干旱区生态环境知识资源中心(Arid): UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF

Arid

DOI	10.3389/fpls.2024.1358965
	UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF
	Wang, Zeyuan; Ding, Jianli; Tan, Jiao; Liu, Junhao; Zhang, Tingting; Cai, Weijian; Meng, Shanshan
通讯作者	Ding, JL
来源期刊	FRONTIERS IN PLANT SCIENCE
ISSN	1664-462X
出版年	2024
卷号	15
英文摘要	Secondary salinization is a crucial constraint on agricultural progress in arid regions. The specific mulching irrigation technique not only exacerbates secondary salinization but also complicates field-scale soil salinity monitoring. UAV hyperspectral remote sensing offers a monitoring method that is high-precision, high-efficiency, and short-cycle. In this study, UAV hyperspectral images were used to derive one-dimensional, textural, and three-dimensional feature variables using Competitive adaptive reweighted sampling (CARS), Gray-Level Co-occurrence Matrix (GLCM), Boruta Feature Selection (Boruta), and Brightness-Color-Index (BCI) with Fractional-order differentiation (FOD) processing. Additionally, three modeling strategies were developed (Strategy 1 involves constructing the model solely with the 20 single-band variable inputs screened by the CARS algorithm. In Strategy 2, 25 texture features augment Strategy 1, resulting in 45 feature variables for model construction. Strategy 3, building upon Strategy 2, incorporates six triple-band indices, totaling 51 variables used in the model's construction) and integrated with the Seagull Optimization Algorithm for Random Forest (SOA-RF) models to predict soil electrical conductivity (EC) and delineate spatial distribution. The results demonstrated that fractional order differentiation highlights spectral features in noisy spectra, and different orders of differentiation reveal different hidden information. The correlation between soil EC and spectra varies with the order. 1.9th order differentiation is proved to be the best order for constructing one-dimensional indices; although the addition of texture features slightly improves the accuracy of the model, the integration of the three-waveband indices significantly improves the accuracy of the estimation, with an R2 of 0.9476. In contrast to the conventional RF model, the SOA-RF algorithm optimizes its parameters thereby significantly improving the accuracy and model stability. The optimal soil salinity prediction model proposed in this study can accurately, non-invasively and rapidly identify excessive salt accumulation in drip irrigation under membrane. It is of great significance to improve the growing conditions of cotton, increase the cotton yield, and promote the sustainable development of Xinjiang's agricultural economy, and also provides a reference for the prevention and control of regional soil salinization.
英文关键词	precision agriculture UAV hyperspectral fractional-order differentiation feature variables SOA-RF
类型	Article
语种	英语
开放获取类型	Green Published
收录类别	SCI-E
WOS记录号	WOS:001174920300001
WOS关键词	GOSSYPIUM-HIRSUTUM L. ; SOIL ; CLASSIFICATION ; 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/2XILL650/403878
推荐引用方式 GB/T 7714	Wang, Zeyuan,Ding, Jianli,Tan, Jiao,et al. UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF[J],2024,15.
APA	Wang, Zeyuan.,Ding, Jianli.,Tan, Jiao.,Liu, Junhao.,Zhang, Tingting.,...&Meng, Shanshan.(2024).UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF.FRONTIERS IN PLANT SCIENCE,15.
MLA	Wang, Zeyuan,et al."UAV hyperspectral analysis of secondary salinization in arid oasis cotton fields: effects of FOD feature selection and SOA-RF".FRONTIERS IN PLANT SCIENCE 15(2024).