Dataset
ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction – viewable snow (SCFV) from AATSR (2002 – 2012), version 1.0
Abstract
This dataset contains Daily Snow Cover Fraction of viewable snow from AATSR, produced by the Snow project of the ESA Climate Change Initiative programme.
Snow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel.
The global SCFV product is available at 0.01° grid size (about 1 km) for all land areas, excluding Antarctica and Greenland ice sheet. The coastal zones of Greenland are included.
The SCFV time series provides daily products for the period 2002 – 2012.
The SCFV product is based on Advanced Along-Track Scanning Radiometer (AATSR) data aboard the Envisat satellite.
The retrieval method of the snow_cci SCFV product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. 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), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 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 clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV.
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 grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable.
The SCFV product aims to serve the needs of users working with the cryosphere and climate research and monitoring activities, including the assessment of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.
The Norwegian Computing Center (Norsk Regnesentral, NR) is responsible for the SCFV product development and generation from AATSR data. The Remote Sensing Research Group of the University of Bern supported the development. ENVEO IT GmbH developed and prepared all auxiliary data sets used for the product generation.
There are a few days without any AATSR acquisitions in the years 2002, 2003, 2004, 2006, 2008, 2010 and 2012.
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 AATSR are based on the AATSR version 4.0 (2021) reprocessed dataset (which is conformant with SLSTR), provided by ESA. The snow_cci SCF processing chain for AATSR includes the masking of clouds, the identification of clearly snow-free areas, and the retrieval of snow cover fraction per grid cell for all remaining observed grid cells. Finally, 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. All SCFV products are prepared according to the CCI data standards. The processing chain was developed by Norsk Regnesentral (Norwegian Computing Center, NR), and the processing took place on the Fram supercomputer operated by UNINETT Sigma2 AS (Sigma2, The Norwegian e-infrastructure for Research & Education). Data were supplied for archiving at the Centre for Environmental Data Analysis (CEDA) as part of the CCI Open Data Portal project. |
| Data Quality: |
The unbiased estimate of the root mean square error of the snow cover fraction is adapted from the approach of Salberg et al. (2021) and is added as an uncertainty layer in each product. The AATSR-based SCFV products are matching the CCI data standards version 2.3, released in July 2021. Salberg, A.-B., K. Luojus, C. Derksen, C. Marin, R. Solberg, L. Keuris, G. Schwaizer, T. Nagler, (2021) ESA CCI+ Snow ECV: End-to-End ECV Uncertainty Budget, version 3.0, November 2021.
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| File Format: |
NetCDF
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Related Documents
Process overview
Instrument/Platform pairings
| ENVISAT AATSR | Deployed on: Envisat |
Computation Element: 1
| Title | ESA Snow Climate Change Initiative: Derivation of SCFV AATSR v1 product. |
| Abstract | The retrieval method of the snow_cci SCFV product from AATSR data has been further developed and improved based on the ESA GlobSnow approach (Metsämäki et al. 2015) and complemented with a pre-classification module. 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), defined as SCDA2.3. All cloud-free pixels are then used for the snow extent mapping, using spectral bands centred at about 659 nm and 1.61 µm, and an emissive band centred at about 10.85 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 clearly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include adaptation of the retrieval method for mapping in forested areas the SCFV. 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 grid size of the SCFV product. Water areas are masked if more than 30% of the grid cell is classified as water, permanent snow and ice areas are masked if more than 50% is identified as such areas in the aggregated map. The product uncertainty for observed land areas is provided as unbiased root mean square error (RMSE) per grid cell in the ancillary variable. |
| Input Description | None |
| Output Description | None |
| Software Reference | None |
| Output Description | None |
- 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_east
- standard_name: longitude
- var_id: lon
- long_name: WGS84 latitude coordinates, center of pixel
- long_name: time
- standard_name: time
- var_id: time
- units: hours
Temporal Range
2002-05-20T00:00:00
2012-04-08T23:59:59
Geographic Extent
90.0000° |
||
-180.0000° |
180.0000° |
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-90.0000° |