A practical two-step framework for all-sky land surface temperature estimation
文献类型：期刊论文
| 作者 | Zhang, Huanyu3,4; Tang, Bo-Hui2,4; Li, Zhao-Liang1 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2024-03-15 |
| 卷号 | 303页码:22 |
| ISSN号 | 0034-4257 |
| 关键词 | All -sky land surface temperature (LST) Hypothetical clear -sky LST Surface energy balance Cloud radiative forcing effect Cloudy -sky conditions |
| DOI | 10.1016/j.rse.2024.113991 |
| 通讯作者 | Tang, Bo-Hui(tangbh@kust.edu.cn) |
| 英文摘要 | Land surface temperature (LST) is a key parameter in global ecological and climate system, while its accurate estimation under cloudy-sky conditions remains a challenge. In this paper, a practical two-step framework has been proposed for all-sky LST estimation, which effectively combined physical mechanisms and machine learning algorithms. The first step was the estimation of hypothetical clear-sky LST. The Bayesian optimizationbased Extreme Gradient Boosting (BO-XGB) model was developed to establish the implicit and complex relationship between hypothetical clear-sky LST and corresponding independent variables, including hypothetical clear-sky radiation energy input, surface biophysical parameters, atmospheric conditions, as well as spatial and temporal features. The second step was the cloud radiative forcing effect (CRFE) correction for hypothetical clear-sky LST under cloudy-sky conditions. Based on the surface energy balance (SEB) equation and conventional force-restore method, the analytical expressions of CRFE correction terms were derived, which speeded up the calculation and facilitated error analysis. Based on several remote sensing products, including Advanced Baseline Imager (ABI) LST and auxiliary data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Global LAnd Surface Satellite (GLASS), as well as ERA5 reanalysis data, the proposed framework was applied to recovered spatially continuous hourly all-sky LST information over the contiguous US (CONUS) in 2018. The accuracy of the proposed method has been validated by ground measurements from 60 representative sites operated by the Surface Radiation Budget (SURFRAD) and AmeriFlux. For clear-sky conditions, the overall root mean square error (RMSE) was 2.67 K, with a bias of -0.47 K and R2 of 0.96. For cloudy-sky conditions, the corresponding accuracy metrics were 2.60 K, -0.49 K, and 0.96, respectively, and RMSEs for all sites were below 4 K, which indicated the stability of the proposed method. Besides, daily average LST has also been calculated based on the estimated hourly LSTs with RMSE of 1.68 K and bias of -0.48 K. |
| WOS关键词 | CLOUDY CONDITIONS ; RADIATION BUDGET ; MODIS ; GEOSTATIONARY ; VALIDATION ; ALGORITHM |
| 资助项目 | National Natural Science Foundation of China[42230109] ; Yunling Scholar Project of the Xingdian Talent Support Program of Yunnan Province ; Platform Construction Project of High -Level Talent in the Kunming University of Science and Technology (KUST) |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCIENCE INC |
| WOS记录号 | WOS:001174640900001 |
| 资助机构 | National Natural Science Foundation of China ; Yunling Scholar Project of the Xingdian Talent Support Program of Yunnan Province ; Platform Construction Project of High -Level Talent in the Kunming University of Science and Technology (KUST) |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/203231]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Tang, Bo-Hui |
| 作者单位 | 1.Inst Agr Resources & Reg Planning, Chinese Acad Agr Sci, Key Lab Agr Remote Sensing, Minist Agr & Rural Affairs, Beijing 100081, Peoples R China 2.Kunming Univ Sci & Technol, Fac Land Resource Engn, Kunming 650093, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst L, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Huanyu,Tang, Bo-Hui,Li, Zhao-Liang. A practical two-step framework for all-sky land surface temperature estimation[J]. REMOTE SENSING OF ENVIRONMENT,2024,303:22. |
| APA | Zhang, Huanyu,Tang, Bo-Hui,&Li, Zhao-Liang.(2024).A practical two-step framework for all-sky land surface temperature estimation.REMOTE SENSING OF ENVIRONMENT,303,22. |
| MLA | Zhang, Huanyu,et al."A practical two-step framework for all-sky land surface temperature estimation".REMOTE SENSING OF ENVIRONMENT 303(2024):22. |
入库方式： OAI收割
来源：地理科学与资源研究所
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