用卫星图像生成北极海冰高分辨率粗糙度图，替代昂贵实地测量。
RoughNet: Mapping Arctic Sea Ice Roughness Using Diffusion-Based Super-Resolution of Satellite Imagery
- 基于条件扩散模型，从10米卫星图重建1米分辨率表面起伏。
- 跨区域测试误差仅9厘米，保留真实粗糙度的统计与频谱特征。
- 适合气候建模与极地导航，为数据稀缺区提供可扩展方案。
准确估算固定海冰粗糙度对气候建模和北极冰上安全通行至关重要，但现有方法依赖昂贵机载调查或稀疏现场测量，限制了空间覆盖范围和可操作性。本文展示可通过条件扩散框架直接从光学卫星影像重建高分辨率海冰地形。RoughNet模型学习将10米分辨率的Sentinel-2多光谱图像映射到局部归一化的1米表面高程残差场，实现从广泛可用卫星数据中精细刻画粗糙度。模型在两个北极区域的机载激光雷达数据上训练，并在第三个未见区域评估，展现出跨不同冰况的泛化能力，部分复现小尺度地形结构。最优模型在域外测试中达到9厘米均方根误差，同时保持底层粗糙度场的统计与频谱特性。结果表明，生成式扩散模型可仅凭光学影像恢复具有物理意义的表面结构，为数据稀疏环境下的高分辨率海冰制图与粗糙度估计提供了可扩展路径。
原文摘要 · Abstract (English)
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.
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