This website uses cookies. By continuing to use this website you are agreeing to our use of cookies. 
CEDA logo

Dataset

 

ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from ATSR-2 (dates), version 1.0

Update Frequency: Not Planned
Status: Underdevelopment
Publication State: Working
Publication Date:
Abstract

This dataset contains Daily Snow Cover Fraction (snow on ground) from ATSR-2, 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.

Citable as:  Naegeli, K.; Neuhaus, C.; Salberg, A.-B.; Schwaizer, G.; Wiesmann, A.; Wunderle, S.; Nagler, T. (9999): ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from ATSR-2 (dates), version 1.0. NERC EDS Centre for Environmental Data Analysis, date of citation. https://catalogue.ceda.ac.uk/uuid/0aeba0c203c2447b9553a78f99d3a276
Abbreviation: Not defined
Keywords: ESA, CCI, Snow, Snow Cover Fraction

Details

Previous Info:
No news update for this record
Previously used record identifiers:
No related previous identifiers.
Access rules:
Public data: access to these data is available to both registered and non-registered users.
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.
Data lineage:

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).
Data Quality:
The unbiased root mean square error per-pixel is added as an uncertainty layer in the product. The AVHRR based SCFG product matches the CCI data standards version 2.2, released in May 2020.
File Format:
Not defined

Related Documents

 ESA CCI Snow project website
 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).
 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).
 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
 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.
 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
 Cloud CCI v3.0 documents
 ESA CCI Snow key documents
 ESA Climate Change Initiative website
 Product User Guide

Process overview

This dataset was generated by a combination of instruments deployed on platforms and computations as detailed below.

Computation Element: 1

Title ESA Snow Climate Change Initiative: Derivation of SCFG AVHRR v1 product.
Abstract 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 630 nm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 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 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.
Input Description None
Output Description None
Software Reference None
Output Description None

No variables found.

Coverage
Temporal Range
Start time:
1982-01-01T00:00:00
End time:
2019-12-31T23:59:59
Geographic Extent

 
90.0000°
  
-180.0000°
  
180.0000°
 
-90.0000°
  
Related parties
Authors (7)
Publishers (1)