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<https://climate.esa.int/media/documents/Snow_cci_D4.2_CRDP_v4.0.pdf>
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        dct:title        "Climate Research Data Package"@en .

<https://fedeo.ceos.org/collections/bc13bb02a958449aac139853c4638f32?httpAccept=application/vnd.iso.19139-2%2Bxml>
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<https://climate.esa.int/en/projects/snow/>
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        dct:description  "No further details."@en ;
        dct:title        "ESA CCI Snow project website"@en .

<https://climate.esa.int/media/documents/CCI_DataStandards_v2-3.pdf>
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        dct:title        "ESA CCI data standards"@en .

<https://snow-cci.enveo.at/>
        a                foaf:Document ;
        dct:description  "No further details."@en ;
        dct:title        "Snow CCI ENVEO homepage"@en .

<http://dx.doi.org/10.5067/MODIS/MOD021KM.006>
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        dct:description  "No further details."@en ;
        dct:title        "MODIS Characterization Support Team, MODIS Adaptive Processing System, 2012. Level 1 and Atmosphere Archive and Distribution System (LAADS):  MOD021KM"@en .

<https://climate.esa.int>
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<https://doi.org/10.1016/j.rse.2014.09.018>
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        dct:description  "No further details."@en ;
        dct:title        "MetsÃ¤mÃ¤ki, S., Pulliainen, J., Salminen, M., Luojus, K., Wiesmann, A., Solberg R. and Ripper, E. 2015. Introduction to GlobSnow Snow Extent products with considerations for accuracy assessment. Remote Sensing of Environment, 156, 96â108"@en .

<http://dx.doi.org/10.5067/MODIS/MOD03.006>
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        dct:description  "No further details."@en ;
        dct:title        "MODIS Characterization Support Team, MODIS Adaptive Processing System, 2012. Level 1 and Atmosphere Archive and Distribution System (LAADS):  MOD03"@en .

<https://doi.org/10.1016/j.rse.2018.04.038>
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        dct:description  "based estimation of fractional snow cover. Remote Sensing Environ., 212, 103-113. https://doi.org/10.1016/j.rse.2018.04.038 - No further details."@en ;
        dct:title        "Salminen, M., Pulliainen, J., MetsÃ¤mÃ¤ki, S., Ikonen, J., HeinilÃ¤, K. (2018). Determination of uncertainty characteristics for the satellite"@en .

<https://climate.esa.int/media/documents/Snow_cci_D4.3_PUG_v5.1.pdf>
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        dct:title        "Product User Guide"@en .

<https://gcmd.earthdata.nasa.gov/kms/concept/8ef6560e-c699-49b4-bcb3-6db68506ca22>
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        dct:description            "This dataset provides daily Snow Cover Fraction Viewable from above (SCFV) derived from Terra MODIS 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.This 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 24 February 2000 to 31 December 2023.This SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. 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 (SCDA) (MetsÃ¤mÃ¤ki et al., 2015).   For all remaining pixels, the snow_cci SCFV retrieval method is applied, using spectral bands centred at about 0.55 Âµm and 1.6 Âµm, and an emissive band centred at about 11 Âµm. 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.Compared with SCFV CRDP v3.0 (https://catalogue.ceda.ac.uk/uuid/e955813b0e1a4eb7af971f923010b4a3/), the SCFV CRDP v4.0 includes the following improvements: â¢\tmore permissive pre-classification allowing more pixels to enter the SCFV retrieval; â¢\tcorrection function applied to spectral reflectance for improved SCFV retrieval at low solar illumination conditions;â¢\tupdated spectral reflectance layers for snow free ground and snow free forest to improve SCFV retrieval;â¢\tupdated uncertainty estimation to account for the changes in the SCFV retrieval;â¢\timproved merging method for generating daily global SCFV products;â¢\tupdated salt lake mask;â¢\textended 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. SCFV products are provided for all of these days, but they contain data gaps.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 MODIS data, with contributions on the product development from Syke." ;
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