An integrating pre-temperature description method for generating all-weather land surface temperature via passive microwave and thermal infrared remote sensing

Integrating passive microwave (PMW) and thermal infrared (TIR) remote sensing to generate all-weather land surface temperature (LST) is essential for effective land thermal monitoring. Previous studies have attempted to adapt TIR-interactive kernel-driven downscaling techniques into the PMW-TIR integration process. However, large-scale spans often introduce significant uncertainties in the generated LST, potentially leading to spatial streaks. To address these challenges, it is critical to introduce a reliable temperature representation at the target resolution to generate accurate all-weather LST. In this study, we propose an integrated pre-temperature description model (ITDM) comprising three modules. The first module is a machine learning-based bias correction-driven generation module (BCDM), which generates relatively precise LST, particularly during the daytime, though it may smooth some spatial textures in certain regions. The second module, a spatial detail-aware generation module (SDAM), utilizes an annual temperature cycle model-based LST as a temperature description, ensuring spatial consistency in the generated LST. The third module integrates the two previous modules, addressing their differences to optimize the final output. Validation results based on MODIS LST indicate that the proposed method achieves a daytime root mean squared error (RMSE) of 3.20 K and a standard deviation of bias (STD) of 3.08 K. For nighttime, the RMSE and STD are 2.24 K and 2.15 K, respectively. Additionally, ten in-situ measurements reveal an average RMSE of 3.90 K in the daytime and 3.34 K in the nighttime. Comparative results with two other advanced methods based on MODIS LST and in-situ LST show that the proposed approach reduces RMSE by 0.04–0.91 K and mitigates streaking phenomena more effectively. The study also discusses feature importance, module performance, and the extendibility of the method. The proposed model significantly contributes to the generation of high-quality all-weather LST.