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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.

Simple

Date (Publication)
2022-03-17T16:44:03
Date (Creation)
2022-03-17T16:44:03
Identifier
https://catalogue.ceda.ac.uk/uuid/763eb87e0682446cafa8c74488dd5fb8
Identifier
NERC EDS Centre for Environmental Data Analysis / 763eb87e0682446cafa8c74488dd5fb8
Identifier
doi / 10.5285/763eb87e0682446cafa8c74488dd5fb8
Author
  Unavailable - Naegeli, Kathrin ( author )
Author
  Unavailable - Neuhaus, Christoph ( author )
Author
  Unavailable - Salberg, Arnt-Børre ( author )
Author
  Unavailable - Schwaizer, Gabriele ( author )
Author
  Unavailable - Weber, Helga ( author )
Author
  Unavailable - Wiesmann, Andreas ( author )
Author
  Unavailable - Wunderle, Stefan ( author )
Author
  Unavailable - Nagler, Thomas ( author )
Custodian
  NERC EDS Centre for Environmental Data Analysis - custodian
RAL Space , STFC Rutherford Appleton Laboratory, Harwell Campus , Didcot , OX11 0QX , United Kingdom
01235446432
Distributor
  NERC EDS Centre for Environmental Data Analysis - distributor
RAL Space , STFC Rutherford Appleton Laboratory, Harwell Campus , Didcot , OX11 0QX , United Kingdom
01235446432
pointofContact
  NERC EDS Centre for Environmental Data Analysis - point_of_contact
RAL Space , STFC Rutherford Appleton Laboratory, Harwell Campus , Didcot , OX11 0QX , United Kingdom
01235446432
Publisher
  NERC EDS Centre for Environmental Data Analysis - publisher
RAL Space , STFC Rutherford Appleton Laboratory, Harwell Campus , Didcot , OX11 0QX , United Kingdom
01235446432
Maintenance and update frequency
notPlanned Not planned
Update scope
dataset Dataset
Keywords
  • ESA
  • CCI
  • Snow
  • Snow Cover Fraction
GEMET - INSPIRE themes, version 1.0
  • orthoimagery
Access constraints
otherRestrictions Other restrictions
Other constraints
Public data: access to these data is available to both registered and non-registered users.
Use constraints
otherRestrictions 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 Grid
Metadata language
EnglishEnglish
Topic category
  • Imagery base maps earth cover
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Begin date
1982-01-01T00:00:00
End date
2018-12-30T23:59:59
Unique resource identifier
WGS 84
Distribution format
  • Data are in NetCDF format ()

Distributor
  NERC EDS Centre for Environmental Data Analysis - Data Center Contact
RAL Space , STFC Rutherford Appleton Laboratory, Harwell Campus , Didcot , OX11 0QX , United Kingdom
01235446432
OnLine resource
CEDA Data Catalogue Page

Detail and access information for the resource

OnLine resource
DOWNLOAD

Download Data

OnLine resource
Cloud CCI v3.0 documents

No further details.

OnLine resource
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.

OnLine resource
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.

OnLine resource
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.

OnLine resource
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.

OnLine resource
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.

OnLine resource
ESA Climate Change Initiative website

No further details.

OnLine resource
ESA CCI Snow project website

No further details.

OnLine resource
ESA CCI Snow key documents

No further details.

OnLine resource
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.

OnLine resource
Product User Guide

No further details.

Hierarchy level
dataset Dataset

Conformance result

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.

File identifier
763eb87e0682446cafa8c74488dd5fb8 XML
Metadata language
EnglishEnglish
Character set
8-bit variable size UCS Transfer Format, based on ISO/IEC 10646 UTF8
Hierarchy level
dataset Dataset
Date stamp
2025-05-09T02:11:25
Metadata standard name
UK GEMINI
Metadata standard version
2.3
Point of contact
  NERC EDS Centre for Environmental Data Analysis
RAL Space , STFC Rutherford Appleton Laboratory, Harwell Campus , Didcot , OX11 0QX , United Kingdom
01235446432
 
 

Overviews

Spatial extent

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Keywords

GEMET - INSPIRE themes, version 1.0
orthoimagery

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