干旱区生态环境知识资源中心(Arid): Integrating proximal soil sensing data and environmental variables to enhance the prediction accuracy for soil salinity and sodicity in a region of Xinjiang Province, China

Arid

DOI	10.1016/j.jenvman.2024.121311
	Integrating proximal soil sensing data and environmental variables to enhance the prediction accuracy for soil salinity and sodicity in a region of Xinjiang Province, China
	Zhao, Shuai; Ayoubi, Shamsollah; Mousavi, Seyed Roohollah; Mireei, Seyed Ahmad; Shahpouri, Faezeh; Wu, Shi-xin; Chen, Chun-bo; Zhao, Zhen-yong; Tian, Chang-yan
通讯作者	Ayoubi, S
来源期刊	JOURNAL OF ENVIRONMENTAL MANAGEMENT
ISSN	0301-4797
EISSN	1095-8630
出版年	2024
卷号	364
英文摘要	Soil salinization and sodification, the primary causes of land degradation and desertification in arid and semiarid regions, demand effective monitoring for sustainable land management. This study explores the utility of partial least square (PLS) latent variables (LVs) derived from visible and near -infrared (Vis-NIR) spectroscopy, combined with remote sensing (RS) and auxiliary variables, to predict electrical conductivity (EC) and sodium absorption ratio (SAR) in northern Xinjiang, China. Using 90 soil samples from the Karamay district, machine learning models (Random Forest, Support Vector Regression, Cubist) were tested in four scenarios. Modeling results showed that RS and Land use alone were unreliable predictors, but the addition of topographic attributes significantly improved the prediction accuracy for both EC and SAR. The incorporation of PLS LVs derived from Vis-NIR spectroscopy led to the highest performance by the Random Forest model for EC (CCC = 0.83, R 2 = 0.80, nRMSE = 0.48, RPD = 2.12) and SAR (CCC = 0.78, R 2 = 0.74, nRMSE = 0.58, RPD = 2.25). The variable importance analysis identified PLS LVs, certain topographic attributes (e.g., valley depth, elevation, channel network base level, diffuse insolation), and specific RS data (i.e., polarization index of VV + VH) as the most influential predictors in the study area. This study affirms the efficiency of Vis-NIR data for digital soil mapping, offering a cost-effective solution. In conclusion, the integration of proximal soil sensing techniques and highly relevant topographic attributes with the RF model has the potential to yield a reliable spatial model for mapping soil EC and SAR. This integrated approach allows for the delineation of hazardous zones, which in turn enables the consideration of best management practices and contributes to the reduction of the risk of degradation in saltaffected and sodicity-affected soils.
英文关键词	Machine learning models Salinity and sodicity maps Digital soil mapping Arid region
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001254835400001
WOS关键词	ELECTROMAGNETIC INDUCTION ; SPECTROSCOPY ; SALINIZATION
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/2XILL650/404479
推荐引用方式 GB/T 7714	Zhao, Shuai,Ayoubi, Shamsollah,Mousavi, Seyed Roohollah,et al. Integrating proximal soil sensing data and environmental variables to enhance the prediction accuracy for soil salinity and sodicity in a region of Xinjiang Province, China[J],2024,364.
APA	Zhao, Shuai.,Ayoubi, Shamsollah.,Mousavi, Seyed Roohollah.,Mireei, Seyed Ahmad.,Shahpouri, Faezeh.,...&Tian, Chang-yan.(2024).Integrating proximal soil sensing data and environmental variables to enhance the prediction accuracy for soil salinity and sodicity in a region of Xinjiang Province, China.JOURNAL OF ENVIRONMENTAL MANAGEMENT,364.
MLA	Zhao, Shuai,et al."Integrating proximal soil sensing data and environmental variables to enhance the prediction accuracy for soil salinity and sodicity in a region of Xinjiang Province, China".JOURNAL OF ENVIRONMENTAL MANAGEMENT 364(2024).