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Dataset

 

ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1982 - 2018), version 2.0

Update Frequency: Not Planned
Status: Completed
Online Status: ONLINE
Publication State: Citable
Publication Date: 2022-03-17
DOI Publication Date: 2022-03-17
Download Stats: last 12 months

Abstract

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.

Citable as:  Naegeli, K.; Neuhaus, C.; Salberg, A.-B.; Schwaizer, G.; Weber, H.; Wiesmann, A.; Wunderle, S.; Nagler, T. (2022): ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1982 - 2018), version 2.0. NERC EDS Centre for Environmental Data Analysis, 17 March 2022. doi:10.5285/763eb87e0682446cafa8c74488dd5fb8. http://dx.doi.org/10.5285/763eb87e0682446cafa8c74488dd5fb8
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 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.

Data Quality:
The unbiased root mean square error per-pixel is added as an uncertainty layer in the product. The AVHRR based SCFV product matches the CCI data standards version 2.3, released in July 2021. For more information on data quality, see the Snow_cci documentation.
File Format:
Data are in NetCDF format

Related Documents

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

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 SCFV AVHRR v2.0 product.
Abstract 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 0.63 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 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.
Input Description None
Output Description None
Software Reference None
Output Description None
  • units: percent
  • long_name: Snow Cover Fraction Viewable
  • var_id: scfv
  • standard_name: snow_area_fraction_viewable_from_above
  • names: Snow Cover Fraction Viewable, snow_area_fraction_viewable_from_above
  • units: percent
  • long_name: Unbiased Root Mean Square Error for Snow Cover Fraction Viewable
  • var_id: scfv_unc
  • standard_name: snow_area_fraction_viewable_from_above standard_error
  • names: Unbiased Root Mean Square Error for Snow Cover Fraction Viewable, snow_area_fraction_viewable_from_above standard_error
  • var_id: lat_bnds
  • var_id: lon_bnds
  • var_id: spatial_ref

Co-ordinate Variables

  • units: degrees_north
  • standard_name: latitude
  • var_id: lat
  • long_name: WGS84 latitude coordinates, center of pixel
  • names: latitude, WGS84 latitude coordinates, center of pixel
  • units: degrees_east
  • standard_name: longitude
  • var_id: lon
  • long_name: WGS84 longitude coordinates, center of pixel
  • names: longitude, WGS84 longitude coordinates, center of pixel
  • long_name: time
  • standard_name: time
  • var_id: time
  • names: time
Coverage
Temporal Range
Start time:
1982-01-01T00:00:00
End time:
2018-12-30T23:59:59
Geographic Extent

 
90.0000°
 
-180.0000°
 
180.0000°
 
-90.0000°