干旱区生态环境知识资源中心(Arid): Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm

Arid

DOI	10.3389/fpls.2023.1284172
	Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm
	Wang, Jingang; Tian, Tian; Wang, Haijiang; Cui, Jing; Shi, Xiaoyan; Song, Jianghui; Li, Tiansheng; Li, Weidi; Zhong, Mingtao; Zhang, Wenxu
通讯作者	Wang, HJ ; Cui, J
来源期刊	FRONTIERS IN PLANT SCIENCE
ISSN	1664-462X
出版年	2023
卷号	14
英文摘要	Soil salinization greatly restricts crop production in arid areas for salinity stress can inhibit crop photosynthesis and growth. Chlorophyll fluorescence and photosynthetic gas exchange (CFPGE) parameters are important indicators of crop photosynthesis and have been widely used to evaluate the impacts of salinity stress on crop photosynthesis and growth. Remote sensing technology can quickly and non-destructively obtain crop information under salinity stress, however, at present, the distribution of spectral features of CFPGE parameters in different regions is still unclear. In this study (2019-2020), under salinity stress conditions, the spectral data of rapeseed leaves were acquired and the CFPGE parameters were simultaneously determined. Then, continuous wavelet transformation (CWT) and standard normal variate (SNV) transformation were utilized to preprocess the raw spectral data. After that, a CFPGE parameter estimation model was constructed by using the partial least squares regression (PLSR) algorithm and the support vector machines (SVM) algorithm based on the spectral features in the red region (600-800 nm) and those in the red, blue-green (350-600 nm), and near-infrared (800-2500 nm) regions. The results showed that the spectral features of CFPGE parameters could be extracted by successive projections algorithm (SPA) based on the CWT preprocessing. The CFPGE parameter estimation model constructed based on the spectral features in the red region (675 nm, 680 nm, 688 nm, 749 nm, and 782 nm) had the highest Fv/Fm estimation accuracy on day 30, with R2c, R2p, and RPD of 0.723, 0.585, and 1.68, respectively. Based on this, the spectral features (578 nm, 976 nm, 1088 nm, 1476 nm, and 2250 nm) in the blue-green and near-infrared regions were added in the variables for modeling, which significantly improved the accuracy and stability of the model, with R2c, R2p, and RPD of 0.886, 0.815, and 2.58, respectively. Therefore, the fusion of the spectral features in the red, blue-green, and near-infrared regions could improve the estimation accuracy of rapeseed leaf CFPGE parameters. This study will provide technical reference for rapid estimation of photosynthetic performance of crops under salinity stress in arid and semi-arid areas.
英文关键词	photosynthesis continuous wavelet transformation partial least squares regression support vector machines brassic anapus
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:001114108000001
WOS关键词	CHLOROPHYLL-A FLUORESCENCE ; SALT STRESS ; STOMATAL CONDUCTANCE ; REFLECTANCE SPECTRA ; NITROGEN-CONTENT ; WINTER-WHEAT ; GRAIN-YIELD ; CANOPY ; INDEXES ; WATER
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/2XILL650/396611
推荐引用方式 GB/T 7714	Wang, Jingang,Tian, Tian,Wang, Haijiang,et al. Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm[J],2023,14.
APA	Wang, Jingang.,Tian, Tian.,Wang, Haijiang.,Cui, Jing.,Shi, Xiaoyan.,...&Zhang, Wenxu.(2023).Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm.FRONTIERS IN PLANT SCIENCE,14.
MLA	Wang, Jingang,et al."Improving the estimation accuracy of rapeseed leaf photosynthetic characteristics under salinity stress using continuous wavelet transform and successive projections algorithm".FRONTIERS IN PLANT SCIENCE 14(2023).