Estimation of all-sky 1 km land surface temperature over the conterminous United States

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 allsky Global Land Data Assimilation System (GLDAS) LST product at 0.25 degrees 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 allsky LST product.