用深度集成学习重建陆地温度，提升城市区域遥感数据精度。
Daily Land Surface Temperature Reconstruction in Landsat Cross-Track Areas Using Deep Ensemble Learning With Uncertainty Quantification
- 融合年周期与高斯过程的深度集成模型，利用双卫星数据增补采样。
- 云层遮蔽下重建误差仅0.84–1.62 K，优于现有方法。
- 可量化不确定性，适合城市热环境与气候研究者使用。
众多实际应用依赖高时空分辨率的地表温度（LST）数据。在复杂城市区域，地表温度变化剧烈，城市街区内部及之间差异显著。Landsat虽提供100米空间分辨率，但重访周期长，云层进一步阻碍数据获取。本文提出DELAG方法，通过融合年度温度周期与高斯过程的深度集成学习，实现复杂城市区域的Landsat LST重建。借助2021年以来Landsat的交叉轨道特性与双星运行机制，数据可用性提升至每16天4景。选取纽约、伦敦和香港三个大洲代表性城市进行实验。结果表明，无论晴空（RMSE = 0.73–0.96 K）或重度云覆盖（RMSE = 0.84–1.62 K）条件下，DELAG均表现优异，优于现有方法。此外，该方法可量化不确定性，增强重建可靠性。进一步将重建的LST用于近地面气温估算，得到结果（RMSE = 1.48–2.11 K），与晴空条件下的直接观测（RMSE = 1.63–2.02 K）相当。实验证明了DELAG的有效性，并展示了地表温度重建在精准气温估算中的广阔应用前景。本研究为复杂城市区域的高时空分辨率地表温度重建提供了新且实用的方法，推动应对复杂气候事件的能力。代码与数据见https://skrisliu.com/delag。
原文摘要 · Abstract (English)
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
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