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Synergy of in situ and space borne observation for snow depth mapping in the Swiss Alps
Institution:1. University of Bern, Department of Geography, Hallerstr. 12, CH-3012 Bern, Switzerland;2. Swiss Federal Institute for Snow and Avalanche Research, Fluelastr. 11, CH-7260 Davos Dorf, Switzerland;1. Institute of Pathology, Medical University of Graz, Graz, Austria;2. Department of Gynecology, General Hospital Leoben, Leoben, Austria;3. Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA;4. Yale Comprehensive Cancer Center, Yale School of Medicine, New Haven, CT, USA;5. Department of Pathology, Huntsman Cancer Hospital, University of Utah, Salt Lake City, UT, USA;6. MLD Pathology, Houston, TX, USA;1. Miami University, Oxford, OH 45056, United States;2. University of Iowa, Iowa City, IA 52242, United States;3. Indiana University, Bloomington, IN 47405, United States
Abstract:The Swiss Federal Institute for Snow and Avalanche Research in Davos (SLF) provides snow depth maps for Switzerland on a spatial resolution of 1 km × 1 km. These snow depth maps are derived from snow station measurements using a spatial interpolation method based on the dependency of snow depth and altitude. During a winter season the number of operating snow stations varies and the area-wide snow depth interpolation becomes increasingly difficult in spring. The objective of the study is to develop an operational and near-real time method to calculate snow depth maps using a combination of in situ snow depth measurements and the snow cover extent provided from space borne observations. The operational daily snow cover product obtained from the polar-orbiting NOAA-AVHRR satellite is used to gain an additional set of virtual snow stations to densify the in situ measurements for an improved spatial interpolation. The capacity of this method is demonstrated on selected days during winter 2005. Cross-validation tests are conducted to examine the quantitative accuracy of the synergetic interpolation method. The error estimators prove the decrease in error variance and increase of overall accuracy pointing out the high capacity of this new interpolation method that can be run in near real-time over a large horizontal domain at high horizontal resolution. A solid method for snow–no snow classification in the processing of the satellite data is essential to the quality of the snow depth maps.
Keywords:NOAA-AVHRR  Snow depth  GIS  Alps
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