Observation Instance

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    "ob_id": 40359,
    "uuid": "f5dce1f7bec2447093cf460a4d3ba2c2",
    "title": "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.\r\n\r\nSCFV 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.\r\n\r\nThe 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.\r\n\r\nThe 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/).\r\nThe 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. \r\n\r\nENVEO leads the SCFV product development and product generation from SLSTR data, with contributions on the product development from Syke.",
    "keywords": "ESA, CCI, Snow, Snow Cover Fraction",
    "publicationState": "citable",
    "dataPublishedTime": "2025-12-03T18:00:30",
    "doiPublishedTime": "2025-12-03T18:05:03",
    "updateFrequency": "notPlanned",
    "status": "completed",
    "result_field": {
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        "dataPath": "/neodc/esacci/snow/data/scfv/SLSTR/v1.0",
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        "storageStatus": "online",
        "volume": 87304149114,
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    "timePeriod": "https://api.catalogue.ceda.ac.uk/api/v2/times/12820/?format=api",
    "geographicExtent": "https://api.catalogue.ceda.ac.uk/api/v2/bboxes/2614/?format=api",
    "nonGeographicFlag": false,
    "phenomena": [
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    "dataLineage": "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.\r\n\r\nThe 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. \r\n\r\nSCFV products from individual frames are merged into daily global SCFV products.\r\n\r\nAll SCFV products are prepared according to the CCI data standards.\r\n\r\nAn automated and a manual quality check was performed on the full time series.",
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    "language": "English",
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