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This study presents RoughNet, a conditional diffusion framework that reconstructs high-resolution sea ice topography from 10 m Sentinel-2 multispectral satellite images, addressing the limitations of traditional airborne surveys and sparse in-situ measurements. By training on airborne LiDAR data from two Arctic regions and evaluating on a third, RoughNet achieves a root mean squared error of 9 cm, effectively generalizing across diverse ice conditions while preserving the statistical properties of the roughness field. This method enables fine-scale roughness characterization, significantly enhancing the scalability and accessibility of Arctic sea ice mapping.
Generative diffusion models can now recover detailed sea ice roughness from standard satellite imagery, transforming our approach to Arctic climate modeling.
Accurate estimation of landfast sea ice roughness is critical for climate modeling and safe Arctic over-ice travel, yet existing approaches rely on costly airborne surveys or sparse in-situ measurements, limiting spatial coverage and operational scalability. Here we show that high-resolution sea ice topography can be reconstructed directly from optical satellite imagery using a conditional diffusion framework. Our approach, RoughNet, learns to map 10 m Sentinel-2 multispectral images to locally normalized 1 m surface elevation residual fields, enabling fine-scale roughness characterization from widely available satellite data. Trained on airborne LiDAR data from two Arctic regions and evaluated on an unseen third Arctic region, the model generalizes across diverse ice conditions and partially reproduces small-scale topographic structure. The best-performing model achieves an out-of-domain root mean squared error of 9 cm while preserving the statistical and spectral properties of the underlying roughness field. These results demonstrate that generative diffusion models can recover physically meaningful surface structure from optical imagery alone, providing a scalable pathway for high-resolution sea ice mapping and roughness estimation in data-sparse environments.