发表机构
University College London; Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research; University of Victoria（伦敦大学学院; 阿尔弗雷德·韦格纳极地与海洋研究所亥姆霍兹中心; 维多利亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在准确估计北极海冰粗糙度，采用RoughNet方法，通过条件扩散框架从光学卫星图像重建海冰地形，经训练和评估，能在不同冰况下泛化，为高分辨率海冰测绘和粗糙度估计提供可扩展途径。
AI 中文摘要
准确估计固定海冰粗糙度对气候建模和北极安全冰上航行至关重要，但现有方法依赖昂贵的航空测量或稀疏的现场测量，限制了空间覆盖和操作可扩展性。本文表明，使用条件扩散框架可直接从光学卫星图像重建高分辨率海冰地形。我们的方法RoughNet学会将10米的哨兵2多光谱图像映射到局部归一化的1米表面高程残差场，从而从广泛可用的卫星数据中进行精细尺度粗糙度表征。该模型在两个北极地区的机载激光雷达数据上训练，并在第三个未见过的北极地区评估，能在不同冰况下泛化并部分再现小规模地形结构。性能最佳的模型在域外均方根误差为9厘米，同时保留了底层粗糙度场的统计和光谱特性。这些结果表明生成扩散模型可仅从光学图像中恢复物理上有意义的表面结构，为数据稀疏环境中的高分辨率海冰测绘和粗糙度估计提供了可扩展途径。
英文摘要
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.
CommentsCode available at https://github.com/tessacannon48/RoughNet
