All-Weather Precipitable Water Vapor Retrieval over Land Using Integrated Near-Infrared and Microwave Satellite Observations
文献类型：期刊论文
| 作者 | Song, Shipeng1,3; Zhu, Mengyao3; Tao, Zexing3; Xu, Duanyang3; Jiao, Sunxin1,3; Yang, Wanqing1,3; Wang, Huaxuan1,2; Zhao, Guodong1,3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2025-08-07 |
| 卷号 | 17期号:15页码:2730 |
| 关键词 | precipitable water vapor near-infrared passive microwave ensemble learning |
| DOI | 10.3390/rs17152730 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Precipitable water vapor (PWV) is a critical component of the Earth's atmosphere, playing a pivotal role in weather systems, climate dynamics, and hydrological cycles. Accurate estimation of PWV is essential for numerical weather prediction, climate modeling, and atmospheric correction in remote sensing. Ground-based observation stations can only provide PWV measurements at discrete points, whereas spaceborne infrared remote sensing enables spatially continuous coverage, but its retrieval algorithm is restricted to clear-sky conditions. This study proposes an innovative approach that uses ensemble learning models to integrate infrared and microwave satellite data and other geographic features to achieve all-weather PWV retrieval. The proposed product shows strong consistency with IGRA radiosonde data, with correlation coefficients (R) of 0.96 for the ascending orbit and 0.95 for the descending orbit, and corresponding RMSE values of 5.65 and 5.68, respectively. Spatiotemporal analysis revealed that the retrieved PWV product exhibits a clear latitudinal gradient and seasonal variability, consistent with physical expectations. Unlike MODIS PWV products, which suffer from cloud-induced data gaps, the proposed method provides seamless spatial coverage, particularly in regions with frequent cloud cover, such as southern China. Temporal consistency was further validated across four east Asian climate zones, with correlation coefficients exceeding 0.88 and low error metrics. This algorithm establishes a novel all-weather approach for atmospheric water vapor retrieval that does not rely on ground-based PWV measurements for model training, thereby offering a new solution for estimating water vapor in regions lacking ground observation stations. |
| URL标识 | 查看原文 |
| WOS关键词 | SURFACE-TEMPERATURE ; GPS METEOROLOGY ; CLOUD LIQUID ; ALGORITHM ; MOISTURE ; ATMOSPHERE ; OCEAN ; PATH |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001549712500001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215570]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Zhu, Mengyao |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; 2.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Song, Shipeng,Zhu, Mengyao,Tao, Zexing,et al. All-Weather Precipitable Water Vapor Retrieval over Land Using Integrated Near-Infrared and Microwave Satellite Observations[J]. REMOTE SENSING,2025,17(15):2730. |
| APA | Song, Shipeng.,Zhu, Mengyao.,Tao, Zexing.,Xu, Duanyang.,Jiao, Sunxin.,...&Zhao, Guodong.(2025).All-Weather Precipitable Water Vapor Retrieval over Land Using Integrated Near-Infrared and Microwave Satellite Observations.REMOTE SENSING,17(15),2730. |
| MLA | Song, Shipeng,et al."All-Weather Precipitable Water Vapor Retrieval over Land Using Integrated Near-Infrared and Microwave Satellite Observations".REMOTE SENSING 17.15(2025):2730. |
入库方式： OAI收割
来源：地理科学与资源研究所
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