干旱区生态环境知识资源中心(Arid): Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms

Arid

DOI	10.3389/fpls.2023.1260772
	Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms
	Tuerxun, Nigela; Zheng, Jianghua; Wang, Renjun; Wang, Lei; Liu, Liang
通讯作者	Zheng, JH
来源期刊	FRONTIERS IN PLANT SCIENCE
ISSN	1664-462X
出版年	2023
卷号	14
英文摘要	The leaf chlorophyll content (LCC) of vegetation is closely related to photosynthetic efficiency and biological activity. Jujube (Ziziphus jujuba Mill.) is a traditional economic forest tree species. Non-destructive monitoring of LCC of jujube is of great significance for guiding agroforestry production and promoting ecological environment protection in arid and semi-arid lands. Hyperspectral data is an important data source for LCC detection. However, hyperspectral data consists of a multitude of bands and contains extensive information. As a result, certain bands may exhibit high correlation, leading to redundant spectral information. This redundancy can distort LCC prediction results and reduce accuracy. Therefore, it is crucial to select appropriate preprocessing methods and employ effective data mining techniques when analyzing hyperspectral data. This study aims to evaluate the performance of hyperspectral data for estimating LCC of jujube trees by integrating different derivative processing techniques with different dimensionality reduction algorithms. Hyperspectral reflectance data were obtained through simulations using an invertible forest reflectance model (INFORM) and measurements from jujube tree canopies. The least absolute shrinkage and selection operator (LASSO) and elastic net (EN) were employed to identify the important bands in the original spectra (OS), first derivative spectra (FD), and second derivative spectra (SD). Support vector regression (SVR) was used to establish the estimation model. The results show that compared with full-spectrum modeling, LASSO and EN algorithms are effective methods for preventing overfitting in LCC machine learning estimation models for different spectral derivatives. The LASSO/EN-based estimation models constructed using FD and SD exhibited superior R2 compared to the OS. The important band of SD can best reveal the relevant information of jujube LCC, and SD-EN-SVR is the most ideal model in both the simulated dataset (R2 = 0.99, RMSE=0.61) and measured dataset (R2 = 0.89, RMSE=0.91). Our results provided a reference for rapid and non-destructive estimation of the LCC of agroforestry vegetation using canopy hyperspectral data.
英文关键词	hyperspectral data elastic net LASSO support vector regression invertible forest reflectance model derivative processing
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:001114100700001
WOS关键词	ESTIMATING LEAF CHLOROPHYLL ; RED-EDGE ; CONIFER FOREST ; REFLECTANCE ; VEGETATION ; SOIL ; SELECTION ; SENTINEL-2 ; INDEXES ; IMAGERY
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/2XILL650/396601
推荐引用方式 GB/T 7714	Tuerxun, Nigela,Zheng, Jianghua,Wang, Renjun,et al. Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms[J],2023,14.
APA	Tuerxun, Nigela,Zheng, Jianghua,Wang, Renjun,Wang, Lei,&Liu, Liang.(2023).Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms.FRONTIERS IN PLANT SCIENCE,14.
MLA	Tuerxun, Nigela,et al."Hyperspectral estimation of chlorophyll content in jujube leaves: integration of derivative processing techniques and dimensionality reduction algorithms".FRONTIERS IN PLANT SCIENCE 14(2023).