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Seasonal Snow Cover Mapping in Alpine Areas Through Time Series of COSMO-SkyMed Images

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6 Author(s)
Notarnicola, C. ; Inst. for Appl. Remote Sensing, EURAC Res., Bolzano, Italy ; Ratti, R. ; Maddalena, V. ; Schellenberger, T.
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A time series of COSMO-SkyMed (CSK) images is exploited for detection of seasonal snow cover in alpine areas. For the first time, a complete time series of CSK images acquired during snow fall and melt periods in winter 2010-2011 is addressed to verify the snow cover mapping capabilities of X-band radar images under different conditions (from dry to wet snow). The algorithm for snow detection is based on a multitemporal approach with the concept that free water in the snowpack attenuates the X-band synthetic aperture radar signal and wet snow can be classified by comparing images acquired under wet snow and snow-free conditions. Thresholds to make this distinction are compared across all the images to check sensitivity to different winter conditions and land-use classes. The impact of variable and fixed thresholds on the retrieved snow-covered areas is assessed. Snow maps from CSK images compared with Landsat Enhanced Thematic Mapper Plus snow maps indicate a constant underestimation in the detection of snow extent, particularly during winter season, thus showing a scarce sensitivity of X-band signals to snow in dry conditions. Probability of error maps are also calculated for each CSK snow map, thus providing information on the classification error associated to each pixel labeled as snow. The analysis of the snow line variation during spring determines good time consistency in the determination of snow maps from CSK images.

Published in:

Geoscience and Remote Sensing Letters, IEEE  (Volume:10 ,  Issue: 4 )