A Mechanism-Learning Deeply Coupled Model for Remote Sensing Retrieval of Global Land Surface Temperature

• URL: http://arxiv.org/abs/2504.07481v3
• Date: Tue, 22 Apr 2025 13:51:47 GMT
• Title: A Mechanism-Learning Deeply Coupled Model for Remote Sensing Retrieval of Global Land Surface Temperature
• Authors: Tian Xie, Menghui Jiang, Huanfeng Shen, Huifang Li, Chao Zeng, Jun Ma, Guanhao Zhang, Liangpei Zhang,
• Abstract summary: Land surface temperature (LST) retrieval from remote sensing data is pivotal for analyzing climate processes and surface energy budgets.<n>In this paper, we propose a deeply coupled framework integrating mechanistic modeling and machine learning to enhance the accuracy and generalizability of single-channel LST retrieval.
• Score: 17.57893699503326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Land surface temperature (LST) retrieval from remote sensing data is pivotal for analyzing climate processes and surface energy budgets. However, LST retrieval is an ill-posed inverse problem, which becomes particularly severe when only a single band is available. In this paper, we propose a deeply coupled framework integrating mechanistic modeling and machine learning to enhance the accuracy and generalizability of single-channel LST retrieval. Training samples are generated using a physically-based radiative transfer model and a global collection of 5810 atmospheric profiles. A physics-informed machine learning framework is proposed to systematically incorporate the first principles from classical physical inversion models into the learning workflow, with optimization constrained by radiative transfer equations. Global validation demonstrated a 30% reduction in root-mean-square error versus standalone methods. Under extreme humidity, the mean absolute error decreased from 4.87 K to 2.29 K (53% improvement). Continental-scale tests across five continents confirmed the superior generalizability of this model.

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