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Wet and Dry Snow Detection Using Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique

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German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Strasse 20, D-82234 Wessling, Germany
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Department of Geography, Earth Observation and Modelling, Kiel University, Ludewig-Meyn-Str. 14, 24118 Kiel, Germany
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Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(8), 895; https://doi.org/10.3390/rs11080895
Received: 18 March 2019 / Revised: 4 April 2019 / Accepted: 10 April 2019 / Published: 12 April 2019
(This article belongs to the Section Environmental Remote Sensing)
Traditional studies on mapping wet snow cover extent (SCE) often feature limitations, especially in vegetated and mountainous areas. The aim of this study is to propose a new total and wet SCE mapping strategy based on freely accessible spaceborne synthetic aperture radar (SAR) data. The approach is transferable on a global scale as well as for different land cover types (including densely vegetated forest and agricultural regions), and is based on the use of backscattering coefficient, interferometric SAR coherence, and polarimetric parameters. Furthermore, four topographical factors were included in the simple tuning of random forest-based land cover type-dependent classification strategy. Results showed the classification accuracy was above 0.75, with an F-measure higher than 0.70, in all five selected regions of interest located around globally distributed mountain ranges. Whilst excluding forest-type land cover classes, the accuracy and F-measure increases to 0.80 and 0.75. In cross-location model set, the accuracy can also be maintained at 0.80 with non-forest accuracy up to 0.85. It has been found that the elevation and polarimetric parameters are the most critical factors, and that the quality of land cover information would also affect the subsequent mapping reliability. In conclusion, through comprehensive validation using optical satellite and in-situ data, our land cover-dependent total SCE mapping approach has been confirmed to be robustly applicable, and the holistic SCE map for different months were eventually derived. View Full-Text
Keywords: synthetic aperture radar; InSAR; PolSAR; backscattering; random forest; snow cover area; land use land cover; Sentinel-2; Landsat synthetic aperture radar; InSAR; PolSAR; backscattering; random forest; snow cover area; land use land cover; Sentinel-2; Landsat
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MDPI and ACS Style

Tsai, Y.-L.S.; Dietz, A.; Oppelt, N.; Kuenzer, C. Wet and Dry Snow Detection Using Sentinel-1 SAR Data for Mountainous Areas with a Machine Learning Technique. Remote Sens. 2019, 11, 895.

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