A Remote Sensing-Based Parameterization Method for Global Maximum Surface Relative Humidity With Energy Balance Multivariable Coupling and XGBoost Model
文献类型：期刊论文
| 作者 | Wang, Xiaowei2,3,4; Jiang, Yazhen3,4; Wang, Xin1; Si, Menglin3; Lou, Yunsheng2; Kou, Huaxi5 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 4565 |
| 卷号 | 63页码:5011109 |
| 关键词 | Land surface Remote sensing Humidity Estimation Temperature measurement Surface resistance Atmospheric measurements Surface treatment Spatiotemporal phenomena Moisture Evapotranspiration (ET) maximum surface relative humidity ( h(s,max) ) surface vapor pressure |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2025.3641945 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | The maximum surface relative humidity ( h(s,max) ) is a key factor that affects surface vapor pressure ( es ), which governs evapotranspiration (ET) rates. Existing empirical parameterizations lack a robust physical basis and exhibit limited generalizability. To address these limitations, we propose a remote sensing-based framework for the first global-scale estimation of h(s,max) , which integrates physical constraints and data-driven methods. The framework first back-calculated h(s,max)by leveraging coupling mechanisms in the surface energy balance equation, using ET and meteorological data from 195 global flux tower sites. Subsequently, XGBoost-based inversion models were constructed separately for different land surface types, with site-level meteorological variables and the back-calculated h(s,max) as inputs. The models were applied to global remote sensing and reanalysis datasets, enabling the global-scale estimation of monthly h(s,max )from 2001 to 2020. Results demonstrate that the h(s,max) models for different land surface types achieved high accuracy, with a mean root mean square error (RMSE) of 0.079 and a correlation coefficient ( R ) of 0.92. A contribution analysis using Shapley Additive Explanations (SHAP) reveals that relative humidity (RH) is the dominant predictor, while secondary factors vary by land surface type. The global monthly h(s,max) estimates for 2001-2020 exhibit distinct climate-driven spatial patterns. Temporal variability is low (CV <10%) in humid regions but exceeds 20% in arid regions. In conclusion, the proposed parameterization framework shows both physical consistency and high accuracy in global-scale h(s,max) estimation, providing essential parameter support for studies of land-atmosphere interactions. |
| URL标识 | 查看原文 |
| WOS关键词 | WATER VAPOR-PRESSURE ; EVAPOTRANSPIRATION RATES ; AIR-TEMPERATURE ; MODIS ; LAND ; EVAPORATION ; IMPACT |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001652017600005 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219521]  |
| 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 | Jiang, Yazhen |
| 作者单位 | 1.China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China; 2.Nanjing Univ Informat Sci & Technol, Sch Ecol & Appl Meteorol, Nanjing 210044, Peoples R China; 3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 4.Chinese Acad Sci, State Key Lab Remote Sensing Sci, Aerosp Informat Res Inst, Beijing 100101, Peoples R China; 5.Capital Normal Univ, Coll Resources Environm & Tourism, Beijing 100083, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Xiaowei,Jiang, Yazhen,Wang, Xin,et al. A Remote Sensing-Based Parameterization Method for Global Maximum Surface Relative Humidity With Energy Balance Multivariable Coupling and XGBoost Model[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,4565,63:5011109. |
| APA | Wang, Xiaowei,Jiang, Yazhen,Wang, Xin,Si, Menglin,Lou, Yunsheng,&Kou, Huaxi.(4565).A Remote Sensing-Based Parameterization Method for Global Maximum Surface Relative Humidity With Energy Balance Multivariable Coupling and XGBoost Model.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,63,5011109. |
| MLA | Wang, Xiaowei,et al."A Remote Sensing-Based Parameterization Method for Global Maximum Surface Relative Humidity With Energy Balance Multivariable Coupling and XGBoost Model".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63(4565):5011109. |
入库方式： OAI收割
来源：地理科学与资源研究所
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