干旱区生态环境知识资源中心(Arid): Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods

Arid

DOI	10.1016/j.agwat.2022.108064
	Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods
	Shao, Guomin; Han, Wenting; Zhang, Huihui; Zhang, Liyuan; Wang, Yi; Zhang, Yu
通讯作者	Han, WT
来源期刊	AGRICULTURAL WATER MANAGEMENT
ISSN	0378-3774
EISSN	1873-2283
出版年	2023
卷号	276
英文摘要	In the upcoming irrigation management in agricultural production, accurate mapping of crop water consumption with a high spatial and temporal resolution at a farm scale is needed. In this study, we developed models for crop coefficients (Kc) estimation using unmanned aerial vehicle (UAV) remote sensing and machine learning (ML) techniques for irrigated maize in a semi-arid region in Northwest China. Kc values were calculated using a procedure given in FAO56 manual using field measurements. Multispectral vegetation indices (VIs), vegetation fraction (VF), thermal-based VIs, and texture information (TI) were derived from UAV-based multispectral, RGB, and thermal infrared imagery, respectively. These remotely sensed variables and their combinations were used to develop prediction models using six ML algorithms (linear regression-LR, polynomial regression-PR, exponential regression-ER, random forest regression-RFR, support vector regression-SVR, and deep neural network-DNN). Among these models, the RFR with the highest accuracy (R2 = 0.69, RMSE = 0.1019) was recommended to estimate maize Kc. The multispectral and thermal-based VIs and texture of the near-infrared band had greater contributions than RGB-based VF and TI in the Kc-RFR model under different irrigation treatments. Furthermore, the maize Kc-RFR prediction model had high accuracy in estimating cumulative evapotranspiration (R2 = 0.89, RMSE = 15.0 mm/stage) during different growth stages and daily soil water content (R2 = 0.85, RMSE = 0.0089 m3/m3) in the root zone. These results show that the integration of UAV remote sensing and ML provides a promising tool to help farmers make decisions using timely mapped crop water consumption, especially under water shortages or drought conditions.
英文关键词	Crop water requirement FAO56 Evapotranspiration UAV Crop coefficient Random Forest regression
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000899210700005
WOS关键词	WHEAT TRITICUM-AESTIVUM ; LEAF-AREA INDEX ; CANOPY TEMPERATURE ; SOIL EVAPORATION ; METEOROLOGICAL DATA ; VEGETATION INDEX ; WATER-STRESS ; GRAIN-YIELD ; ARID REGION ; DATA FUSION
WOS类目	Agronomy ; Water Resources
WOS研究方向	Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395028
推荐引用方式 GB/T 7714	Shao, Guomin,Han, Wenting,Zhang, Huihui,et al. Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods[J],2023,276.
APA	Shao, Guomin,Han, Wenting,Zhang, Huihui,Zhang, Liyuan,Wang, Yi,&Zhang, Yu.(2023).Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods.AGRICULTURAL WATER MANAGEMENT,276.
MLA	Shao, Guomin,et al."Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods".AGRICULTURAL WATER MANAGEMENT 276(2023).