Deep learning model achieves 0.81 correlation with lidar snow depth from Sentinel-1 InSAR, outperforming physics-based retrievals at 0.47 correlation, with demonstrated temporal and spatial transferability.
Tracking flowpaths in a complex karst system through tracer test and hydrogeochemical monitoring: Implications for groundwater protection (gran sasso, italy)
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Deep Learning-Based Snow Depth Retrieval Using Sentinel-1 Repeat-Pass InSAR
Deep learning model achieves 0.81 correlation with lidar snow depth from Sentinel-1 InSAR, outperforming physics-based retrievals at 0.47 correlation, with demonstrated temporal and spatial transferability.