干旱区生态环境知识资源中心(Arid): Estimation of summer maize evapotranspiration using MARS model in the semi-arid region of northwest China

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DOI	10.1016/j.compag.2020.105495
	Estimation of summer maize evapotranspiration using MARS model in the semi-arid region of northwest China
	Shan, Xiaoqin; Cui, Ningbo; Cai, Huanjie; Hu, Xiaotao; Zhao, Lu
通讯作者	Cui, NB
来源期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN	0168-1699
EISSN	1872-7107
出版年	2020
卷号	174
英文摘要	Evapotranspiration (ET) is a crucial parameter of agricultural water management, and its accurate estimation is of great significance to implement precision irrigation and optimal allocation of regional water resources. This study investigated the efficiency of the multivariate adaptive regression splines (MARS) model for estimating daily ET of summer maize under twelve input combinations, including complete and incomplete meteorological factors and crop growth indicators for the period 2011-2013. The performance of the MARS model was compared with the empirical Priestley-Taylor (P-T), Shuttleworth-Wallace (S-W) and Two-Patch (T-P) models as well as the back-propagation neural networks (BPNN) model. The optimal MARS and BPNN models achieved better ET prediction than the optimal empirical models at each growth stage. The MARS model was superior to the BPNN model at all growth stages in terms of mean absolute error: seedling emergence to tasseling (0.8476 mm/d vs. 0.9833 mm/d), tasseling to grouting (0.6777 mm/d vs. 0.7162 mm/d), grouting to harvest (0.3342 mm/d vs. 0.4336 mm/d) and the entire growth period (0.8752 mm/d vs. 0.9577 mm/d). Generally, the MARS model outperformed the BPNN model and empirical models at all growth stages, indicating that the optimal MARS models accurately modeled the complex nonlinear relationships between ET and the meteorological factors and crop growth indicators. The MARS model is thus highly recommended for estimating ET during the entire growth period of summer maize in the semi-arid regions when lack of adequate meteorological factors or crop growth indicators.
英文关键词	Evapotranspiration Summer maize Multivariate adaptive regression splines Back-propagation neural network Empirical models
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000540218000020
WOS关键词	LIMITED METEOROLOGICAL DATA ; SUPPORT-VECTOR-MACHINE ; GLOBAL SOLAR-RADIATION ; NEURAL-NETWORKS ; EMPIRICAL EQUATIONS ; POTENTIAL EVAPOTRANSPIRATION ; CLIMATIC DATA ; TEMPERATURE ; PREDICTION ; FUZZY
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS研究方向	Agriculture ; Computer Science
来源机构	西北农林科技大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/324744
作者单位	[Shan, Xiaoqin; Cui, Ningbo; Zhao, Lu] Sichuan Univ, Coll Water Resource & Hydropower, Chengdu 610065, Peoples R China; [Shan, Xiaoqin; Cui, Ningbo; Zhao, Lu] Sichuan Univ, State Key Lab Hydraul & Mt River Engn, Chengdu 610065, Peoples R China; [Cui, Ningbo; Cai, Huanjie; Hu, Xiaotao] Northwest A&F Univ, Key Lab Agr Soil & Water Engn Arid & Semiarid Are, Minist Educ, Yangling 712100, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Shan, Xiaoqin,Cui, Ningbo,Cai, Huanjie,et al. Estimation of summer maize evapotranspiration using MARS model in the semi-arid region of northwest China[J]. 西北农林科技大学,2020,174.
APA	Shan, Xiaoqin,Cui, Ningbo,Cai, Huanjie,Hu, Xiaotao,&Zhao, Lu.(2020).Estimation of summer maize evapotranspiration using MARS model in the semi-arid region of northwest China.COMPUTERS AND ELECTRONICS IN AGRICULTURE,174.
MLA	Shan, Xiaoqin,et al."Estimation of summer maize evapotranspiration using MARS model in the semi-arid region of northwest China".COMPUTERS AND ELECTRONICS IN AGRICULTURE 174(2020).