干旱区生态环境知识资源中心(Arid): Digital soil mapping for soil types using machine learning approaches at the landscape scale in the arid regions of Iran

Arid

DOI	10.1016/j.asr.2024.04.042
	Digital soil mapping for soil types using machine learning approaches at the landscape scale in the arid regions of Iran
	Manteghi, Shaho; Moravej, Kamran; Mousavi, Seyed Roohollah; Delavar, Mohammad Amir; Mastinu, Andrea
通讯作者	Moravej, K
来源期刊	ADVANCES IN SPACE RESEARCH
ISSN	0273-1177
EISSN	1879-1948
出版年	2024
卷号	74 期号:1 页码:1-16
英文摘要	The aims of this research are (i) to compare random forest (RF), boosted regression tree (BRT), and multinomial logistic regression (MnLR) models to prepare the prediction maps of soil great group and subgroup levels, (ii) determination of the most important environmental covariates influencing the production of digital soil mapping (DSM) in an arid climate, (iii) to evaluate the efficiency of spectra indices extracted from Sentinel-2A digital images and data capability of ALOS-PALSAR radar data, and (iv) investigating the effect of sub-surface genetic horizons in the modeling of different types of soil map classes distribution. The principal component analysis method was employed to select the best set from the pool of environmental covariates (n = 46) such as geomorphometric parameters (GPs), RS indices, and diagnostic soil properties (DSP). The relative importance results indicate that Gypsic (GYP) subsurface horizon, standardized height (StH), slope length (SL), and normalized different vegetation index (NDVI) had an important role in the prediction of soil classes compared to the other selected covariates. DSM methodology was used in this research by incorporating of RF model and representative soil-forming factors that can be used for preparing the maps of soil classes in low-relief areas with a similar soil-landscape relationship. Totally, in this study places a spotlight on the profound impact of sub-surface genetic horizons, shedding light on their pivotal role in accurately modeling soil class distributions. These findings not only advance our comprehension of soil variability in arid regions but also hold immense implications for the burgeoning field of pedometrics. (c) 2024 COSPAR. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
英文关键词	Alluvial landform Arid landscape Boosted regression tree Environmental covariates Random forest
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001244290900001
WOS关键词	MULTINOMIAL LOGISTIC-REGRESSION ; SPATIAL PREDICTION ; GREAT GROUPS ; INDEX ; VEGETATION ; CATCHMENT ; NITROGEN ; DEPTH ; MAP
WOS类目	Engineering, Aerospace ; Astronomy & Astrophysics ; Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS研究方向	Engineering ; Astronomy & Astrophysics ; Geology ; Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/402614
推荐引用方式 GB/T 7714	Manteghi, Shaho,Moravej, Kamran,Mousavi, Seyed Roohollah,et al. Digital soil mapping for soil types using machine learning approaches at the landscape scale in the arid regions of Iran[J],2024,74(1):1-16.
APA	Manteghi, Shaho,Moravej, Kamran,Mousavi, Seyed Roohollah,Delavar, Mohammad Amir,&Mastinu, Andrea.(2024).Digital soil mapping for soil types using machine learning approaches at the landscape scale in the arid regions of Iran.ADVANCES IN SPACE RESEARCH,74(1),1-16.
MLA	Manteghi, Shaho,et al."Digital soil mapping for soil types using machine learning approaches at the landscape scale in the arid regions of Iran".ADVANCES IN SPACE RESEARCH 74.1(2024):1-16.