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