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DOI	10.1016/j.catena.2019.104424
	Conventional and digital soil mapping in Iran: Past, present, and future
	Zeraatpisheh, Mojtaba 1; Jafari, Azam 2; Bodaghabadi, Mohsen Bagheri 3; Ayoubi, Shamsollah 4; Taghizadeh-Mehrjardi, Ruhollah 5,6; Toomanian, Norair 7; Kerry, Ruth 8; Xu, Ming 1
通讯作者	Zeraatpisheh, Mojtaba ; Xu, Ming
来源期刊	CATENA
ISSN	0341-8162
EISSN	1872-6887
出版年	2020
卷号	188
英文摘要	Demand for accurate soil information is increasing for various applications. This paper investigates the history of soil survey in Iran, particularly more recent developments in the use of digital soil mapping (DSM) approaches rather than conventional soil mapping (CSM) methods. A 2000-2019 literature search of articles on DSM of areas of Iran in international journals found 40 studies. These showed an increase in frequency over time, and most were completed in the arid and semi-arid regions of central Iran. Artificial Neural Networks (ANN), Random Forests (RF), and Multinomial Logistic Regression (MnLR) were the most commonly applied models for predicting soil classes and properties and ANN performed best in most comparative studies. Given the scale of inquiry of most studies (local or regional), quantitative environmental variables such as terrain attributes and remote sensing data were frequently used whereas qualitative variables such as geomorphology, geology, land use, and legacy soil maps were rarely used. The literature review of CSM showed that this method is incapable of defining the spatial distribution of soils and also provides a lower accuracy than DSM method. This review has identified research gaps that need filling. In Iran, coherent national scale DSM with consistent methodology is needed to update legacy soil maps, and to apply DSM in forestlands, hillslopes, deserts, and mountainous regions which have largely been ignored in recent DSM studies. This review should also be useful for producing more local and regional digital soil maps more rapidly as it helps show which covariates and mathematical methods have been best suited to this scale of DSM in Iran.
英文关键词	Soil survey Environmental variables Soil properties and classes Legacy soil map Soil-landscape modeling Machine learning
类型	Article
语种	英语
国家	Peoples R China ; Iran ; Germany ; USA
收录类别	SCI-E
WOS记录号	WOS:000518488500003
WOS关键词	REMOTELY-SENSED DATA ; SPATIAL VARIABILITY ; LOGISTIC-REGRESSION ; SEMIARID REGION ; QUALITY INDEXES ; NEURAL-NETWORKS ; ORGANIC-MATTER ; BORUJEN REGION ; GREAT GROUPS ; ARID REGION
WOS类目	Geosciences, Multidisciplinary ; Soil Science ; Water Resources
WOS研究方向	Geology ; Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/314237
作者单位	1.Henan Univ, Coll Environm & Planning, Key Lab Geospatial Technol Middle & Lower Yellow, Kaifeng 475004, Peoples R China; 2.Shahid Bahonar Univ Kerman, Coll Agr, Dept Soil Sci, Kerman, Iran; 3.AREEO, Soil & Water Res Inst, Karaj, Iran; 4.Isfahan Univ Technol, Coll Agr, Dept Soil Sci, Esfahan 8415683111, Iran; 5.Ardakan Univ, Fac Agr & Nat Resources, Ardakan 8951656767, Iran; 6.Eberhard Karls Univ Tubingen, Inst Geog, Soil Sci & Geomorphol, D-72070 Tubingen, Germany; 7.AREEO, Soil & Water Res Dept, Isfahan Agr & Nat Resources Res & Educ Ctr, Esfahan 81785199, Iran; 8.Brigham Young Univ, Dept Geog, Provo, UT 84602 USA
推荐引用方式 GB/T 7714	Zeraatpisheh, Mojtaba,Jafari, Azam,Bodaghabadi, Mohsen Bagheri,et al. Conventional and digital soil mapping in Iran: Past, present, and future[J],2020,188.
APA	Zeraatpisheh, Mojtaba.,Jafari, Azam.,Bodaghabadi, Mohsen Bagheri.,Ayoubi, Shamsollah.,Taghizadeh-Mehrjardi, Ruhollah.,...&Xu, Ming.(2020).Conventional and digital soil mapping in Iran: Past, present, and future.CATENA,188.
MLA	Zeraatpisheh, Mojtaba,et al."Conventional and digital soil mapping in Iran: Past, present, and future".CATENA 188(2020).