A Physical Mechanism-Constrained Deep Learning Hybrid Model for Retrieving Surface Temperature Under Nonprecipitation Cloud
文献类型：期刊论文
| 作者 | Zhu, Xin-Ming2,3,4; Yan, Si4; Tang, Bo-Hui1,2,3,4; Cheng, Yuan-Liang1; Fan, Dong2,3,4 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 63页码:5007419 |
| 关键词 | Atmospheric modeling Land surface temperature Accuracy Microwave theory and techniques Land surface Data models Clouds Remote sensing Couplings Maximum likelihood estimation Deep learning (DL) mechanistic constraint passive microwave (PMW) data surface temperature |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2025.3607942 |
| 产权排序 | 4 |
| 文献子类 | Article |
| 英文摘要 | The wide range acquisition of all-weather land surface temperatures (LSTs) from passive microwave (PMW) remotely sensed data contributes to understanding the land-atmosphere interactions, surface energy balance, and the global water cycle. Although significant progress has been made in PMW-based LST retrieval using statistical models, physical models, and machine learning methods, there remains a need to propose a model with high accuracy alongside strong physical interpretability and good generalization ability. This article aims to develop a physics-constrained deep learning (DL) hybrid model to obtain accurate LSTs under nonprecipitation clouds and then compare it with the pure physical and DL models. The hybrid model is developed by incorporating the physical loss function into the convolutional neural network, inheriting the advantages of the physical and DL models. Results show that the constructed model achieved good performance with a root mean square error (RMSE) of 1.60 K and a mean absolute error (MAE) of 1.26 K in the simulated data. Sensitivity analysis revealed that the hybrid model is less sensitive to input parameters than the pure physical and pure DL models and exhibits robustness across varying land surface and atmospheric conditions. Furthermore, during the evaluation using U.S. Surface Radiation Budget (SURFRAD) site data, the hybrid model yielded RMSEs of 4.37 and 3.48 K for day and night, respectively, with Advanced Microwave Scanning Radiometer 2 (AMSR2) and ERA5 data from 2012 to 2024, while outperforming the other two models at each SURFRAD site. The spatiotemporal applicability of the hybrid model further highlighted its superior generalization ability in mapping LSTs. We believe that the evident strength of the developed model over the traditional pure physical and DL models is attributed to its good accuracy, robustness to uncertainties in input parameters, and physical interpretability, which will benefit the other parameter estimates. |
| URL标识 | 查看原文 |
| WOS关键词 | MICROWAVE LAND ; AMSR-E ; BRIGHTNESS TEMPERATURE ; EMISSIVITY ; PREDICTION ; PARAMETERS ; ALGORITHM ; INDEX |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001575188400033 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217477]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Tang, Bo-Hui |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.Yunnan Int Joint Lab Integrated Sky Ground Intelli, Kunming 650093, Peoples R China; 3.Yunnan Key Lab Quantitat Remote Sensing, Kunming 650093, Peoples R China; 4.Kunming Univ Sci & Technol, Fac Land Resources Engn, Kunming 650093, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Zhu, Xin-Ming,Yan, Si,Tang, Bo-Hui,et al. A Physical Mechanism-Constrained Deep Learning Hybrid Model for Retrieving Surface Temperature Under Nonprecipitation Cloud[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2025,63:5007419. |
| APA | Zhu, Xin-Ming,Yan, Si,Tang, Bo-Hui,Cheng, Yuan-Liang,&Fan, Dong.(2025).A Physical Mechanism-Constrained Deep Learning Hybrid Model for Retrieving Surface Temperature Under Nonprecipitation Cloud.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,63,5007419. |
| MLA | Zhu, Xin-Ming,et al."A Physical Mechanism-Constrained Deep Learning Hybrid Model for Retrieving Surface Temperature Under Nonprecipitation Cloud".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63(2025):5007419. |
入库方式： OAI收割
来源：地理科学与资源研究所
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