干旱区生态环境知识资源中心(Arid): The Spatial Prediction of Soil Texture Fractions in Arid Regions of Iran

Arid

DOI	10.3390/soilsystems3040065
	The Spatial Prediction of Soil Texture Fractions in Arid Regions of Iran
	Mehrabi-Gohari, Elham; Matinfar, Hamid Reza; Jafari, Azam; Taghizadeh-Mehrjardi, Ruhollah; Triantafilis, John
通讯作者	Matinfar, HR
来源期刊	SOIL SYSTEMS
EISSN	2571-8789
出版年	2019
卷号	3 期号:4
英文摘要	To predict the soil texture fractions, 115 profiles were identified based on the Latin hypercube sampling technique, the horizons were sampled, and the clay, sand, and silt contents (in percentages) of soil samples were measured. Then equal-area quadratic spline depth functions were used to derive clay, sand, and silt contents at five standard soil depths (0-5, 5-15, 15-30, 30-60, and 60-100 cm). Auxiliary variables used in this study include the terrain attributes (derived from a digital elevation model), Landsat 8 image data (acquired in 2015), geomorphological map, and spectrometric data (laboratory data). Artificial neural network (ANN), regression tree (RT), and neuro-fuzzy (ANFIS) models were used to make a correlation between soil data (clay, sand, and silt) and auxiliary variables. The results of this study showed that the ANFIS model was more accurate in the prediction of the three parameters of clay, silt, and sand than ANN and RT. Moreover, the ability of ANFIS model to estimate the soil texture fractions in the surface layers was higher than the lower layers. The mean coefficient of determination (R-2) values calculated by 10-fold cross validation suggested the higher prediction performance in the upper depth intervals and higher prediction error in the lower depth intervals (e.g., R-2 = 0.91, concordance correlation coefficient (CCC) = 0.90, RMSE = 4.00 g kg(-1) for sand of 0-5 cm depth, and R-2 = 0.68, CCC = 0.60, RMSE = 8.03 g kg(-1) for 60-100 cm depth). The results also showed that the most important auxiliary variables are spectrometric data, multi-resolution, valley-bottom flatness index and wetness index. Overall, it is recommended to use ANFIS models for the digital mapping of soil texture fractions in other arid regions of Iran.
英文关键词	artificial neural network regression tree neuro-fuzzy spectrometry data
类型	Article
语种	英语
开放获取类型	DOAJ Gold
收录类别	ESCI
WOS记录号	WOS:000505592400009
WOS关键词	CONTINUOUS DEPTH FUNCTIONS ; ARTIFICIAL NEURAL-NETWORK ; ORGANIC-CARBON ; REGRESSION ; CLASSIFICATION ; UNCERTAINTY ; STORAGE ; VALLEY ; AREAS ; FUZZY
WOS类目	Soil Science
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/334029
作者单位	[Mehrabi-Gohari, Elham; Matinfar, Hamid Reza] Lorestan Univ, Coll Agr, Dept Soil Sci, Khorramabad 6815144316, Iran; [Jafari, Azam] Shahid Bahonar Univ Kerman, Agr Fac, Dept Soil Sci, Kerman 7616914111, Iran; [Taghizadeh-Mehrjardi, Ruhollah] Univ Tubingen, Inst Geog, Soil Sci & Geomorphol, D-72070 Tubingen, Germany; [Taghizadeh-Mehrjardi, Ruhollah] Ardakan Univ, Fac Agr & Nat Resources, Ardakan 8951656767, Iran; [Triantafilis, John] Univ New South Wales, Sch Biol Earth & Environm Sci, Fac Sci, Sydney, NSW 2052, Australia
推荐引用方式 GB/T 7714	Mehrabi-Gohari, Elham,Matinfar, Hamid Reza,Jafari, Azam,et al. The Spatial Prediction of Soil Texture Fractions in Arid Regions of Iran[J],2019,3(4).
APA	Mehrabi-Gohari, Elham,Matinfar, Hamid Reza,Jafari, Azam,Taghizadeh-Mehrjardi, Ruhollah,&Triantafilis, John.(2019).The Spatial Prediction of Soil Texture Fractions in Arid Regions of Iran.SOIL SYSTEMS,3(4).
MLA	Mehrabi-Gohari, Elham,et al."The Spatial Prediction of Soil Texture Fractions in Arid Regions of Iran".SOIL SYSTEMS 3.4(2019).