cci-data-standards/
ESA CCI Data Standards
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
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 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.