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Open AccessReview

Remote Sensing of Snow Cover Using Spaceborne SAR: A Review

1
German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), Muenchener Strasse 20, D-82234 Wessling, Germany
2
Department of Geography, Earth Observation and Modelling, Kiel University, Ludewig-Meyn-Str. 14, 24118 Kiel, Germany
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(12), 1456; https://doi.org/10.3390/rs11121456
Received: 23 April 2019 / Revised: 31 May 2019 / Accepted: 14 June 2019 / Published: 19 June 2019
The importance of snow cover extent (SCE) has been proven to strongly link with various natural phenomenon and human activities; consequently, monitoring snow cover is one the most critical topics in studying and understanding the cryosphere. As snow cover can vary significantly within short time spans and often extends over vast areas, spaceborne remote sensing constitutes an efficient observation technique to track it continuously. However, as optical imagery is limited by cloud cover and polar darkness, synthetic aperture radar (SAR) attracted more attention for its ability to sense day-and-night under any cloud and weather condition. In addition to widely applied backscattering-based method, thanks to the advancements of spaceborne SAR sensors and image processing techniques, many new approaches based on interferometric SAR (InSAR) and polarimetric SAR (PolSAR) have been developed since the launch of ERS-1 in 1991 to monitor snow cover under both dry and wet snow conditions. Critical auxiliary data including DEM, land cover information, and local meteorological data have also been explored to aid the snow cover analysis. This review presents an overview of existing studies and discusses the advantages, constraints, and trajectories of the current developments. View Full-Text
Keywords: synthetic aperture radar; backscattering; InSAR; PolSAR; snow classification; wet snow; cryosphere; data fusion; machine learning synthetic aperture radar; backscattering; InSAR; PolSAR; snow classification; wet snow; cryosphere; data fusion; machine learning
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MDPI and ACS Style

Tsai, Y.-L.S.; Dietz, A.; Oppelt, N.; Kuenzer, C. Remote Sensing of Snow Cover Using Spaceborne SAR: A Review. Remote Sens. 2019, 11, 1456.

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