High-Resolution Global Land Surface Temperature Retrieval via a Coupled Mechanism-Machine Learning Framework
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909772524027904 |
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| author | Xie, Tian Shen, Huanfeng Jiang, Menghui Jiménez-Muñoz, Juan-Carlos Sobrino, José A. Li, Huifang Zeng, Chao |
| author_facet | Xie, Tian Shen, Huanfeng Jiang, Menghui Jiménez-Muñoz, Juan-Carlos Sobrino, José A. Li, Huifang Zeng, Chao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_04991 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | High-Resolution Global Land Surface Temperature Retrieval via a Coupled Mechanism-Machine Learning Framework Xie, Tian Shen, Huanfeng Jiang, Menghui Jiménez-Muñoz, Juan-Carlos Sobrino, José A. Li, Huifang Zeng, Chao Atmospheric and Oceanic Physics Artificial Intelligence Machine Learning 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. |
| title | High-Resolution Global Land Surface Temperature Retrieval via a Coupled Mechanism-Machine Learning Framework |
| topic | Atmospheric and Oceanic Physics Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2509.04991 |