Validation of the operational MSG-SEVIRI snow cover product over Austria

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.

MetOp/AVHRR Snow Detection Method for Meteorological Applications

“…Literature-based accuracy estimates of some operational snow extent products are presented in Table 8. Similar table has been presented earlier by Surer et al (2014). The accuracy of the present product is comparable with accuracy other products.…”

Cross-Country Assessment of H-SAF Snow Products by Sentinel-2 Imagery Validated against In-Situ Observations and Webcam Photography

“…Hence the reliability of assuming this high-resolution dataset as a reference for intercomparison purposes. The results obtained in this study reveal that S-2 data can be properly used to continuously assess these medium resolution satellite snow products, which have been commonly validated against in-situ data so far [53,56]. However, it is noteworthy to consider that under specific conditions the snow mapping derived from S-2 data can be affected by critical flaws.…”

“…Therefore, the comprehensive validation of satellite snow products is of key importance to properly assess and quantify their reliability, to identify possible errors and to provide input for further improvements. Indeed, the availability of information on the quality control of remotely-sensed data is critically needed by the scientific community, as one of the main key criteria for the selection of the most proper dataset to be effectively used, according to the final purpose.Numerous studies have addressed the validation of satellite snow products at local and global scale by assessing the accuracy of remotely-sensed observations against ground-based data, which is one of the most widely used validation procedures [42][43][44][45][46][47][48][49][50][51][52][53][54]. Lacking any available in-situ reference data, a common approach relies on a cross-sensor comparison among different satellite-derived snow products by assuming one of the analyzed datasets as the reference truth [55][56][57][58].…”

“…The interest in H-SAF snow products is focused on investigating the potential of these datasets and their suitability for hydrological purposes [56]. Over past decades, operational H-SAF snow products have been continuously validated against ground-based snow measurements [53,56]. Even though the high-resolution imagery can be reasonably used to establish reliable ground truth, a finer spatial resolution does not necessarily entail a higher accuracy of the satellite product, since its accuracy strongly depends on the retrieval algorithm used to derive snow-related information.…”