干旱区生态环境知识资源中心(Arid): Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas

Arid

DOI	10.3390/agriculture13050976
	Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas
	Suleymanov, Azamat; Gabbasova, Ilyusya; Komissarov, Mikhail; Suleymanov, Ruslan; Garipov, Timur; Tuktarova, Iren; Belan, Larisa
通讯作者	Suleymanov, A
来源期刊	AGRICULTURE-BASEL
EISSN	2077-0472
出版年	2023
卷号	13 期号:5
英文摘要	The problem of salinization/spreading of saline soils is becoming more urgent in many regions of the world, especially in context of climate change. The monitoring of salt-affected soils' properties is a necessary procedure in land management and irrigation planning and is aimed to obtain high crop harvest and reduce degradation processes. In this work, a machine learning method was applied for modeling of the spatial distribution of topsoil (0-20 cm) properties-in particular: soil organic carbon (SOC), pH, and salt content (dry residue). A random forest (RF) machine learning approach was used in combination with environmental variables to predict soil properties in a semi-arid area (Trans-Ural steppe zone). Soil, salinity, and texture maps; topography attributes; and remote sensing data (RSD) were used as predictors. The coefficient of determination (R-2) and the root mean square error (RMSE) were used to estimate the performance of the RF model. The cross-validation result showed that the RF model achieved an R-2 of 0.59 and an RMSE of 0.68 for SOM; 0.36 and 0.65, respectively, for soil pH; and 0.78 and 1.21, respectively for dry residue prediction. The SOC content ranged from 0.8 to 2.8%, with an average value of 1.9%; soil pH ranged from 5.9 to 8.4, with an average of 7.2; dry residue varied greatly from 0.04 to 16.8%, with an average value of 1.3%. A variable importance analysis indicated that remote sensing variables (salinity indices and NDVI) were dominant in the spatial prediction of soil parameters. The importance of RSD for evaluating saline soils and their properties is explained by their absorption characteristics/reflectivity in the visible and near-infrared spectra. Solonchak soils are distinguished by a salt crust on the land surface and, as a result, reduced SOC contents and vegetation biomass. However, the change in saline and non-saline soils over a short distance with mosaic structure of soil cover requires high-resolution RSD or aerial images obtained from unmanned aerial vehicle/drones for successful digital mapping of soil parameters. The presented results provide an effective method to estimate soil properties in saline landscapes for further land management/reclamation planning of degraded soils in arid and semi-arid regions.
英文关键词	digital soil mapping dry residue machine learning pH salt-affected soil soil organic carbon
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000994224000001
WOS关键词	ORGANIC-CARBON STOCKS ; SALT-AFFECTED SOILS ; SALINIZATION ; DEGRADATION ; CLIMATE ; REGIONS ; RUSSIA ; MATTER ; DETECT ; THREAT
WOS类目	Agronomy
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/395147
推荐引用方式 GB/T 7714	Suleymanov, Azamat,Gabbasova, Ilyusya,Komissarov, Mikhail,et al. Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas[J],2023,13(5).
APA	Suleymanov, Azamat.,Gabbasova, Ilyusya.,Komissarov, Mikhail.,Suleymanov, Ruslan.,Garipov, Timur.,...&Belan, Larisa.(2023).Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas.AGRICULTURE-BASEL,13(5).
MLA	Suleymanov, Azamat,et al."Random Forest Modeling of Soil Properties in Saline Semi-Arid Areas".AGRICULTURE-BASEL 13.5(2023).