Title
Synergy of in situ and space borne observation for snow depth mapping in the Swiss Alps
Abstract
The Swiss Federal Institute for Snow and Avalanche Research in Davos (SLF) provides snow depth maps for Switzerland on a spatial resolution of 1km×1km. 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.
Year
DOI
Venue
2007
10.1016/j.jag.2006.10.001
International Journal of Applied Earth Observation and Geoinformation
Keywords
Field
DocType
NOAA-AVHRR,Snow depth,GIS,Alps
Meteorology,Geographic information system,Satellite,Multivariate interpolation,Interpolation,Remote sensing,Altitude,Image resolution,Geography,Snow,Estimator
Journal
Volume
Issue
ISSN
9
3
0303-2434
Citations 
PageRank 
References 
4
0.48
2
Authors
3
Name
Order
Citations
PageRank
Nando Foppa171.21
Andreas Stoffel240.82
Roland Meister340.48