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.

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.

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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.

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