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.
Snow water equivalent change mapping from slope-correlated synthetic aperture radar interferometry (insar) phase variations
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CE 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.