干旱区生态环境知识资源中心(Arid): Predicting spatial distribution of soil organic matter using regression approaches at the regional scale (Eastern Azerbaijan, Iran)

Arid

DOI	10.1007/s10661-021-09416-0
	Predicting spatial distribution of soil organic matter using regression approaches at the regional scale (Eastern Azerbaijan, Iran)
	Ebrahimzadeh, Golnaz; Mahabadi, Nafiseh Yaghmaeian; Aqdam, Kamal Khosravi; Asadzadeh, Farrokh
通讯作者	Mahabadi, NY (corresponding author), Univ Guilan, Fac Agr Sci, Dept Soil Sci, Rasht, Iran.
来源期刊	ENVIRONMENTAL MONITORING AND ASSESSMENT
ISSN	0167-6369
EISSN	1573-2959
出版年	2021
卷号	193 期号:9
英文摘要	Soil organic matter (SOM) is one of the important factors in arid and semiarid areas, which describes the soil quality. Spatial estimation of SOM is important to understand the SOM storage and the emphasis of the SOM in the global carbon cycle and environmental issues. Mapping of SOM content can have significant uses in environmental modeling. In the current study, various methods have been evaluated for estimating the SOM content through soil samples and using auxiliary variables in the west of Eastern Azerbaijan province, Iran. In this study, support vector machine (SVM), multi-factor regression (MFR), and multi-factor weighted regression model (MWRM) approaches have been suggested for predicting and investigating the spatial distribution of SOM. In total, 155 surface soil samples (from the depth of 0 to 30 cm) were obtained. These soil samples were randomly divided into training data set (105 soil samples) and testing data set (50 samples). According to the results, SOM is affected by soil properties as well as environmental factors (normalized difference vegetation index (NDVI)). In this study, clay, silt/sand, NDVI, and soil moisture were used as auxiliary variables in the estimation of SOM. Three methods were compared to determine a suitable method for spatial estimation of SOM, and results showed that SVM has the lowest estimation error (RMSE = 0.100, MAE = 0.07, and MRE = 3.32) and highest regression coefficient (R-2 = 0.719) during SOM estimation. The present results show the indirect effect of elevation by controlling auxiliary variables and confirm the importance of auxiliary variables in spatial distribution patterns of SOM.
英文关键词	Soil organic matter Auxiliary variables Support vector machine Multi-factor weighted regression model Kriging
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000694254300005
WOS关键词	SUPPORT VECTOR MACHINES ; CARBON STOCKS ; RANDOM FOREST ; LAND-USE ; CHEMICAL-PROPERTIES ; NEURAL-NETWORKS ; TEMPERATURE ; VARIABILITY ; DECOMPOSITION ; CLIMATE
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/2XILL650/368470
作者单位	[Ebrahimzadeh, Golnaz; Mahabadi, Nafiseh Yaghmaeian; Aqdam, Kamal Khosravi] Univ Guilan, Fac Agr Sci, Dept Soil Sci, Rasht, Iran; [Asadzadeh, Farrokh] Urmia Univ, Fac Agr, Dept Soil Sci, Orumiyeh, Iran
推荐引用方式 GB/T 7714	Ebrahimzadeh, Golnaz,Mahabadi, Nafiseh Yaghmaeian,Aqdam, Kamal Khosravi,et al. Predicting spatial distribution of soil organic matter using regression approaches at the regional scale (Eastern Azerbaijan, Iran)[J],2021,193(9).
APA	Ebrahimzadeh, Golnaz,Mahabadi, Nafiseh Yaghmaeian,Aqdam, Kamal Khosravi,&Asadzadeh, Farrokh.(2021).Predicting spatial distribution of soil organic matter using regression approaches at the regional scale (Eastern Azerbaijan, Iran).ENVIRONMENTAL MONITORING AND ASSESSMENT,193(9).
MLA	Ebrahimzadeh, Golnaz,et al."Predicting spatial distribution of soil organic matter using regression approaches at the regional scale (Eastern Azerbaijan, Iran)".ENVIRONMENTAL MONITORING AND ASSESSMENT 193.9(2021).