New Geostationary Satellite–Based Snow-Cover Algorithm

Abstract

Snow cover plays an important role in the climate system by changing the energy and mass transfer between the atmosphere and the surface. Reliable observations of the snow cover are difficult to obtain without satellites. This paper introduces a new algorithm for satellite-based snow-cover detection that is in operational use for Meteosat in the European Organisation for the Exploitation of Meteorological Satellites Satellite Application Facility on Land Surface Analysis (LSA SAF). The new version of the product is compared with the old version and the NOAA/National Environmental Satellite, Data, and Information Service Interactive Multisensor Snow and Ice Mapping System (IMS) snow-cover product. The new version of the LSA SAF snow-cover product improves the accuracy of snow detection and is comparable to the IMS product in cloud-free conditions.

The Potential of Earth Observation for the Analysis of Cold Region Land Surface Dynamics in Europe—A Review

“…For instance, (1) maximum likelihood classification was employed by Turpin et al [174] in Arctic Sweden and the Swiss Alps using Landsat TM imagery; (2) decision trees were used by Malcher et al [175] to classify snow, forest snow, fractional snow, and clouds in the Austrian Alps using MODIS and MERIS data; (3) Hüsler et al [176] developed a test-score aggregation technique for snow detection using historical NOAA-AVHRR data over the European Alps; (4) for geostationary satellite data like Meteosat Second Generation-Spinning Enhanced Visible and Infrared Imager (MSG-SEVIRI), Siljamo and Hyvärinen [177] introduced a multi-rule thresholding based snow cover algorithm and demonstrated its application in Europe; (5) Pepe et al [178] used crisp and fuzzy soft classifiers on MODIS and ASTER images to extract SCA in the Austrian Alps and the …”

Snow Cover Maps from MODIS Images at 250 m Resolution, Part 2: Validation

“…The use of a forest mask clearly shows that the differences in the two products are due to the treatment of the forested areas and, more precisely, due to the underestimation of snow detection by the EURAC SCA algorithm. As already mentioned in the description of the results, one major difference is that EURAC algorithm to maintain the resolution of 250 m does not include NDSI (500 m), which better constrains, along with NDVI, the detection of snow in forest [20]. The study of the distribution of omission and commission within forested areas clearly shows that omission is the biggest factor of discrepancy between both EURAC and NASA products, with in average 22.3% of omission error versus 4.9% of commission error.…”

Snow Extent Variability in Lesotho Derived from MODIS Data (2000–2014)

“…The snow information from the reference sources is compared with the MODIS snow map on a per pixel basis, after all of the reference data sets were resampled to the spatial resolution of the MODIS snow product. A contingency table (Table 1) was used to indicate the quality of the MODIS snow product: if both products identified the pixel as snow, it is labeled as a hit (h); when neither products indicated the pixel as snow, it is labeled as zero (z); if the MODIS product indicates the pixel as snow, but not the validation source, the pixel is marked as false (f); and if the opposite occurs, the pixel is indicated as a miss (m) [51][52][53]. Based on these measures, the hit rate (HR) and bias were calculated for each validation source (Landsat-TM and ENVISAT MERIS):…”