Abstract
Abstract. The objective of this study is to evaluate the mapping accuracy of the MSG-SEVIRI operational snow cover product over Austria. The SEVIRI instrument is aboard the geostationary Meteosat Second Generation (MSG) satellite. The snow cover product provides 32 images per day, with a relatively low spatial resolution of 5 km over Austria. The mapping accuracy is examined at 178 stations with daily snow depth observations and compared with the daily MODIS-combined (Terra + Aqua) snow cover product for the period April 2008–June 2012. The results show that the 15 min temporal sampling allows a significant reduction of clouds in the snow cover product. The mean annual cloud coverage is less than 30% in Austria, as compared to 52% for the combined MODIS product. The mapping accuracy for cloud-free days is 89% as compared to 94% for MODIS. The largest mapping errors are found in regions with large topographical variability. The errors are noticeably larger at stations with elevations that differ greatly from those of the mean MSG-SEVIRI pixel elevations. The median of mapping accuracy for stations with absolute elevation difference less than 50 m and more than 500 m is 98.9 and 78.2%, respectively. A comparison between the MSG-SEVIRI and MODIS products indicates an 83% overall agreement. The largest disagreements are found in Alpine valleys and flatland areas in the spring and winter months, respectively.
Highlights
Monitoring and modeling of snow characteristics is important for many hydrological applications, including snowmelt runoff forecasting and water resources assessment using a range of techniques (e.g., Blöschl and Kirnbauer, 1991; Blöschl et al, 1991; Nester et al, 2012)
The analysis of mapping errors indicates that Meteosat Second Generation (MSG)
This study evaluates the snow cover mapping accuracy of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) tends to underestimate snow cover, in MSG-SEVIRI operational product
Summary
Monitoring and modeling of snow characteristics is important for many hydrological applications, including snowmelt runoff forecasting and water resources assessment using a range of techniques (e.g., Blöschl and Kirnbauer, 1991; Blöschl et al, 1991; Nester et al, 2012). Satellite imagery is an attractive alternative, as the resolution and availability does not depend much on the terrain characteristics (Parajka and Blöschl, 2008). Operational satellite products have become available that provide snow cover information at different spatial and temporal resolutions (Table 1). The numerous validation studies indicate that the satellite products have large snow mapping accuracy with respect to ground snow observations for cloud-free conditions, which varies between 69 and 94 % in the winter seasons. The main limitation of existing optical platforms operating at a daily timescale is cloud coverage, which significantly reduces the availability of snow cover information. There are different approaches for cloud reduction, including space–time filtering (e.g., Parajka and Blöschl, 2008; Gafurov and Bárdossy, 2009; Hall et al, 2010, among others), but clouds are real and the accuracy of such approaches decreases with their efficiency to reduce clouds
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