Layered Soil Moisture Retrieval and Agricultural Application Based on Multi-Source Remote Sensing and Vegetation Suppression Technology: A Case Study of Youyi Farm, China
文献类型：期刊论文
| 作者 | Zhao, Zhonghe4; Li, Yuyang5; Liu, Kun6; Wu, Chunsheng1; Yu, Bowei2; Liu, Gaohuan1; Wang, Youxiao3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2025-06-21 |
| 卷号 | 17期号:13页码:2130 |
| 关键词 | soil moisture multi-source remote sensing vegetation suppression random forest UAV multispectral imagery |
| DOI | 10.3390/rs17132130 |
| 产权排序 | 4 |
| 文献子类 | Article |
| 英文摘要 | Soil moisture dynamics are a key parameter in regulating agricultural productivity and ecosystem functioning. The accurate monitoring and quantitative retrieval of soil moisture play a crucial role in optimizing agricultural water resource management. In recent years, the development of multi-source remote sensing technologies-such as high spatiotemporal resolution optical, radar, and thermal infrared sensors-has opened new avenues for efficient soil moisture retrieval. However, the accuracy of soil moisture retrieval decreases significantly when the soil is covered by vegetation. This study proposes a multi-modal remote sensing collaborative retrieval framework that integrates UAV-based multispectral imagery, Sentinel-1 radar data, and in situ ground sampling. By incorporating a vegetation suppression technique, a random-forest-based quantitative soil moisture model was constructed to specifically address the interference caused by dense vegetation during crop growing seasons. The results demonstrate that the retrieval performance of the model was significantly improved across different soil depths (0-5 cm, 5-10 cm, 10-15 cm, 15-20 cm). After vegetation suppression, the coefficient of determination (R2) exceeded 0.8 for all soil layers, while the mean absolute error (MAE) decreased by 35.1% to 49.8%. This research innovatively integrates optical-radar-thermal multi-source data and a physically driven vegetation suppression strategy to achieve high-accuracy, meter-scale dynamic mapping of soil moisture in vegetated areas. The proposed method provides a reliable technical foundation for precision irrigation and drought early warning. |
| URL标识 | 查看原文 |
| WOS关键词 | SYNTHETIC-APERTURE RADAR ; MODEL ; BAND ; SENTINEL-1 ; SUPPORT ; BIOMASS ; SAR |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001526279400001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215340]  |
| 专题 | 拉萨站高原生态系统研究中心_外文论文 |
| 通讯作者 | Wang, Youxiao |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; 2.Beijing Normal Univ, Sch Environm, State Key Joint Lab Environm Simulat & Pollut Cont, Beijing 100875, Peoples R China; 3.Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan 250101, Peoples R China 4.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China; 5.Jilin Univ, Coll Geoexplorat Sci & Technol, Changchun 130300, Peoples R China; 6.Inner Mongolia Agr Univ, Coll Agron, Hohhot 010019, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Zhao, Zhonghe,Li, Yuyang,Liu, Kun,et al. Layered Soil Moisture Retrieval and Agricultural Application Based on Multi-Source Remote Sensing and Vegetation Suppression Technology: A Case Study of Youyi Farm, China[J]. REMOTE SENSING,2025,17(13):2130. |
| APA | Zhao, Zhonghe.,Li, Yuyang.,Liu, Kun.,Wu, Chunsheng.,Yu, Bowei.,...&Wang, Youxiao.(2025).Layered Soil Moisture Retrieval and Agricultural Application Based on Multi-Source Remote Sensing and Vegetation Suppression Technology: A Case Study of Youyi Farm, China.REMOTE SENSING,17(13),2130. |
| MLA | Zhao, Zhonghe,et al."Layered Soil Moisture Retrieval and Agricultural Application Based on Multi-Source Remote Sensing and Vegetation Suppression Technology: A Case Study of Youyi Farm, China".REMOTE SENSING 17.13(2025):2130. |
入库方式： OAI收割
来源：地理科学与资源研究所
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