干旱区生态环境知识资源中心(Arid): Incorporating environmental variables, remote and proximal sensing data for digital soil mapping of USDA soil great groups

Arid

DOI	10.1080/01431161.2020.1763506
	Incorporating environmental variables, remote and proximal sensing data for digital soil mapping of USDA soil great groups
	Asgari, Najmeh; Ayoubi, Shamsollah; Jafari, Azam; Dematte, Jose A. M.
通讯作者	Ayoubi, S
来源期刊	INTERNATIONAL JOURNAL OF REMOTE SENSING
ISSN	0143-1161
EISSN	1366-5901
出版年	2020
卷号	41 期号:19 页码:7624-7648
英文摘要	The present study was conducted to evaluate the effectiveness of combining proximal, and remote sensing with environmental variables for predicting USDA (United States Department of Agriculture) soil great groups (the third hierarchical level of USDA soil classification system) in a semi-arid region located at Jouneqan district in Chaharmahal & Bakhtiary province, Iran. Accordingly, two predictive models, including support vector machine (SVM), and multinomial logistic regression (MLR) using different covariates, including lithology, geomorphology, remote sensing-derived vegetation indices, DEM (digital elevation model)-derived attributes, diffuse reflectance spectroscopy-derived soil colour qualifiers, and magnetic susceptibility were examined. A total of 102 soil profiles were excavated, described, and classified up to the great group level, and soil samples were collected from various genetic horizons. The cross validation leave-one-out (LOOCV) was used as validation approach, and the performance of the models was assessed using the kappa coefficient (kappa), and overall accuracy. Results showed that considering the kappa values (kappa = 0.6-0.8), the classification performance identified as substantial for both MLR, and SVM when geospatial data, soil colour qualifiers, and magnetic susceptibility were used together as predictors. However, the MLR classifier outperformed SVM (kappa: 0.78, and 0.66, respectively). Chroma, normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), geomorphology map, and some terrain attributes, including slope were among the most important predictors. Our findings confirmed that combining geospatial data, and proximal sensing information in multivariate classification algorithms could improve soil classification accuracy in a quick, and cost-efficient way.
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000551322300001
WOS关键词	MAGNETIC-SUSCEPTIBILITY MEASUREMENTS ; MULTINOMIAL LOGISTIC-REGRESSION ; REFLECTANCE SPECTRA ; PHYSICAL-PROPERTIES ; SPATIAL PREDICTION ; SEMIARID REGION ; HEAVY-METALS ; COLOR ; DRAINAGE ; DEPTH
WOS类目	Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/325141
作者单位	[Asgari, Najmeh; Ayoubi, Shamsollah] Isfahan Univ Technol, Coll Agr, Dept Soil Sci, Esfahan 84115683111, Iran; [Jafari, Azam] Shahid Bahonar Univ Kerman, Coll Agr, Dept Soil Sci, Kerman, Iran; [Dematte, Jose A. M.] Coll Agr Luiz Queiroz, Dept Soil Sci, Piracicaba, Brazil
推荐引用方式 GB/T 7714	Asgari, Najmeh,Ayoubi, Shamsollah,Jafari, Azam,et al. Incorporating environmental variables, remote and proximal sensing data for digital soil mapping of USDA soil great groups[J],2020,41(19):7624-7648.
APA	Asgari, Najmeh,Ayoubi, Shamsollah,Jafari, Azam,&Dematte, Jose A. M..(2020).Incorporating environmental variables, remote and proximal sensing data for digital soil mapping of USDA soil great groups.INTERNATIONAL JOURNAL OF REMOTE SENSING,41(19),7624-7648.
MLA	Asgari, Najmeh,et al."Incorporating environmental variables, remote and proximal sensing data for digital soil mapping of USDA soil great groups".INTERNATIONAL JOURNAL OF REMOTE SENSING 41.19(2020):7624-7648.