干旱区生态环境知识资源中心(Arid): Digital mapping and spatial modeling of some soil physical and mechanical properties in a semi-arid region of Iran

Arid

DOI	10.1007/s10661-023-11980-6
	Digital mapping and spatial modeling of some soil physical and mechanical properties in a semi-arid region of Iran
	Khosravani, Pegah; Baghernejad, Majid; Moosavi, Ali Akbar; Rezaei, Meisam
通讯作者	Baghernejad, M
来源期刊	ENVIRONMENTAL MONITORING AND ASSESSMENT
ISSN	0167-6369
EISSN	1573-2959
出版年	2023
卷号	195 期号:11
英文摘要	The soil's physical and mechanical (SPM) properties have significant impacts on soil processes, such as water flow, nutrient movement, aeration, microbial activity, erosion, and root growth. To digitally map some SPM properties at four global standard depths, three machine learning algorithms (MLA), namely, random forest, Cubist, and k-nearest neighbor, were employed. A total of 200-point observation was designed with the aim of a field survey across the Marvdasht Plain in Fars Province, Iran. After sampling from topsoil (0 to 30 cm) and subsoil depths (30 to 60 cm), the samples were transferred to the laboratory to determine the mean weight diameter (MWD) and geometric mean diameter (GMD) of aggregates in the laboratory. In addition, shear strength (SS) and penetration resistance (PR) were measured directly during the field survey. In parallel, 79 environmental factors were prepared from topographic and remote sensing data. Four soil variables were also included in the modeling process, as they were co-located with SPM properties based on expert opinion. For selecting the most influential covariates, the variance inflation factor (VIF) and Boruta methods were employed. Two covariate dataset scenarios were used to assess the impact of soil and environmental factors on the modeling of SPM properties including SPM and environmental covariates (scenario 1) and SPM, environmental covariates, and soil variables (scenario 2). From all covariates, nine soil and environmental factors were selected for modeling the SPM properties, of which four of them were the soil variables, three were related to remote sensing, and two factors had topographic sources. The results indicated that scenario 2 outperformed in all standard depths. The findings suggested that clay and SOM are key factors in predicting SPM, highlighting the importance of considering soil variables in addition to environmental covariates for enhancing the accuracy of machine learning prediction. The k-nearest neighbor algorithm was found to be highly effective in predicting SPM, while the random forest algorithm yielded the highest R2 value (0.92) for penetration resistance properties at 15-30 depth. Overall, the approach used in this research has the potential to be extended beyond the Marvdasht Plain of Fars Province, Iran, as well as to other regions worldwide with comparable soil-forming factors. Moreover, this study provides a valuable framework for the digital mapping of SPM properties, serving as a guide for future studies seeking to predict SPM properties. Globally, the output of this research has important significance for soil management and conservation efforts and can facilitate the development of sustainable agricultural practices.
英文关键词	Cubist Environmental covariate k-nearest neighbor Machine learning algorithms Random forest Spline function
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001093878300003
WOS关键词	ORGANIC-CARBON ; LAND-USE ; AGGREGATE STABILITY ; DEPTH FUNCTIONS ; SALINITY ; VARIABILITY ; ERODIBILITY ; NITROGEN ; MATTER ; AREA
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/2XILL650/396191
推荐引用方式 GB/T 7714	Khosravani, Pegah,Baghernejad, Majid,Moosavi, Ali Akbar,et al. Digital mapping and spatial modeling of some soil physical and mechanical properties in a semi-arid region of Iran[J],2023,195(11).
APA	Khosravani, Pegah,Baghernejad, Majid,Moosavi, Ali Akbar,&Rezaei, Meisam.(2023).Digital mapping and spatial modeling of some soil physical and mechanical properties in a semi-arid region of Iran.ENVIRONMENTAL MONITORING AND ASSESSMENT,195(11).
MLA	Khosravani, Pegah,et al."Digital mapping and spatial modeling of some soil physical and mechanical properties in a semi-arid region of Iran".ENVIRONMENTAL MONITORING AND ASSESSMENT 195.11(2023).