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Open AccessArticle

A Combination of PROBA-V/MODIS-Based Products with Sentinel-1 SAR Data for Detecting Wet and Dry Snow Cover in Mountainous Areas

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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
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(16), 1904; https://doi.org/10.3390/rs11161904
Received: 24 June 2019 / Revised: 30 July 2019 / Accepted: 13 August 2019 / Published: 14 August 2019
(This article belongs to the Section Environmental Remote Sensing)
In the present study, we explore the value of employing both vegetation indexes as well as land surface temperature derived from Project for On-Board Autonomy—Vegetation (PROBA-V) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, respectively, to support the detection of total (wet + dry) snow cover extent (SCE) based on a simple tuning machine learning approach and provide reliability maps for further analysis. We utilize Sentinel-1-based synthetic aperture radar (SAR) observations, including backscatter coefficient, interferometric coherence, and polarimetric parameters, and four topographical factors as well as vegetation and temperature information to detect the total SCE with a land cover-dependent random forest-based approach. Our results show that the overall accuracy and F-measure are over 90% with an ’Area Under the receiver operating characteristic Curve (ROC)’ (AUC) score of approximately 80% over five study areas located in different mountain ranges, continents, and hemispheres. These accuracies are also confirmed by a comprehensive validation approach with different data sources, attesting the robustness and global transferability. Additionally, based on the reliability maps, we find an inversely proportional relationship between classification reliability and vegetation density. In conclusion, comparing to a previous study only utilizing SAR-based observations, the method proposed in the present study provides a complementary approach to achieve a higher total SCE mapping accuracy while maintaining global applicability with reliable accuracy and corresponding uncertainty information. View Full-Text
Keywords: Synthetic aperture radar; InSAR; PolSAR; backscatter; machine learning; snow cover area; land use land cover; Sentinel-2; Landsat Synthetic aperture radar; InSAR; PolSAR; backscatter; machine learning; snow cover area; land use land cover; Sentinel-2; Landsat
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Tsai, Y.-L.S.; Dietz, A.; Oppelt, N.; Kuenzer, C. A Combination of PROBA-V/MODIS-Based Products with Sentinel-1 SAR Data for Detecting Wet and Dry Snow Cover in Mountainous Areas. Remote Sens. 2019, 11, 1904.

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