High-Resolution Global Land Surface Temperature Retrieval via a Coupled Mechanism-Machine Learning Framework
This addresses the problem of reliable LST retrieval for climate monitoring and ecosystem studies, though it is incremental as it builds on existing physical and ML methods.
The paper tackled the challenge of accurately retrieving land surface temperature (LST) under heterogeneous conditions by proposing a coupled mechanism-machine learning framework, achieving MAE=1.84K, RMSE=2.55K, and R-squared=0.966, with over 50% error reduction in extreme cases.
Land surface temperature (LST) is vital for land-atmosphere interactions and climate processes. Accurate LST retrieval remains challenging under heterogeneous land cover and extreme atmospheric conditions. Traditional split window (SW) algorithms show biases in humid environments; purely machine learning (ML) methods lack interpretability and generalize poorly with limited data. We propose a coupled mechanism model-ML (MM-ML) framework integrating physical constraints with data-driven learning for robust LST retrieval. Our approach fuses radiative transfer modeling with data components, uses MODTRAN simulations with global atmospheric profiles, and employs physics-constrained optimization. Validation against 4,450 observations from 29 global sites shows MM-ML achieves MAE=1.84K, RMSE=2.55K, and R-squared=0.966, outperforming conventional methods. Under extreme conditions, MM-ML reduces errors by over 50%. Sensitivity analysis indicates LST estimates are most sensitive to sensor radiance, then water vapor, and less to emissivity, with MM-ML showing superior stability. These results demonstrate the effectiveness of our coupled modeling strategy for retrieving geophysical parameters. The MM-ML framework combines physical interpretability with nonlinear modeling capacity, enabling reliable LST retrieval in complex environments and supporting climate monitoring and ecosystem studies.