Many real-world applications rely on land surface temperature (LST) data at high spatiotemporal resolution. In complex urban areas, LST exhibits significant variations, fluctuating dramatically within and across city blocks. Landsat provides high spatial resolution data at 100 meters but is limited by long revisit time, with cloud cover further disrupting data collection. Here, we propose DELAG, a deep ensemble learning method that integrates annual temperature cycles and Gaussian processes, to reconstruct Landsat LST in complex urban areas. Leveraging the cross-track characteristics and dual-satellite operation of Landsat since 2021, we further enhance data availability to 4 scenes every 16 days. We select New York City, London and Hong Kong from three different continents as study areas. Experiments show that DELAG successfully reconstructed LST in the three cities under clear-sky (RMSE = 0.73-0.96 K) and heavily-cloudy (RMSE = 0.84-1.62 K) situations, superior to existing methods. Additionally, DELAG can quantify uncertainty that enhances LST reconstruction reliability. We further tested the reconstructed LST to estimate near-surface air temperature, achieving results (RMSE = 1.48-2.11 K) comparable to those derived from clear-sky LST (RMSE = 1.63-2.02 K). The results demonstrate the successful reconstruction through DELAG and highlight the broader applications of LST reconstruction for estimating accurate air temperature. Our study thus provides a novel and practical method for Landsat LST reconstruction, particularly suited for complex urban areas within Landsat cross-track areas, taking one step toward addressing complex climate events at high spatiotemporal resolution. Code and data are available at https://skrisliu.com/delag

翻译：众多实际应用依赖于高时空分辨率的地表温度数据。在复杂的城市区域，地表温度呈现显著的空间异质性，在城市街区内部及街区之间均存在剧烈波动。Landsat卫星可提供100米空间分辨率的数据，但受限于较长的重访周期，且云层覆盖会进一步干扰数据采集。本研究提出DELAG——一种融合年温度循环与高斯过程的深度集成学习方法，用于重建复杂城市区域的Landsat地表温度。利用2021年以来Landsat卫星的跨轨观测特性及双星运行模式，我们将数据可用性提升至每16天4景影像。选取位于三大洲的纽约、伦敦和香港作为研究区域。实验表明，DELAG在晴空条件下（均方根误差=0.73-0.96 K）和浓云条件下（均方根误差=0.84-1.62 K）均成功重建了三个城市的地表温度，其性能优于现有方法。此外，DELAG能够量化不确定性，从而提升地表温度重建的可靠性。我们进一步利用重建的地表温度估算近地表气温，所得结果（均方根误差=1.48-2.11 K）与基于晴空地温的估算结果（均方根误差=1.63-2.02 K）具有可比性。这些结果验证了DELAG方法的成功重建能力，并凸显了地表温度重建在精确气温估算中的广泛应用前景。本研究为Landsat地表温度重建提供了一种新颖实用的方法，特别适用于Landsat跨轨区域内的复杂城市环境，为在高时空分辨率下解析复杂气候事件迈出了重要一步。代码与数据详见https://skrisliu.com/delag