{"ID":2874201,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.04991","arxiv_id":"2509.04991","title":"High-Resolution Global Land Surface Temperature Retrieval via a Coupled Mechanism-Machine Learning Framework","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.","short_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 l...","url_abs":"https://arxiv.org/abs/2509.04991","url_pdf":"https://arxiv.org/pdf/2509.04991v1","authors":"[\"Tian Xie\",\"Huanfeng Shen\",\"Menghui Jiang\",\"Juan-Carlos Jiménez-Muñoz\",\"José A. Sobrino\",\"Huifang Li\",\"Chao Zeng\"]","published":"2025-09-05T10:37:27Z","proceeding":"physics.ao-ph","tasks":"[\"physics.ao-ph\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}