干旱区生态环境知识资源中心(Arid): Tree-based algorithms for spatial modeling of soil particle distribution in arid and semi-arid region

Arid

DOI	10.1007/s10661-024-12431-6
	Tree-based algorithms for spatial modeling of soil particle distribution in arid and semi-arid region
	Abakay, Osman; Kilic, Mirac; Gunal, Hikmet; Kilic, Orhan Mete
通讯作者	Kiliç, M
来源期刊	ENVIRONMENTAL MONITORING AND ASSESSMENT
ISSN	0167-6369
EISSN	1573-2959
出版年	2024
卷号	196 期号:3
英文摘要	Accurate estimation of particle size distribution across a large area is crucial for proper soil management and conservation, ensuring compatibility with capabilities and enabling better selection and adaptation of precision agricultural techniques. The study investigated the performance of tree-based models, ranging from simpler options like CART to sophisticated ones like XGBoost, in predicting soil texture over a wide geographic region. Models were constructed using remotely sensed plant and soil indexes as covariates. Variable selection employed the Boruta approach. Training and testing data for machine learning models consisted of particle size distribution results from 622 surface soil samples collected in southeastern Turkey. The XGBoostClay model emerged as the most accurate predictor, with an R2 value of 0.74. Its superiority was further underlined by a 21.36% relative improvement in XGBoostClay RMSE compared to RFClay and 44.5% compared to CARTClay. Similarly, the R2 values for XGBoostSilt and XGBoostSand models reached 0.71 and 0.75 in predicting sand and silt content, respectively. Among the considered covariates, the normalized ratio vegetation index and slope angle had the highest impact on clay content (21%), followed by topographic position index and simple ratio clay index (20%), while terrain ruggedness index had the least impact (18%). These results highlight the effectiveness of Boruta approach in selecting an adequate number of variables for digital mapping, suggesting its potential as a viable option in this field. Furthermore, the findings of this study suggest that remote sensing data can effectively contribute to digital soil mapping, with tree-based model development leading to improved prediction performance.
英文关键词	Digital soil mapping Sand Clay Silt Remote sensing Particle size distribution Model Data mining
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:001162183500007
WOS关键词	ORGANIC-CARBON ; MOISTURE-CONTENT ; SHEAR-STRENGTH ; SIZE FRACTIONS ; LAND-USE ; PREDICTION ; VEGETATION ; QUALITY ; BEAMS ; GIS
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/2XILL650/403592
推荐引用方式 GB/T 7714	Abakay, Osman,Kilic, Mirac,Gunal, Hikmet,et al. Tree-based algorithms for spatial modeling of soil particle distribution in arid and semi-arid region[J],2024,196(3).
APA	Abakay, Osman,Kilic, Mirac,Gunal, Hikmet,&Kilic, Orhan Mete.(2024).Tree-based algorithms for spatial modeling of soil particle distribution in arid and semi-arid region.ENVIRONMENTAL MONITORING AND ASSESSMENT,196(3).
MLA	Abakay, Osman,et al."Tree-based algorithms for spatial modeling of soil particle distribution in arid and semi-arid region".ENVIRONMENTAL MONITORING AND ASSESSMENT 196.3(2024).