Daily Soil Moisture Retrieval by Fusing CYGNSS and Multi-Source Auxiliary Data Using Machine Learning Methods
文献类型：期刊论文
| 作者 | Yang, Ting2,5; Wang, Jundong1,4; Sun, Zhigang1,2,4,5; Li, Sen3 |
| 刊名 | SENSORS
 |
| 出版日期 | 2023-11-01 |
| 卷号 | 23期号:22页码:12 |
| 关键词 | CYGNSS soil moisture data fusion land cover GBRT |
| DOI | 10.3390/s23229066 |
| 通讯作者 | Yang, Ting(yangt@igsnrr.ac.cn) |
| 英文摘要 | The Cyclone Global Navigation Satellite System (CYGNSS), a publicly accessible spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) data, provides a new alternative opportunity for large-scale soil moisture (SM) retrieval, but with interference from complex environmental conditions (i.e., vegetation cover and ground roughness). This study aims to develop a high-accuracy model for CYGNSS SM retrieval. The normalized surface reflectivity calculated by CYGNSS is fused with variables that are highly related to the SM obtained from optical/microwave remote sensing to solve the problem of the influence of complicated environmental conditions. The Gradient Boost Regression Tree (GBRT) model aided by land-type data is then used to construct a multi-variables SM retrieval model with six different land types of multiple models. The methodology is tested in southeastern China, and the results correlate very well with the existing satellite remote sensing products and in situ SM data (R = 0.765, ubRMSE = 0.054 m3m-3 vs. SMAP; R = 0.653, ubRMSE = 0.057 m3 m-3 vs. ERA5 SM; R = 0.691, ubRMSE = 0.057 m3m-3 vs. in situ SM). This study makes contributions from two aspects: (1) improves the accuracy of the CYGNSS retrieval of SM based on fusion with other auxiliary data; (2) constructs the SM retrieval model with multi-layer multiple models, which is suitable for different land properties. |
| WOS关键词 | TEMPERATURE ; ALGORITHM |
| 资助项目 | National Natural Science Foundation of China |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| 出版者 | MDPI |
| WOS记录号 | WOS:001113699400001 |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200229]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Yang, Ting |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, CAS Engn Lab Yellow River Delta Modern Agr, Beijing 100101, Peoples R China 3.China Meteorol Adm, Natl Meteorol Ctr, Beijing 100081, Peoples R China 4.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 5.Shandong Dongying Inst Geog Sci, Dongying 257000, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Ting,Wang, Jundong,Sun, Zhigang,et al. Daily Soil Moisture Retrieval by Fusing CYGNSS and Multi-Source Auxiliary Data Using Machine Learning Methods[J]. SENSORS,2023,23(22):12. |
| APA | Yang, Ting,Wang, Jundong,Sun, Zhigang,&Li, Sen.(2023).Daily Soil Moisture Retrieval by Fusing CYGNSS and Multi-Source Auxiliary Data Using Machine Learning Methods.SENSORS,23(22),12. |
| MLA | Yang, Ting,et al."Daily Soil Moisture Retrieval by Fusing CYGNSS and Multi-Source Auxiliary Data Using Machine Learning Methods".SENSORS 23.22(2023):12. |
入库方式： OAI收割
来源：地理科学与资源研究所
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