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Dataset

 

ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1979 - 2023), version 4.0

Update Frequency: Not Planned
Status: Ongoing
Online Status: ONLINE
Publication State: Citable
Publication Date: 2025-09-04
DOI Publication Date: 2025-09-04
Download Stats: last 12 months
Dataset Size: 37.06K Files | 530GB

Abstract

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 1979-2023.

The product V4.0 is based on EUMETSAT Fundamental Data Record (FDR) medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud 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 using dynamic reference reflectance values (snow, forest, ground) temporally and spatially adapted to consider angle dependencies (sun, view). 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. RMSE is retrieved from a statistical model and added as pixel-wise information.

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, in cooperation with Gamma Remote Sensing is responsible for the SCFG product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation.

The SCFG AVHRR product comprises a few data gaps in 1979 – 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March – 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years.

Citable as:  Xiao, X.; Naegeli, K.; Neuhaus, C.; Salberg, A.-B.; Schwaizer, G.; Wiesmann, A.; Wunderle, S.; Nagler, T. (2025): ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1979 - 2023), version 4.0. NERC EDS Centre for Environmental Data Analysis, 04 September 2025. doi:10.5285/80d96e3a14854420b6f742d70877c431. https://dx.doi.org/10.5285/80d96e3a14854420b6f742d70877c431

Abbreviation: Not defined
Keywords: ESA, CCI, Snow, Snow Cover Fraction

Details

Previous Info:

2025-09-08 One of the archived files (20170720-ESACCI-L3C_SNOW-SCFG-AVHRR_METOPB-fv4.0.nc) had become corrupted, and was replaced on 08/09… Show More 2025-09-08 One of the archived files (20170720-ESACCI-L3C_SNOW-SCFG-AVHRR_METOPB-fv4.0.nc) had become corrupted, and was replaced on 08/09/2025. Show Less

2025-09-08 One of the archived files (20170720-ESACCI-L3C_SNOW-SCFG-AVHRR_METOPB-fv4.0.nc) had become corrupted, and was replaced on 08/09… Show More 2025-09-08 One of the archived files (20170720-ESACCI-L3C_SNOW-SCFG-AVHRR_METOPB-fv4.0.nc) had become corrupted, and was replaced on 08/09/2025. Show Less

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(s):
https://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).
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 uncertainty layer in the product. The AVHRR based SCFG 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:
NetCDF

Related Documents

 ESA Climate Change Initiative website
 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).
 ESA CCI Snow key 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
 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
 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
 Snow CCI ENVEO homepage
 ESA CCI Data Standards
 Product User Guide
 EUMETSAT, 2023a. AVHRR Fundamental Data Record - Release 1 - Multimission, European Organisation for the Exploitation of Meteorological Satellites [data set].
 EUMETSAT, 2023b. PyGAC Fundamental Data Record Algorithm Theoretical Basis Document.
 Karlsson, K.-G., Stengel, M., Meirink, J.F., Riihelä, A., Trentmann, J., Akkermans, T., Stein, D., Devasthale, A., Eliasson, S., Johansson, E., Håkansson, N., Solodovnik, I., Benas, N., Clerbaux, N., Selbach, N., Schröder, M., Hollmann, R., 2023. CLARA-A3: The third edition of the AVHRR-based CM SAF climate data record on clouds, radiation and surface albedo covering the period 1979 to 2023. Earth Syst. Sci. Data 15, 4901–4926.
 Climate Research Data Package v4.1

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 v4.0 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. RMSE is retrieved from a statistical model and added as pixel-wise information.
Input Description None
Output Description None
Software Reference None
Output Description

None

  • units: degrees
  • standard_name: sensor_zenith_angle
  • var_id: satzen
  • long_name: Sensor Zenith Angle
  • units: percent
  • standard_name: surface_snow_area_fraction
  • long_name: Snow Cover Fraction on Ground
  • var_id: scfg
  • units: percent
  • long_name: Unbiased Root Mean Square Error for Snow Cover Fraction on Ground
  • standard_name: surface_snow_area_fraction standard_error
  • var_id: scfg_unc
  • 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
  • units: degrees_east
  • standard_name: longitude
  • var_id: lon
  • long_name: WGS84 longitude coordinates, center of pixel
  • standard_name: time
  • var_id: scanline_time
  • long_name: scanline time as fractional hours of the day
  • units: h
  • long_name: time
  • standard_name: time
  • var_id: time
  • units: hours
Coverage
Temporal Range
Start time:
1979-01-01T00:00:00
End time:
2023-12-31T23:59:59
Geographic Extent

 
90.0000°
 
-180.0000°
 
180.0000°
 
-90.0000°