干旱区生态环境知识资源中心(Arid): Application of proximal sensing approach to predict cation exchange capacity of calcareous soils using linear and nonlinear data mining algorithms

Arid

DOI	10.1007/s11368-024-03825-7
	Application of proximal sensing approach to predict cation exchange capacity of calcareous soils using linear and nonlinear data mining algorithms
	Karami, Ali; Moosavi, Ali Akbar; Pourghasemi, Hamid Reza; Ronaghi, Abdolmajid; Ghasemi-Fasaei, Reza; Lado, Marcos
通讯作者	Moosavi, AA ; Pourghasemi, HR
来源期刊	JOURNAL OF SOILS AND SEDIMENTS
ISSN	1439-0108
EISSN	1614-7480
出版年	2024
卷号	24 期号:6 页码:2248-2267
英文摘要	Purpose The cation exchange capacity (CEC) is a pivotal soil attribute that influences soil chemistry, fertility, and productivity. Nevertheless, the conventional techniques employed for CEC measurements present challenges in terms of complexity, cost, and laboriousness. Hence, there is a demand for expedited, cost-effective, streamlined alternative methodologies that can yield accurate outcomes. The objective of this study was to employ and compare various techniques, including Pedotransfer Functions (PTFs) based on fundamental soil properties, support vector regression (SVR), sequential orthogonalized partial least squares (SOPLS) as a multiblock data analysis method, and Spectrotransfer Function (SPTF) utilizing visible-near infrared (VNIR) and mid infrared (MIR) diagnostic wavelengths to estimate the CEC of calcareous soils with diverse land uses in the semi-arid region of Fars province, Iran. Materials and methods A total of 130 samples were gathered from the soils of the study region, CEC was measured using sodium acetate, and the spectral reflectance in the VNIR and transmission in the MIR regions were measured, and prediction models were created using linear support vector regression (L-SVR), radial basis function support vector regression (RBF-SVR), partial least squares regression (PLSR), and multiblock data analysis algorithms, after different spectral preprocessing methods. Results and discussion The results generally indicated that spectroscopy models performed better than PTFs in predicting CEC with the multiblock SOPLS showing the best results (R-2 = 0.92, RMSE = 1.67 cmol((+)) kg(-1), and RPIQ = 4.34). The performance of the models followed the order: SOPLS > SPTF > L-SVR > RBF-SVR. Conclusion Our findings indicate that spectroscopy coupled with SOPLS analysis can be a robust, viable, fast, cheap, and efficient alternative assessment method with acceptable accuracy for estimating soil CEC in calcareous soils, instead of the difficult, costly, and cumbersome conventional measurement approaches or other estimation methods.
英文关键词	Multiblock analysis Partial least squares regression Pedotransfer functions Spectrotransfer function Stepwise multiple linear regression Support vector regression Soil spectroscopy
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001243301400001
WOS关键词	PARTIAL LEAST-SQUARES ; NEAR-INFRARED SPECTROSCOPY ; REFLECTANCE SPECTROSCOPY ; MULTIPLE-REGRESSION ; ORGANIC-MATTER ; NIR ; PARAMETERS ; MODELS ; VIOLET ; AREA
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/2XILL650/404700
推荐引用方式 GB/T 7714	Karami, Ali,Moosavi, Ali Akbar,Pourghasemi, Hamid Reza,et al. Application of proximal sensing approach to predict cation exchange capacity of calcareous soils using linear and nonlinear data mining algorithms[J],2024,24(6):2248-2267.
APA	Karami, Ali,Moosavi, Ali Akbar,Pourghasemi, Hamid Reza,Ronaghi, Abdolmajid,Ghasemi-Fasaei, Reza,&Lado, Marcos.(2024).Application of proximal sensing approach to predict cation exchange capacity of calcareous soils using linear and nonlinear data mining algorithms.JOURNAL OF SOILS AND SEDIMENTS,24(6),2248-2267.
MLA	Karami, Ali,et al."Application of proximal sensing approach to predict cation exchange capacity of calcareous soils using linear and nonlinear data mining algorithms".JOURNAL OF SOILS AND SEDIMENTS 24.6(2024):2248-2267.