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.

翻译：卫星反演的地表温度产品因其在大都市区域提供一致的网格化覆盖，成为地表城市热岛监测的核心数据源。然而，云污染常常遮蔽LST观测，限制了其在连续SUHI分析中的可用性。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，这需要辅助观测数据，而这些数据在持续云层覆盖下可能无法获取或不可靠。纯空间间隙填补方法提供了一种替代方案，但传统的统计方法在大面积或空间连续缺失区域效果不佳，而许多基于深度学习的空间模型会随着缺失率的增加而性能迅速下降。基于去噪扩散的图像修复模型的最新进展已证明其在高度缺失情况下的鲁棒性有所提升，这启发了我们将其应用于空间LST重建。在本研究中，我们提出了UrbanDIFF，一种用于重建云污染城市LST影像的纯空间去噪扩散模型。该模型以静态城市结构信息为条件，包括建成区地表数据和数字高程模型，并在推理过程中通过一个有监督的像素引导细化步骤，强制模型与已揭示的无云像素保持严格一致性。UrbanDIFF使用美国七个主要大都市区2002年至2025年的NASA MODIS Terra LST数据进行训练和评估。使用覆盖率为20%至85%的合成云掩模进行的实验表明，UrbanDIFF始终优于插值基线方法，尤其是在密集云层遮挡下，在85%云覆盖率时取得了0.89的SSIM、1.2 K的RMSE和0.84的R²，并且在云密度增加时表现出更慢的性能退化。