The global warming poses a significant threat to glacier ecosystem. Accelerated melting of glaciers leading to sea-level rise has become a global concern. This study aims to explore the feasibility of glacier detection at a large spatial scale using polarized Synthetic Aperture Radar (SAR) data, MODIS-Land Surface Temperature (LST) data and Automatic Weather Station (AWS) temperature data. In the five study regions from 2017 to 2021, we conduct incidence angle correction on backscattering coefficient and polarization parameters through an empirical model. Additionally, polarization decomposition of SLC data was performed to obtain Alpha and Entropy images. To assess glacier melting status, AWS data and MODIS-LST were utilized as ground truth, categorized into different Radar Glacier Zones (RGZ) describing diverse physical properties of glacier surfaces. An ensemble decision tree model is trained using the distinct feature values of both SAR data types to determine glacier melt status. The accuracy of glacier melting detection using backscatter features can reach 80%, while the accuracy using polarization decomposition features is 74%. Despite the lower accuracy compared to the former, polarization decomposition demonstrated greater sensitivity in areas with poor discrimination based on backscatter, particularly in bare ice zones. Combining the features of both backscatter and polarization decomposition achieved an accuracy of 83%. The spatial distribution of melt detection results closely aligned with recent anomalous melting events. Future research should focus on deepening the understanding and application of various physical parameters, optimizing models to enhance glacier melt detection accuracy and maximizing the utilization of SAR data.
 

Glacier melt; Greenland ice sheet; Sentinel-1; backscatter coefficient; radar glacier zones