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.
Advancing terrestrial snow depth monitoring with machine learning and l-band insar data: a case study using nasa’s snowex 2017 data
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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.