Abstract
Satellite-derived Land Surface Temperature (LST) products are central to surface urban heat island (SUHI) monitoring due to their consistent grid-based coverage over large metropolitan regions. However, cloud contamination frequently obscures LST observations, limiting their usability for continuous SUHI analysis. Most existing LST reconstruction methods rely on multitemporal information or multisensor data fusion, requiring auxiliary observations that may be unavailable or unreliable under persistent cloud cover. Purely spatial gap-filling approaches offer an alternative, but traditional statistical methods degrade under large or spatially contiguous gaps, while many deep learning based spatial models deteriorate rapidly with increasing missingness. Recent advances in denoising diffusion based image inpainting models have demonstrated improved robustness under high missingness, motivating their adoption for spatial LST reconstruction. In this work, we introduce UrbanDIFF, a purely spatial denoising diffusion model for reconstructing cloud contaminated urban LST imagery. The model is conditioned on static urban structure information, including built-up surface data and a digital elevation model, and enforces strict consistency with revealed cloud free pixels through a supervised pixel guided refinement step during inference. UrbanDIFF is trained and evaluated using NASA MODIS Terra LST data from seven major United States metropolitan areas spanning 2002 to 2025. Experiments using synthetic cloud masks with 20 to 85 percent coverage show that UrbanDIFF consistently outperforms an interpolation baseline, particularly under dense cloud occlusion, achieving SSIM of 0.89, RMSE of 1.2 K, and R2 of 0.84 at 85 percent cloud coverage, while exhibiting slower performance degradation as cloud density increases.
Abstract (translated)
基于卫星的陆地表面温度（LST）产品对于城市热岛（UHI）监测至关重要，因为它们在大型都市区域提供了连续一致的网格覆盖。然而，云层污染经常遮蔽了LST观测值，限制了其用于持续性UHI分析的能力。大多数现有的LST重建方法依赖于多时相信息或多传感器数据融合，这些方法需要辅助观察，但在长时间的云层覆盖下可能不可用或不准确。纯空间插值法提供了另一种选择，但传统的统计方法在处理大范围或连续的空间缺口时效果较差；而基于深度学习的空间模型在缺失值增加的情况下性能迅速下降。 最近，在去噪扩散基础上进行图像修复的方法显示出更高的鲁棒性来应对高比例的缺失数据，这激发了这些方法用于空间LST重建的应用。在这项工作中，我们介绍了一种专为城市地区设计的纯空间去噪扩散模型——UrbanDIFF，用于恢复被云污染的城市LST影像。该模型结合静态城市结构信息（包括建筑表面数据和数字高程模型），并通过对未受云层遮蔽像素进行监督式的像素引导细化步骤，在推理过程中严格保证与已知清晰区域的一致性。 UrbanDIFF使用美国宇航局MODIS Terra LST数据在七个主要的美国都市区进行了训练和评估，时间跨度从2002年到2025年。实验采用合成云掩模（覆盖范围为20%至85%）显示，UrbanDIFF在所有测试条件下均优于插值基准方法，特别是在高密度云层遮蔽的情况下表现出色，达到了SSIM为0.89、RMSE为1.2K以及R²为0.84的性能指标（当云覆盖达到85%时），并且随着云量增加而显示出较慢的性能下降趋势。
URL
https://arxiv.org/abs/2512.17782
