Dataset
ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2023), version 4.0
Abstract
This dataset provides daily Snow Cover Fraction on Ground (SCFG) derived from Terra MODIS observations, produced within the ESA Climate Change Initiative Snow project.
SCFG expresses the proportion of land area within each about 1 km x 1 km pixel that is covered by snow. In forested areas, the masking effect of the forest canopy is corrected to estimate the SCFG. The SCFG is given in percentage (%) per pixel.
The SCFG 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 SCFG time series spans 24 February 2000 to 31 December 2023.
The SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. For the SCFG product generation from MODIS, 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 SCFG 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 SCFG 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.
Compared with SCFG CRDP v3.0 (https://catalogue.ceda.ac.uk/uuid/80567d38de3f4b038ee6e6e53ed1af8a/), the SCFG CRDP v4.0 includes the following improvements:
• more permissive pre-classification allowing more pixels to enter the SCFG retrieval;
• correction function applied to spectral reflectance for improved SCFG retrieval at low solar illumination conditions;
• updated spectral reflectance layers for snow free ground and snow free forest to improve SCFG retrieval;
• updated uncertainty estimation to account for the changes in the SCFG retrieval;
• improved merging method for generating daily global SCFG products;
• updated salt lake mask;
• extended time series, to December 2023.
There are several days with no MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. In addition, on multiple days between 2000 and 2006 and in 2023, as well as on single days in 2012, 2015 and 2016, 2018, and 2020, the available MODIS data exhibit either limited spatial coverage, or corruption during data download. SCFG products are provided for all of these days, but they contain data gaps.
The SCFG 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 SCFG product development and product generation from MODIS 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 SCFG products from MODIS are based on the MODIS/Terra Calibrated Radiances 5-Min L1B Swath 1km (MOD021KM) and the MODIS/Terra Geolocation Fields 5-Min L1A Swath 1km (MOD03) Collection 6.1 data sets, provided by NASA. |
| 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 MODIS based SCFG 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
| Moderate Resolution Imaging Spectroradiometer (MODIS) | Deployed on: Terra Satellite, part of the Earth Observation System Morning Constellation (EOS-AM) |
Computation Element: 1
| Title | ESA Snow Climate Change Initiative: Derivation of SCFG MODIS v4.0 product. |
| Abstract | The SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. The retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved by ENVEO (ENVironmental Earth Observation IT GmbH) based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO. For the SCFG product generation from MODIS, 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 SCFG 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 SCFG retrieval for remaining pixels. The main differences of the snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable snow free ground reflectance and snow free forest reflectance maps instead of global constant values, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the usage of a global forest canopy transmissivity based on tree canopy cover of the year 2000 from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) of the year 2000. The retrieval approach ensures consistency between the SCFG CRDP v4.0 and the Snow Cover Fraction Viewable from above (SCFV) CRDP 4.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ bc13bb02a958449aac139853c4638f32). In non-forested areas, the SCFG and SCFV estimations from MODIS 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 SCFG 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. SCFG products and associated layers from individual MODIS tiles are merged into daily global SCFG 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 MODIS granule used for the classification. Input description: • Terra MODIS Collection 6.1 MOD021KM (Level 1B Calibrated Radiances - 1km; DOI: 10.5067/MODIS/MOD021KM.061) and MOD03 (Geolocation - 1km; DOI: 10.5067/MODIS/MOD03.061) products • 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, 2025-05-24) and snow free forest (v4, 2025-05-24), • 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: |
| 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
Temporal Range
2000-02-24T00:00:00
2023-12-31T23:59:59
Geographic Extent
90.0000° |
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-180.0000° |
180.0000° |
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-90.0000° |