Land surface temperature (LST) plays a crucial role in the dynamic energy interchange between the land surface and the atmosphere. Utilizing thermal infrared (TIR) remote sensing constitutes a significant method for effectively capturing LST across extensive geographic regions. Over the course of several decades, researchers have dedicated their efforts to refining algorithms for deriving LST from TIR remote sensing data. Among these algorithms, the split-window (SW) technique stands out, as it directly mitigates atmospheric distortions by leveraging the brightness temperature (BT) from two adjacent TIR channels at the top of the atmosphere.

The Joint Polar Satellite System, JPSS-2/NOAA-21, represents the most recent launch in September 2021 by the National Oceanic and Atmospheric Administration (NOAA). Its primary objective is to furnish comprehensive global environmental data, encompassing insights into weather patterns, atmospheric dynamics, and various environmental indicators. Achieving this mission involves a constellation of polar-orbiting satellites. Remarkably, JPSS-2/NOAA-21 delivers two-channel Thermal Infrared (TIR) imagery, characterized by a specific spatial resolution. Consequently, the advancement of the Split-Window (SW) algorithm for Land Surface Temperature (LST) retrieval becomes especially pertinent in this context. In this study, SW algorithm was developed, and the accuracy and noise sensitivity of the results under different observation conditions were compared based on the simulation dataset to select the algorithm with the best performance. The ground measurement data under different land cover types and the global NOAA LST products were selected to evaluate the accuracy of the proposed algorithm. Validation and comparison with ground-based measurements or existing LST products showcase the algorithm's efficacy in providing accurate and reliable land surface temperature estimates over diverse landscapes and climatic conditions. The results show that the ground validation accuracy is about 1.4 K, demonstrating the potential of the split-window algorithm to contribute significantly to land surface temperature monitoring, climate studies, and environmental management initiatives utilizing data from the JPSS-2/NOAA-21 satellite system.