An Innovative Framework for Hourly Satellite Soil Moisture Retrieval via Integrated Spatiotemporal Downscaling Techniques
文献类型：期刊论文
| 作者 | Song, Peilin7; Wang, Mengran7; Dong, Lixin1; Zhao, Tianjie6; Zhao, Haigen5; Huang, Jingfeng4; Yao, Panpan3; Zheng, Jingyao6; Zhang, Yongqiang2 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 63页码:4422716 |
| 关键词 | Spatial resolution Satellite broadcasting Microwave radiometry Soil moisture Monitoring Spaceborne radar Sensors Remote sensing Microwave imaging Data models Diurnal temperature cycle (DTC) downscaling geostationary satellite hourly soil moisture microwave |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2025.3635555 |
| 产权排序 | 7 |
| 文献子类 | Article |
| 英文摘要 | Satellite microwave remote sensing is acknowledged as the primary method for obtaining global-scale surface soil moisture (SSM) data. Typically, spaceborne microwave sensors aboard polar-orbiting satellites yield SSM estimates with a native spatial resolution of several tens of kilometers and a temporal resolution of about once or twice daily. This indicates considerable potential for enhancing both of their spatial and temporal resolutions. Although spatial downscaling of spaceborne microwave SSM has garnered significant attention recently, the enhancement of their temporal resolution has received less focus. This study pioneers a methodology for generating hourly scale satellite SSM estimates. The developed approach integrates a novel blending module that combines geostationary satellite observations with a spatially downscaled SSM dataset derived from traditional fusion among microwave and optical observations on polar-orbiting platforms. This blending module leverages land surface temperature (LST) data from geostationary satellites, which effectively quantify SSM variations on an hourly interval upon the thermal inertia theory. Consequently, a comprehensive spatiotemporal integrated framework for SSM downscaling is established to produce hourly scale SSM at a resolution of 6 km, enhancing upon the daily and 36-km resolutions of existing microwave SSM datasets. Validation of the downscaled hourly SSM estimates was conducted through an established ground soil moisture observatory network in North China, revealing an unbiased root-mean-square error (ubRMSE) of no higher than 0.04 cm(3)/cm(3). This result confirms preservation of the fundamental accuracy of original microwave SSM retrievals and demonstrates the effectiveness of the developed framework in improving both spatial and temporal representativeness of SSM data. |
| URL标识 | 查看原文 |
| WOS关键词 | LAND-SURFACE TEMPERATURE ; COARSE RESOLUTION SATELLITE ; ALL-WEATHER ; GEOSTATIONARY ; PRODUCT ; CHINA ; MODIS ; WATER ; CYCLE |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001630260100004 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/219409]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Song, Peilin; Zhao, Haigen |
| 作者单位 | 1.China Meteorol Adm, Natl Satellite Meteorol Ctr, Key Lab Radiometr Calibrat & Validat Environm Sate, Beijing 100081, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 3.North China Univ Water Resources & Elect Power, Coll Surveying & Geoinformat, Zhengzhou 450046, Peoples R China; 4.Zhejiang Univ, Inst Appl Remote Sensing & Informat Technol, Hangzhou 310058, Peoples R China; 5.Chinese Acad Agr Sci, Inst Environm & Sustainable Dev Agr, Beijing 100081, Peoples R China; 6.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; 7.Xi An Jiao Tong Univ, Minist Educ, Fac Elect & Informat Engn, Key Lab Phys Elect & Devices, Xian 710049, Shaanxi, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Song, Peilin,Wang, Mengran,Dong, Lixin,et al. An Innovative Framework for Hourly Satellite Soil Moisture Retrieval via Integrated Spatiotemporal Downscaling Techniques[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2025,63:4422716. |
| APA | Song, Peilin.,Wang, Mengran.,Dong, Lixin.,Zhao, Tianjie.,Zhao, Haigen.,...&Zhang, Yongqiang.(2025).An Innovative Framework for Hourly Satellite Soil Moisture Retrieval via Integrated Spatiotemporal Downscaling Techniques.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,63,4422716. |
| MLA | Song, Peilin,et al."An Innovative Framework for Hourly Satellite Soil Moisture Retrieval via Integrated Spatiotemporal Downscaling Techniques".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63(2025):4422716. |
入库方式： OAI收割
来源：地理科学与资源研究所
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