Deep learning coupled with split window and temperature-emissivity separation (DL-SW-TES) method improves clear-sky high-resolution land surface temperature estimation

Land surface temperature (LST) is a fundamental parameter in environmental and climatic studies. Over the past decades, various clear-sky LST retrieval methods have been developed, among which the temperature-emissivity separation (TES) algorithm prevails due to its good accuracy and the simultaneous retrieval of LST and land surface emissivity (LSE). However, TES relies on complete atmospheric profiles and radiative transfer calculations for atmospheric correction, which accumulates large uncertainties and requires intensive computation. In this study, we integrated the physical mechanisms of the split window (SW) and TES algorithms into the deep learning (DL) model, constructing the DL-SW-TES framework. This new framework directly retrieves LST from easily accessible parameters without requiring any prior knowledge of LSE information and atmospheric profiles. The DL-SW-TES framework was evaluated using both the simulation dataset and high-resolution ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) observations. The simulation analysis showed that the DL-SW-TES method achieved a root mean squared error (RMSE) of 1.05 K in LST retrieval and appeared robust across various uncertainty conditions. The evaluation of the ECOSTRESS LST estimates at the six radiometer sites revealed that the DL-SW-TES method achieved a better performance with an overall RMSE of 1.56 K and a bias of −0.06 K compared to the official ECO2LTSE product (with an RMSE of 1.94 K and a bias of −0.25 K). The nighttime ground measurements from the twelve pyrgeometer sites reaffirms the accuracy improvements achieved by the new model, with bias reduced by 0.7 K and RMSE reduced by approximately 0.3 K. LST estimates from DL-SW-TES and the ECO2LTSE product also present good consistency in terms of spatial patterns. The demonstrated advantage of the developed DL-SW-TES method over the traditional TES is attributed to its simplified input parameters and robustness to uncertainties in these parameters. We conclude that DL-SW-TES achieves improved accuracy compared to the traditional TES algorithm with significantly simplified input parameters and enhanced computational efficiency, standing as a promising approach for mapping clear-sky high-resolution LST at large scales from the future thermal missions. The source code and data are available at https://github.com/cas222huan/DLSWTES.