High-Resolution Global Land Surface Temperature Retrieval via a Coupled Mechanism-Machine Learning Framework

• URL: http://arxiv.org/abs/2509.04991v1
• Date: Fri, 05 Sep 2025 10:37:27 GMT
• Title: High-Resolution Global Land Surface Temperature Retrieval via a Coupled Mechanism-Machine Learning Framework
• Authors: Tian Xie, Huanfeng Shen, Menghui Jiang, Juan-Carlos Jiménez-Muñoz, José A. Sobrino, Huifang Li, Chao Zeng,
• Abstract summary: Land surface temperature (LST) is vital for land-atmosphere interactions and climate processes.<n>Traditional split window (SW) algorithms show biases in humid environments.<n>We propose a coupled mechanism model-ML framework integrating physical constraints with data-driven learning for robust LST retrieval.
• Score: 8.446900030915625
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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