Land surface temperature (LST) has been effectively retrieved from thermal infrared (TIR) satellite measurements under clear-sky conditions. However, TIR satellite data are often severely contaminated by clouds, which cause spatiotemporal discontinuities and low retrieval accuracy in the LST products. Several solutions have been proposed to fill the “gaps”; however, a majority of these possess constraints. For example, fusion methods with microwave data suffer from coarse spatial resolution and diverse land cover types while spatial-temporal interpolation methods neglect cloudy cooling effects. We developed a novel method to estimate cloudy-sky LST from polar-orbiting satellite data based on the surface energy balance (SEB) principle. First, the hypothetical clear-sky LST of missing or likely cloud-contaminated pixels was reconstructed by assimilating high-quality satellite retrievals into a time-evolving model built from reanalysis data using a Kalman filter data assimilation algorithm. Second, clear-sky LST was hypothetically corrected by accounting for cloud cooling based on SEB theory. The proposed method was applied to Visible Infrared Imaging Radiometer Suite (VIIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) data, and further validated using ground measurements of fourteen sites from SURFRAD, BSRN, and AmeriFlux in 2013. VIIRS LST recovered from cloud gaps exhibited a root mean square error (RMSE) of 3.54 K, a bias of −0.36 K, R² of 0.94, and sample size (N) of 2411, comparable to the accuracy of clear-sky LST products and cloudy-sky LST estimation from MODIS (RMSE of 3.69 K, bias of −0.45 K, R² of 0.93, and N of 2398). Thus, the proposed method performs well across different sensors, seasons, and land cover types. The abnormal retrieval values caused by cloud contamination were also corrected in the proposed method. The overall accuracy was better than the downscaled cloudy-sky LST retrieved from passive microwave (PMW) observations and former SEB-based cloudy-sky LST estimation methods. Validation using time-series measurements showed that the all-sky LST time series, including both clear- and cloudy-sky retrievals, can capture realistic variability without sudden abruptions or discontinuities. RMSE values for the all-sky LST varied from 2.54 to 4.15 K at the fourteen sites. Spatially continuous LST maps over the Contiguous United States were compared with corresponding maps from PMW data in the winter and summer of 2018, exhibiting similar spatial patterns but with additional spatial details. Moreover, sensitivity analysis suggested that the reconstruction of clear-sky LST dominantly impacts the accuracy of cloudy-sky LST estimation. The proposed method can be potentially implemented in similar satellite sensors for global real-time production.

地表温度 (LST) 已从晴空条件下的热红外 (TIR) 卫星测量中有效地反演。然而，TIR卫星数据往往受到云层的严重污染，导致LST产品的时空不连续性和反演精度低。已经提出了几种解决方案来填补“空白”；然而，其中大多数都有限制。例如，微波数据融合方法存在空间分辨率粗糙和土地覆盖类型多样的问题，而时空插值方法忽略了多云冷却效应。我们开发了一种基于表面能量平衡 (SEB) 原理从极轨卫星数据估计多云天空 LST 的新方法。第一的，通过将高质量卫星检索同化到使用卡尔曼滤波器数据同化算法从再分析数据构建的时间演化模型中，重建了丢失或可能被云污染的像素的假设晴空 LST。其次，根据 SEB 理论，通过考虑云冷却，对晴空 LST 进行了假设校正。所提出的方法应用于可见红外成像辐射计套件 (VIIRS) 和中分辨率成像光谱仪 (MODIS) 数据，并使用 2013 年来自 SURFRAD、BSRN 和 AmeriFlux 的 14 个站点的地面测量进一步验证。 从云隙中恢复的 VIIRS LST 展示均方根误差 (RMSE) 为 3.54 K，偏差为 -0.36 K，R² of 0.94，样本大小 (N) 为 2411，与 MODIS 的晴天 LST 产品和多云天 LST 估计的准确性相当（RMSE 为 3.69 K，偏差为 -0.45 K，R ²0.93，N 为 2398）。因此，所提出的方法在不同的传感器、季节和土地覆盖类型上表现良好。提出的方法还修正了由云污染引起的异常检索值。整体精度优于从被动微波 (PMW) 观测和以前基于 SEB 的多云天空 LST 估计方法中检索到的缩小的多云天空 LST。使用时间序列测量的验证表明，全天空 LST 时间序列，包括晴空和多云天空的检索，可以捕捉现实的变化，而不会突然中断或不连续。十四个站点的全天 LST 的 RMSE 值从 2.54 到 4.15 K 不等。将美国本土的空间连续 LST 地图与 2018 年冬季和夏季 PMW 数据的相应地图进行了比较，表现出相似的空间模式，但具有额外的空间细节。此外，敏感性分析表明，晴空 LST 的重建主要影响多云天空 LST 估计的准确性。所提出的方法可以潜在地在类似的卫星传感器中实现，以进行全球实时生产。