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Dataset

 

ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1982 - 2018), version 2.0

Update Frequency: Not Planned
Latest Data Update: 2022-01-04
Status: Completed
Online Status: ONLINE
Publication State: Citable
Publication Date: 2022-03-17
DOI Publication Date: 2022-03-17
Download Stats: last 12 months
Dataset Size: 13.4K Files | 25GB

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 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 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. 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.

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 is responsible for the SCFG product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation.

The SCFG 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 - snow on ground (SCFG) from AVHRR (1982 - 2018), version 2.0. NERC EDS Centre for Environmental Data Analysis, 17 March 2022. doi:10.5285/3f034f4a08854eb59d58e1fa92d207b6. https://dx.doi.org/10.5285/3f034f4a08854eb59d58e1fa92d207b6
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(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, 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 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:
Data are in NetCDF format

Related Documents

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

Citations: 2

The following citations have been automatically harvested from external sources associated with this resource where DOI tracking is possible. As such some citations may be missing from this list whilst others may not be accurate. Please contact the helpdesk to raise any issues to help refine these citation trackings.

Bessenbacher, V., Schumacher, D.L., Hirschi, M., Seneviratne, S.I. & Gudmundsson, L. (2023) Gap‐Filled Multivariate Observations of Global Land–Climate Interactions. Journal of Geophysical Research: Atmospheres 128. https://doi.org/10.1029/2023jd039099 https://doi.org/10.1029/2023jd039099
Naegeli, K., Franke, J., Neuhaus, C., Rietze, N., Stengel, M., Wu, X. & Wunderle, S. (2022) Revealing four decades of snow cover dynamics in the Hindu Kush Himalaya. Scientific Reports 12. https://doi.org/10.1038/s41598-022-17575-4 https://doi.org/10.1038/s41598-022-17575-4

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 v2.0 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

  • units: percent
  • standard_name: surface_snow_area_fraction
  • long_name: Snow Cover Fraction on Ground
  • var_id: scfg
  • names: surface_snow_area_fraction, Snow Cover Fraction on Ground
  • 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
  • names: Unbiased Root Mean Square Error for Snow Cover Fraction on Ground, surface_snow_area_fraction 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°