Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms
文献类型：期刊论文
| 作者 | Zhang, Yu5,6,7; Han, Wenting1,3,4,7; Zhang, Huihui2; Niu, Xiaotao5,6,7; Shao, Guomin1,4 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2023-02-01 |
| 卷号 | 617页码:12 |
| ISSN号 | 0022-1694 |
| 关键词 | Unmanned aerial vehicle (UAV) Soil moisture content (SMC) Machine learning Maize Growth stages Deficit irrigation |
| DOI | 10.1016/j.jhydrol.2023.129086 |
| 通讯作者 | Han, Wenting() |
| 英文摘要 | Timely and accurate estimation of soil moisture content (SMC) is essential for precise irrigation management at the farm scale. Unmanned aerial vehicle (UAV) remote sensing with a high spatiotemporal resolution has become a promising method for SMC monitoring. Many existing SMC models have only been tested at a specific crop growth stage using a single type of sensor, and the effects of growth stage and irrigation variation on SMC estimation accuracy remain unclear. To address these limitations, this study used UAV-based multimodal data to quantify SMC in a maize field under various levels of irrigation over two years using three machine learning algorithms (MLA): partial least squares regression (PLSR), K nearest neighbor (KNN), and random forest regression (RFR). The results demonstrated that multimodal data fusion improves the SMC estimation accuracy regardless of the MLA, especially the joint use of thermal and multispectral data. Among three SMC regression models, the RFR model produced the most accurate SMC estimation for the two growing seasons regardless of sensor combinations. The RFR model using all three types of data generated the most accurate and robust SMC estimation at the vegetative stage with R2 of 0.68 and 0.78, and rRMSE of 20.82% and 19.36% for 10- and 20-cm soil depths, respectively; it also produced the best SMC estimation accuracy under well-watered and mild to modest deficit irrigation treatments for both soil depths. The study shows that the high spatial-temporal maps of SMC using UAV-based multimodal data has promising potential for supporting decision-making in irrigation scheduling at the farmland scale. |
| WOS关键词 | WATER-CONTENT ESTIMATION ; DEFICIT IRRIGATION ; VEGETATION INDEXES ; SURFACE ; YIELD ; LEAF ; REFLECTANCE ; PREDICTION ; PRECISION ; FUSION |
| 资助项目 | National Natural Science Founda- tion of China[51979233] ; Key Research and Development Project of Shaanxi Province[2022KW-47] |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| 出版者 | ELSEVIER |
| WOS记录号 | WOS:000921284700001 |
| 资助机构 | National Natural Science Founda- tion of China ; Key Research and Development Project of Shaanxi Province |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/189449]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Han, Wenting |
| 作者单位 | 1.Northwest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Shaanxi, Peoples R China 2.USDA ARS, Water Management & Syst Res Unit, 2150 Ctr Ave, Bldg D, Ft Collins, CO 80526 USA 3.Northwest A&F Univ, Inst Soil & Water Conservat, Yangling 712100, Shaanxi, Peoples R China 4.Minist Agr, Key Lab Agr Internet Things, Yangling 712100, Shaanxi, Peoples R China 5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 6.Chinese Acad Sci & Minist Water Resources, Inst Soil & Water Conservat, Yangling 712100, Shaanxi, Peoples R China 7.Chinese Acad Sci & Minist Educ, Res Ctr Soil & Water Conservat & Ecol Environm, Yangling 712100, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Yu,Han, Wenting,Zhang, Huihui,et al. Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms[J]. JOURNAL OF HYDROLOGY,2023,617:12. |
| APA | Zhang, Yu,Han, Wenting,Zhang, Huihui,Niu, Xiaotao,&Shao, Guomin.(2023).Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms.JOURNAL OF HYDROLOGY,617,12. |
| MLA | Zhang, Yu,et al."Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms".JOURNAL OF HYDROLOGY 617(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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