A combination of PROBA-V/MODIS-based products with Sentinel-1 SAR data for detecting wet and dry snow cover in mountainous areas
Journal
Remote Sensing
Journal Volume
11
Journal Issue
16
Date Issued
2019-01-01
Author(s)
Abstract
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 differentmountain 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 SCEmapping accuracywhilemaintaining global applicability with reliable accuracy and corresponding uncertainty information.
Subjects
Backscatter | InSAR | Land use land cover | Landsat | Machine learning | PolSAR | Sentinel-2 | Snow cover area | Synthetic aperture radar
SDGs
Type
journal article