Land surface temperature (LST) is a crucial parameter for hydrology, climate monitoring, and ecological and environmental research. LST products from thermal infrared (TIR) satellite data have been widely used for that. However, TIR information cannot provide LST data under cloudy-sky conditions. All-sky LST can be estimated from microwave measurements, but their coarse spatial resolution, narrow swaths, and short temporal range make it impossible to generate a long-term, high-resolution, accurate global all-sky LST global. This study proposes a methodology for generating the all-sky LST product by combining multiple data from Moderate Resolution Imaging Spectroradiometer (MODIS), reanalysis, and ground in situ measurements using a random forest. Field measurements from the AmeriFlux and Surface Radiation Budget (SURFRAD) networks were used for model training and validation. Cloudy-sky and clear-sky LST models were developed separately. To further improve the accuracy of the cloudy-sky LST model, the conventional RF model was extended to incorporate temporal information. The models were validated using in situ LST measurements from 2010, 2011, and 2017 that were not used for the model training. For the cloudy-sky and clear-sky models, root-mean-square-error (RMSE) = 2.767 and 2.756 K, R² = 0.943 and 0.963, and bias = −0.143 and − 0.138 K, respectively. The same validation samples were used to validate both the MODIS LST product under clear-sky conditions and all-sky Global Land Data Assimilation System (GLDAS) LST product at 0.25° spatial resolution, with RMSE = 3.033 and 4.157 K, bias = −0.362 and − 0.224 K, and R² = 0.904 and 0.955, respectively. Additionally, the 10-fold cross-validation results using all the training datasets further indicate the model stability. The models were applied to generate the all-sky LST product from 2000 to 2015 over the conterminous United States (CONUS). Our product shows similar spatial patterns to the MODIS and GLDAS LST products, but it is more accurate. Both validation and product comparisons demonstrated the robustness of our proposed models in generating the all-sky LST product.

地表温度 (LST) 是水文、气候监测以及生态和环境研究的关键参数。来自热红外 (TIR) 卫星数据的 LST 产品已被广泛用于此目的。但是，TIR 信息无法提供多云天空条件下的 LST 数据。全天 LST 可以通过微波测量进行估计，但它们的空间分辨率粗糙、条带窄、时间范围短，因此无法生成长期、高分辨率、准确的全球全天 LST 全球。本研究提出了一种方法，通过结合来自中分辨率成像光谱仪 (MODIS)、再分析和使用随机森林的地面原位测量的多个数据来生成全天空 LST 产品。来自 AmeriFlux 和表面辐射预算 (SURFRAD) 网络的现场测​​量值用于模型训练和验证。多云天空和晴天 LST 模型是分开开发的。为了进一步提高多云天空 LST 模型的准确性，对传统的 RF 模型进行了扩展，以包含时间信息。这些模型使用 2010 年、2011 年和 2017 年未用于模型训练的原位 LST 测量值进行了验证。对于阴天和晴天模型，均方根误差 (RMSE) = 2.767 和 2.756 K，R 和 2017 年未用于模型训练。对于阴天和晴天模型，均方根误差 (RMSE) = 2.767 和 2.756 K，R 和 2017 年未用于模型训练。对于阴天和晴天模型，均方根误差 (RMSE) = 2.767 和 2.756 K，R²  = 0.943 和 0.963，偏差分别 = -0.143 和 - 0.138 K。相同的验证样本用于验证晴空条件下的 MODIS LST 产品和 0.25° 空间分辨率下的全天空全球陆地数据同化系统 (GLDAS) LST 产品，RMSE = 3.033 和 4.157 K，偏差 = -0.362和 − 0.224 K，和 R ² = 0.904 和 0.955，分别。此外，使用所有训练数据集的 10 倍交叉验证结果进一步表明了模型的稳定性。这些模型用于生成 2000 年至 2015 年美国本土 (CONUS) 的全天 LST 产品。我们的产品显示出与 MODIS 和 GLDAS LST 产品相似的空间模式，但更准确。验证和产品比较都证明了我们提出的模型在生成全天空 LST 产品方面的稳健性。