Dataset
ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from SLSTR (2020 - 2022), version 1.0
Abstract
This dataset provides daily Snow Cover Fraction Viewable from above (SCFV) derived from Sentinel-3A&B SLSTR observations, produced within the ESA Climate Change Initiative Snow project.
SCFV expresses the proportion of land area within each about 1 km x 1 km pixel that is covered by snow. SCFV represents snow viewable from above, whether on the forest canopy or on the ground in clear-cut or non-forested areas. The SCFV is given in percentage (%) per pixel. The SCFV product is available at about 1 km pixel size for global land areas except the Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. The SCFV time series spans 01 September 2020 to 31 December 2022. The time series is extended within the Copernicus Climate Change Service (C3S) for Cryosphere from 1 January 2023 onwards.
The SCFV product is based on Sea and Land Surface Temperature Radiometer (SLSTR) data on-board the Sentinel-3A and Sentinel-3B satellites. For the SCFV product generation from SLSTR, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm (SCDA) (Metsämäki et al., 2015). For all remaining pixels, the snow_cci SCFV retrieval method is applied, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach that first identifies pixels which are assessed as snow free, followed by SCFV retrieval for remaining pixels. Permanent snow/ice and water bodies are masked using the Land Cover CCI 2000 dataset, supplemented by a manually mapped salt-lake mask. Per-pixel uncertainty is provided in the ancillary variable as an unbiased Root Mean Square Error (RMSE) for all observed land pixels.
The retrieval approach used for the SLSTR based SCFV CRDP (Climate Research Data Package) v1.0 is the same as the one used for the SCFV CRDP v4.0 from Moderate resolution Imaging Spectroradiometer (MODIS) on board of the Terra satellite, covering the period 2000 – 2023 (https://catalogue.ceda.ac.uk/uuid/bc13bb02a958449aac139853c4638f32/).
The SCFV product is aimed to support cryosphere and climate research applications, including variability and trend analyses, climate modelling and studies in hydrology, meteorology, and ecology.
ENVEO leads the SCFV product development and product generation from SLSTR data, with contributions on the product development from Syke.
Details
| Previous Info: |
No news update for this record
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| Previously used record identifiers: |
No related previous identifiers.
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| 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 SCFV products from SLSTR are based on the Sentinel-3A&B SLSTR Level-1B product (SL_1_RBT), providing radiances and brightness temperatures for each pixel in a regular image grid for each view and SLSTR channel. The nadir view observations from Non-Time Critical (NTC) data products of baseline collection 4 are used as input, provided by Copernicus and ESA as frames for every 3 minutes. The snow_cci SCF processing chain for SLSTR includes the masking of clouds, the pre-classification of largely snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Permanent snow and ice areas as well as water bodies are masked in the SCFV products using the corresponding classes from the Land Cover CCI map of the year 2000 as auxiliary layers. Salt lakes are masked based on a manual delineation of such areas from Terra MODIS data. The same water, permanent snow and ice area and salt lake mask as for the Terra MODIS based SCFV CRDP v4.0 (https://catalogue.ceda.ac.uk/uuid/bc13bb02a958449aac139853c4638f32/) is used to ensure consistency between the SCFV products across the different sensors and time series. SCFV products from individual frames are merged into daily global SCFV products. All SCFV products are prepared according to the CCI data standards. An automated and a manual quality check was performed on the full time series. |
| Data Quality: |
The unbiased root mean square error of snow cover fraction adapted from the approach of Salminen et al. (2018) is added as uncertainty layer in each product. The SLSTR based SCFV products are matching the CCI data standards version 2.3, released in July 2021. For more information on data quality, see the Snow_cci documentation.
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| File Format: |
NetCDF
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Related Documents
Process overview
Instrument/Platform pairings
| Sentinel 3 Sea and Land Surface Temperature Radiometer (SLSTR) | Deployed on: Sentinel 3A |
| Sentinel 3 Sea and Land Surface Temperature Radiometer (SLSTR) | Deployed on: Sentinel 3B |
Computation Element: 1
| Title | ESA Snow Climate Change Initiative: Derivation of SCFV SLSTR v1.0 product. |
| Abstract | The SCFV product is based on Sea and Land Surface Temperature Radiometer (SLSTR) data on-board the Sentinel-3A and Sentinel-3B satellites. The retrieval method of the snow_cci SCFV product from SLSTR data has been developed and improved by ENVEO (ENVironmental Earth Observation IT GmbH) to provide consistent snow cover fraction estimations with the Snow Cover Fraction on Ground (SCFG) product (https://catalogue.ceda.ac.uk/uuid/38a71d034b5c4097821de29ee3bc2498/) which is based on an enhanced version of the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFV product generation from SLSTR, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm (SCDA) (Metsämäki et al., 2015). For all remaining pixels, the snow_cci SCFV retrieval method is applied, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping method is a two-step approach that first identifies pixels that are largely snow free, followed by SCFV retrieval for remaining pixels. Spatially variable background reflectance and forest reflectance maps and a constant value for the spectral reflectance of wet snow are used for the SCFV retrieval approach. In non-forested areas, the SCFV and SCFG estimations from SLSTR data are the same. Permanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 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. Salt lakes are masked based on manual delineation from Terra MODIS data. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable. SCFV products and associated layers from individual SLSTR frames from Sentinel-3A and Sentinel-3B satellites are merged into daily global SCFV products. Each daily product contains additionally the sensor zenith angle per pixel in degree, and the acquisition time per pixel referring to the scan line time of the SLSTR frame used for the classification. Input Description: • Sentinel-3A SLSTR L1B and Sentinel-3B SLSTR L1B data (SL_1_RBT), NTC products, baseline collection 4. • 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, 14.11.2025. https://catalogue.ceda.ac.uk/uuid/b382ebe6679d44b8b0e68ea4ef4b701c • Hansen, M. C., P. V. Potapov, R. Moore, M. Hancher, S. A. Turubanova, A. Tyukavina, D. Thau, S. V. Stehman, S. J. Goetz, T. R. Loveland, A. Kommareddy, A. Egorov, L. Chini, C. O. Justice, and J. R. G. Townshend. 2013. “High-Resolution Global Maps of 21st-Century Forest Cover Change.” Science 342 (15 November): 850–53. Data available on-line from: http://earthenginepartners.appspot.com/science-2013-global-forest. • Global auxiliary layers prepared by ENVEO: • permanent snow and ice area and water mask based on Land Cover map v2.0.7 from 2000 and salt lake mask manually mapped from MODIS data (v2.0, 2025-04-03) • spectral reflectance layers for snow free ground (v4, 2025-05-23) and snow free forest (v4, 2025-05-23), • Normalized Difference Snow Index (NDSI) threshold map (v5.0, 2025-03-19), • transmissivity map based on tree canopy cover v1.4 for year 2000 (Hansen et al., 2013) and Land Cover map v2.0.7 for year 2000 (v01, 2021-10-01) Output Description: Daily global Snow Cover Fraction products including uncertainty estimation, sensor zenith angle and acquisition time per pixel |
| 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
- long_name: Snow Cover Fraction Viewable
- var_id: scfv
- standard_name: 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
- 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
Temporal Range
2020-09-01T00:00:00
2022-12-31T23:59:59
Geographic Extent
90.0000° |
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-180.0000° |
180.0000° |
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-90.0000° |