干旱区生态环境知识资源中心(Arid): Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP

Arid

DOI	10.7717/peerj.6310
	Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP
	Wang, Haifeng 1,2; Chen, Yinwen 3; Zhang, Zhitao 1,2; Chen, Haorui 4; Li, Xianwen 2; Wang, Mingxiu 5; Chai, Hongyang 2
通讯作者	Zhang, Zhitao
来源期刊	PEERJ
ISSN	2167-8359
出版年	2019
卷号	7
英文摘要	Soil salinization is the primary obstacle to the sustainable development of agriculture and eco-environment in arid regions. The accurate inversion of the major water-soluble salt ions in the soil using visible-near infrared (VIS-NIR) spectroscopy technique can enhance the effectiveness of saline soil management. However, the accuracy of spectral models of soil salt ions turns out to be affected by high dimensionality and noise information of spectral data. This study aims to improve the model accuracy by optimizing the spectral models based on the exploration of the sensitive spectral intervals of different salt ions. To this end, 120 soil samples were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. After determining the raw reflectance spectrum and content of salt ions in the lab, the spectral data were pretreated by standard normal variable (SNV). Subsequently the sensitive spectral intervals of each ion were selected using methods of gray correlation (GC), stepwise regression (SR) and variable importance in projection (VIP). Finally, the performance of both models of partial least squares regression (PLSR) and support vector regression (SVR) was investigated on the basis of the sensitive spectral intervals. The results indicated that the model accuracy based on the sensitive spectral intervals selected using different analytical methods turned out to be different: VIP was the highest, SR came next and GC was the lowest. The optimal inversion models of different ions were different. In general, both PLSR and SVR had achieved satisfactory model accuracy, but PLSR outperformed SVR in the forecasting effects. Great difference existed among the optimal inversion accuracy of different ions: the predicative accuracy of Ca2+, Na+, Mg2+ and SO42- was very high, that of CO32- was high and K+ was relatively lower, but HCO3- failed to have any predicative power. These findings provide a new approach for the optimization of the spectral model of water-soluble salt ions and improvement of its predicative precision.
英文关键词	Soil salinization Water-soluble salt ions VIS-NIR GC SR VIP Model
类型	Article
语种	英语
国家	Peoples R China ; USA
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000456309600005
WOS关键词	REFLECTANCE SPECTROSCOPY ; SALINIZED SOILS ; INNER-MONGOLIA ; METHODS PLSR ; FIELD ; MODELS ; INDICATORS ; PREDICTION ; SPECTRA ; CHINA
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
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/2XILL650/217895
作者单位	1.Northwest A&F Univ, Key Lab Agr Soil & Water Engn Arid & Semiarid Are, Minist Educ, Yangling, Shaanxi, Peoples R China; 2.Northwest A&F Univ, Coll Water Resources & Architectural Engn, Yangling, Shaanxi, Peoples R China; 3.Northwest A&F Univ, Dept Foreign Languages, Yangling, Shaanxi, Peoples R China; 4.China Inst Water Resources & Hydropower Res, Dept Irrigat & Drainage, Beijing, Peoples R China; 5.Univ Calif Irvine, Dept Civil & Environm Engn, Irvine, CA USA
推荐引用方式 GB/T 7714	Wang, Haifeng,Chen, Yinwen,Zhang, Zhitao,et al. Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP[J]. 西北农林科技大学,2019,7.
APA	Wang, Haifeng.,Chen, Yinwen.,Zhang, Zhitao.,Chen, Haorui.,Li, Xianwen.,...&Chai, Hongyang.(2019).Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP.PEERJ,7.
MLA	Wang, Haifeng,et al."Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP".PEERJ 7(2019).