Determination of the Optimal Channel Configuration for Land Surface Temperature Retrieval Using Split Window Algorithm

Currently, various algorithms have been developed to retrieve regional and global land surface temperature (LST) from satellite thermal infrared (TIR) observations, among which the split window (SW) algorithm is the most widely used one. However, the LST retrieval accuracy would be affected by the channel centers and channel widths owing to the vast atmospheric conditions and land surface types around the world. The theoretical channel configuration leading to the best performance of the SW algorithm is still not well investigated currently. In this study, the LST retrieval accuracies of the SW algorithm using different channel configurations were studied iteratively through the whole TIR atmospheric window. Consequently, the two channels centered at 10.3 and $11.5~\mu $ m with the widths of 0.3 and $0.4~\mu $ m were found to be the optimal channel configuration for applying the SW algorithm. Based on the global atmospheric profiles provided in the ERA5 and SeeBor V5.0 database and the emissivity spectra provided in the ECOSTRESS library, the performance of the SW algorithm using the determined channel configuration was delicately evaluated. Results show that the LST retrieval root mean square error (RMSE) of the determined channel configuration was 1.09 K, better than that of the MODIS (1.28 K), Landsat-9 (1.24 K), and Sentinel-3 A (1.24 K) instruments regarding the global atmospheric profiles provided in the ERA5 database. Similar results were obtained corresponding to SeeBor V5.0 atmospheric profiles with the LST retrieval RMSE of 1.28 K (determined channel configuration), 1.49 K (MODIS), 1.96 K (Landsat-9), and 1.94 K (Sentinel-3 A).