干旱区生态环境知识资源中心(Arid): Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy

Arid

DOI	10.1016/j.jenvman.2023.118854
	Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy
	Lotfollahi, Leila; Delavar, Mohammad Amir; Biswas, Asim; Fatehi, Shahrokh; Scholten, Thomas
通讯作者	Delavar, MA
来源期刊	JOURNAL OF ENVIRONMENTAL MANAGEMENT
ISSN	0301-4797
EISSN	1095-8630
出版年	2023
卷号	345
英文摘要	Drought and the impacts of climate change have led to an escalation in soil salinity and alkalinity across various regions worldwide, including Iran. The Chahardowli Plain in western Iran, in particular, has witnessed a significant intensification of this phenomenon over the past decade. Consequently, modeling of soil attributes that serve as indicators of soil salinity and alkalinity became a priority in this region. To date, only a limited number of studies have been conducted to assess indicators of salinity and alkalinity through spectrometry across diverse spectral ranges. The spectral ranges encompassing mid-infrared (mid-IR), visible, and near-infrared (vis-NIR) spectroscopy were employed to estimate soil properties including sodium adsorption ratio (SAR), exchangeable sodium ratio (ESR), exchangeable sodium percentage (ESP), pH, and electrical conductivity (EC). Five distinct models were employed: Partial Least Squares Regression (PLSR), bootstrapping aggregation PLSR (BgPLSR), Memory-Based Learning (MBL), Random Forest (RF), and Cubist. The calibration and assessment of model performance were carried out using several key metrics including Ratio of Performance to Deviation (RPD) and the coefficient of determination (R2). Analysis of the outcomes indicates that the accuracy and precision of the mid-IR spectra surpassed that of vis-NIR spectra, except for pH, which exhibited a superior RPD compared to other properties. Notably, in the prediction of pH utilizing vis-NIR reflectance spectra, the BgPLSR model exhibited the highest accuracy and precision, boasting an RPD value of 2.56. In the domain of EC prediction, the PLSR model yielded an RPD of 2.64. For SAR, the MBL model achieved an RPD of 2.70, while ESR prediction benefited from the MBL model with an impressive RPD of 4.36. Likewise, the MBL model demonstrated remarkable precision and accuracy in ESP prediction, garnering an RPD of 4.41. The MBL model's efficacy in forecasting with limited datasets was notably pronounced among the models considered. This study underscores the valuable role of spectral predictions in facilitating the work of soil surveyors in gauging salinity and alkalinity indicators. It is recommended that the integration of spectrometry-based salinity and alkalinity predictions be incorporated into forthcoming soil mapping endeavors within semi-arid and arid regions.
英文关键词	Bootstrapping aggregation PLSR FT-IR Memory-based learning Reflectance spectra Soil alkalinity Soil salinity
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001069058500001
WOS关键词	NEAR-INFRARED SPECTROSCOPY ; PARTIAL LEAST-SQUARES ; DIFFUSE-REFLECTANCE SPECTROSCOPY ; SODIUM ADSORPTION RATIO ; EXCHANGEABLE SODIUM ; ULTRA-VIOLET ; NIR ; CARBON ; REGRESSION ; LIBRARIES
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/397296
推荐引用方式 GB/T 7714	Lotfollahi, Leila,Delavar, Mohammad Amir,Biswas, Asim,et al. Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy[J],2023,345.
APA	Lotfollahi, Leila,Delavar, Mohammad Amir,Biswas, Asim,Fatehi, Shahrokh,&Scholten, Thomas.(2023).Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy.JOURNAL OF ENVIRONMENTAL MANAGEMENT,345.
MLA	Lotfollahi, Leila,et al."Spectral prediction of soil salinity and alkalinity indicators using visible, near-, and mid-infrared spectroscopy".JOURNAL OF ENVIRONMENTAL MANAGEMENT 345(2023).