ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2022), version 3.0
type: Dataset
keyword: ESA
keyword: CCI
keyword: Snow
keyword: Snow Cover Fraction
keyword: orthoimagery
keyword: EARTH SCIENCE>CLIMATE INDICATORS>CRYOSPHERIC INDICATORS>SNOW COVER
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  title: Salminen, M., Pulliainen, J., MetsÃ¤mÃ¤ki, S., Ikonen, J., HeinilÃ¤, K. (2018). Determination of uncertainty characteristics for the satellite-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.
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  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, 96â108 - No further details.
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description: This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme.Snow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the masking effect of the forest canopy. The SCFG is given in percentage (%) per pixel. The global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. The SCFG time series provides daily products for the period 2000 â 2022. 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 based on the ESA GlobSnow approach described by MetsÃ¤mÃ¤ki et al. (2015) and complemented with a pre-classification module developed by ENVEO (ENVironmental Earth Observation IT GmbH). 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 version 2.0 (SCDA2.0) (MetsÃ¤mÃ¤ki et al., 2015). All cloud free pixels are then used for the snow extent mapping, 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: 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 SCFG retrieval method is applied. The main differences of the snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in MetsÃ¤mÃ¤ki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow fre…

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20000310-ESACCI-L3C_SNOW-SCFG-MODIS_TERRA-fv3.0.nc
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20000311-ESACCI-L3C_SNOW-SCFG-MODIS_TERRA-fv3.0.nc
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20000312-ESACCI-L3C_SNOW-SCFG-MODIS_TERRA-fv3.0.nc
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20000313-ESACCI-L3C_SNOW-SCFG-MODIS_TERRA-fv3.0.nc
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Landing Page in JSON media type.
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PeriodOfTime
start date: 2000-02-24T00:00:00.000Z
end date: 2022-12-31T23:59:59.999Z