Comparison of land surface temperature retrieved by split-window algorithm using thermal infrared observations from multiple satellites in the china region

Land Surface Temperature (LST) is crucial for studying various surface processes. Previous validations of remotely sensed LST products gained mostly the combined results of remotely sensed observations overlaid with inversion algorithms, failing to account for the impact of multi-source data on inversion results. To address this, data from six polar-orbiting satellites within China region in 2019 were collected using a uniform inversion algorithm. The derived LST values were then validated against in-situ measurements from 13 ground sites in China. Results showed minor disparities in LST validation results among satellites, with an root mean square error (RMSE) ranging from 2.6K to 3.0K. Variation analysis revealed higher errors in extreme temperature levels and a decrease in RMSE with increasing angular distance. These findings enhance understanding of multi-source data's influence on LST inversion quality and promote collaborative utilization of satellite data.