Land Surface Temperature Retrieval From Sentinel-3A SLSTR Data: Comparison Among Split-Window, Dual-Window, Three-Channel, and Dual-Angle Algorithms

Land surface temperature (LST) is a vital parameter for studying global ecological, climatic, and environmental changes. Although various LST retrieval algorithms have been proposed, including split-window (SW), dual-window (DW), three-channel (TC), and dual-angle (DA) algorithms, few studies have compared these algorithms using the same satellite observations. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3A provides a unique opportunity to conduct this comparison due to its DA viewing capability and multiple thermal infrared (TIR) and mid-infrared (MIR) channels. Here, we implemented two SW algorithms, one DW algorithm, two TC algorithms, and one DA algorithm for the SLSTR data. The LST retrievals from these six algorithms were validated, along with the SLSTR operational LST product based on an emissivity-implicit SW algorithm. Temperature- and radiance-based validation methods were used to evaluate different LST retrievals across different land cover types (LCTs). The results indicated that the proposed SW algorithm had the highest accuracy, followed by the Perez-Planells SW and the official algorithms. The overall root-mean-square errors (RMSEs) of these three SW algorithms were 1.42, 1.79, and 2.05 K. The three algorithms involving the MIR channel (one DW and two TC algorithms) were more suitable for nighttime LST retrieval and had similar performances to the three SW algorithms, with a nighttime RMSE of approximately 1.36 K. The LST retrieval accuracy of the DA algorithm had the highest uncertainty and was closely related to the angular variation in surface emissivity and brightness temperature (BT). The findings of this study contribute to a better understanding of the different LST retrieval algorithms and facilitate potential improvements in the official LST retrieval algorithm for SLSTR.