Dataset
ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from SLSTR (2020 - 2022), version 1.0
Update Frequency: Not Planned
Status: Underdevelopment
Publication State: Preview
Publication Date:
THIS RECORD HAS NOT BEEN PUBLISHED YET - PREVIEW ONLY!
Abstract
This dataset contains Daily Snow Cover Fraction of viewable snow from the SLSTR satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme.
Citable as: [ PROVISIONAL ] Nagler, T.; Schwaizer, G.; Mölg, N.; Keuris, L.; Hetzenecker, M.; Metsämäki, S. (9999): ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from SLSTR (2020 - 2022), version 1.0. NERC EDS Centre for Environmental Data Analysis, date of citation. https://catalogue.ceda.ac.uk/uuid/f5dce1f7bec2447093cf460a4d3ba2c2/
Abbreviation: Not defined
Keywords: ESA, CCI, Snow, Snow Cover Fraction
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: |
Please contact the data centre for details on how to access these data.
For data use licensing information please contact: support@ceda.ac.uk |
| Data lineage: |
The snow_cci SCFV 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. The snow_cci SCF processing chain for MODIS includes the masking of clouds, the identification of certainly snow free areas, and the classification of snow cover fraction per pixel for all remaining observed pixels. Finally, permanent snow and ice areas as well as water bodies are masked in the SCFV products based on the Land Cover CCI map of the year 2000. 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. We acknowledge Norsk Regnesentral (Norwegian Computing Center, NR) for downloading the MODIS data from NASA, and UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education) for providing the processing infrastructure for the CRDP generation from MODIS. |
| Data Quality: |
The unbiased root mean square error of snow cover fraction adapted from the approach of Salminen et al. (2018) is added as an uncertainty layer in each product. The MODIS based SCFV products match the CCI data standards version 2.3, released in July 2021. For further information on the data quality, see the Snow_cci documentation..
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| File Format: |
Not defined
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Related Documents
Process overview
This dataset was generated by a combination of instruments deployed on platforms
and computations as detailed below.
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 SCFV MODIS v2.0 product. |
| Abstract | The retrieval method of the Snow_cci SCFV product from MODIS 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 developed by ENVEO. For the SCFV 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 version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 550 nm 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: 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. 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 adaptation of the retrieval method using of a spatially variable ground reflectance instead of global constant values for snow free land, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data to assure in forested areas consistency of the SCFV and the SCFG CRDP v2.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b) using the same retrieval approach. Improvements of the Snow_cci SCFV version 2.0 compared to the Snow_cci version 1.0 include (i) the utilisation of an updated ground reflectance map derived from statistical analyses of an extended MODIS time series, (ii) an update of the forest mask used for the transmissivity estimation, and (iii) an update of the constant reflectance value for wet snow based on the analysis of time series of the MODIS reflectance at 0.55 µm. 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. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable. |
| Input Description | None |
| Output Description | None |
| Software Reference | None |
| Output Description | None |
No variables found.
Coverage
Temporal Range
Start time:
2000-02-24T00:00:00
End time:
2022-12-31T23:59:59
Geographic Extent
90.0000° |
||
-180.0000° |
180.0000° |
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-90.0000° |
Related parties