干旱区生态环境知识资源中心(Arid): Enhancing soil particle content prediction accuracy: advanced hyperspectral analysis and machine learning models

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DOI	10.1007/s11368-024-03886-8
	Enhancing soil particle content prediction accuracy: advanced hyperspectral analysis and machine learning models
	Wang, Xiao; Ding, Jianli; Han, Lijing; Tan, Jiao; Ge, Xiangyu
通讯作者	Ding, JL
来源期刊	JOURNAL OF SOILS AND SEDIMENTS
ISSN	1439-0108
EISSN	1614-7480
出版年	2024
英文摘要	PurposePrediction of soil particle content is essential for soil texture classification, soil management and agricultural production. This study aimed to achieve high-accuracy predictions of soil particle content in the Ogan-Kucha River Oasis using hyperspectral data and environmental variables.Materials and methodsWe collected 62 representative surface soil samples (depth: 0-10 cm), and conducting indoor soil particle content and spectral measurements. The relationship between environmental variables and soil particle content was analyzed using the Boruta algorithm, and seven three-band spectral indices (TBIs) were constructed using an optimal band algorithm. By integrating environmental covariates and TBIs, soil particle inversion models were developed using the extreme learning machine (ELM), backpropagation neural networks (BP), neural networks optimized with the sparrow search algorithm (SSA-BP), and neural networks optimized with the sparrow search algorithm enhanced by Sine chaos mapping (Sine-SSA-BP).Results and discussionThe results demonstrated that (1) the Boruta algorithm identified key environmental covariates that affect specific soil particle components; (2) there was significant variation in the correlation between different TBIs and soil particle content, with absolute correlation coefficients ranging from 0.225 to 0.852; (3) the estimation models established by the four machine learning algorithms performed well in predicting soil particle content, particularly for silt (R2: 0.664-0.858, RMSE: 11.107-17.128) and clay (R2: 0.444-0.857, RMSE: 0.550-1.405), for which higher accuracy was achieved; and (4) compared with the traditional ELM (R2: 0.422-0.664), BP (R2: 0.487-0.673) and SSA-BP models (R2: 0.625-0.777), the Sine-SSA-BP model showed a significant improvement in prediction accuracy, with the highest R2 reaching 0.858.ConclusionCompared to the traditional ELM, BP and SSA-BP models, the Sine-SSA-BP model significantly excelled in predicting soil particle content, offering innovative insights and robust support for soil texture classification and management.
英文关键词	Soil particle content Hyperspectral Backpropagation neural network Chaos mapping
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001297251400001
WOS关键词	SPATIAL PREDICTION ; SPECTROSCOPY ; CLAY ; OPTIMIZATION ; COMBINATION ; ALGORITHM ; SELECTION ; INDEXES ; PH
WOS类目	Environmental Sciences ; Soil Science
WOS研究方向	Environmental Sciences & Ecology ; Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/404705
推荐引用方式 GB/T 7714	Wang, Xiao,Ding, Jianli,Han, Lijing,et al. Enhancing soil particle content prediction accuracy: advanced hyperspectral analysis and machine learning models[J],2024.
APA	Wang, Xiao,Ding, Jianli,Han, Lijing,Tan, Jiao,&Ge, Xiangyu.(2024).Enhancing soil particle content prediction accuracy: advanced hyperspectral analysis and machine learning models.JOURNAL OF SOILS AND SEDIMENTS.
MLA	Wang, Xiao,et al."Enhancing soil particle content prediction accuracy: advanced hyperspectral analysis and machine learning models".JOURNAL OF SOILS AND SEDIMENTS (2024).