干旱区生态环境知识资源中心(Arid): Improving the monitoring of root zone soil salinity under vegetation cover conditions by combining canopy spectral information and crop growth parameters

Arid

DOI	10.3389/fpls.2023.1171594
	Improving the monitoring of root zone soil salinity under vegetation cover conditions by combining canopy spectral information and crop growth parameters
	Shi, Xiaoyan; Song, Jianghui; Wang, Haijiang; Lv, Xin; Tian, Tian; Wang, Jingang; Li, Weidi; Zhong, Mingtao; Jiang, Menghao
通讯作者	Wang, HJ ; Lv, X
来源期刊	FRONTIERS IN PLANT SCIENCE
ISSN	1664-462X
出版年	2023
卷号	14
英文摘要	Soil salinization is one of the main causes of land degradation in arid and semi-arid areas. Timely and accurate monitoring of soil salinity in different areas is a prerequisite for amelioration. Hyperspectral technology has been widely used in soil salinity monitoring due to its high efficiency and rapidity. However, vegetation cover is an inevitable interference in the direct acquisition of soil spectra during crop growth period, which greatly limits the monitoring of soil salinity by remote sensing. Due to high soil salinity could lead to difficulty in plants' water absorption, and inhibit plant dry matter accumulation, a method for monitoring root zone soil salinity by combining vegetation canopy spectral information and crop aboveground growth parameters was proposed in this study. The canopy spectral information was acquired by a spectroradiometer, and then variable importance in projection (VIP), competitive adaptive reweighted sampling (CARS), and random frog algorithm (RFA) were used to extract the salinity spectral features in cotton canopy spectrum. The extracted features were then used to estimate root zone soil salinity in cotton field by combining with cotton plant height, aboveground biomass, and shoot water content. The results showed that there was a negative correlation between plant height/aboveground biomass/shoot water content and soil salinity in 0-20, 0-40, and 0-60 cm soil layers at different growth stages of cotton. Spectral feature selection by the three methods all improved the prediction accuracy of soil salinity, especially CARS. The prediction accuracy based on the combination of spectral features and cotton growth parameters was significantly higher than that based on only spectral features, with R-2 increasing by 10.01%, 18.35%, and 29.90% for the 0-20, 0-40, and 0-60 cm soil layer, respectively. The model constructed based on the first derivative spectral preprocessing, spectral feature selection by CARS, cotton plant height, and shoot water content had the highest accuracy for each soil layer, with R-2 of 0.715,0.769, and 0.742 for the 0-20, 0-40, 0-60 cm soil layer, respectively. Therefore, the method by combining cotton canopy hyperspectral data and plant growth parameters could significantly improve the prediction accuracy of root zone soil salinity under vegetation cover conditions. This is of great significance for the amelioration of saline soil in salinized farmlands arid areas.
英文关键词	canopy hyperspectral data growth parameters partial least squares regression soil salinization variable selection
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:001027436300001
WOS关键词	SELECTION METHODS ; STRESS TOLERANCE ; SALT TOLERANCE ; SPECTROSCOPY ; LEAF ; PLS ; CARBON
WOS类目	Plant Sciences
WOS研究方向	Plant Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396614
推荐引用方式 GB/T 7714	Shi, Xiaoyan,Song, Jianghui,Wang, Haijiang,et al. Improving the monitoring of root zone soil salinity under vegetation cover conditions by combining canopy spectral information and crop growth parameters[J],2023,14.
APA	Shi, Xiaoyan.,Song, Jianghui.,Wang, Haijiang.,Lv, Xin.,Tian, Tian.,...&Jiang, Menghao.(2023).Improving the monitoring of root zone soil salinity under vegetation cover conditions by combining canopy spectral information and crop growth parameters.FRONTIERS IN PLANT SCIENCE,14.
MLA	Shi, Xiaoyan,et al."Improving the monitoring of root zone soil salinity under vegetation cover conditions by combining canopy spectral information and crop growth parameters".FRONTIERS IN PLANT SCIENCE 14(2023).