干旱区生态环境知识资源中心(Arid): Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran

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DOI	10.1016/j.geoderma.2018.09.006
	Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran
	Zeraatpisheh, Mojtaba 1,2; Ayoubi, Shamsollah 1; Jafari, Azam 3; Tajik, Samaneh 1; Finke, Peter 4
通讯作者	Zeraatpisheh, Mojtaba
来源期刊	GEODERMA
ISSN	0016-7061
EISSN	1872-6259
出版年	2019
卷号	338 页码:445-452
英文摘要	Knowledge about distribution of soil properties over the landscape is required for a variety of land management applications and resources, modeling, and monitoring practices. The main aim of this research was to conduct a spatially prediction of the top soil properties such as soil organic carbon (SOC), calcium carbonate equivalent (CCE), and clay content using digital soil mapping (DSM) approaches in Borujen region, Chaharmahal-Va-Bakhtiari province, central Iran. To achieve this goal, a total of 334 soil samples were collected from 0 to 30 cm depth. Three non-linear models including Cubist (Cu), Random Forest (RF), Regression Tree (RT) and a Multiple Linear Regression (MLR) were used to link environmental covariates and the studied soil properties. The environmental covariates were obtained from a digital elevation model (DEM) and satellite imagery (Landsat Enhanced Thematic Mapper; ETM). The model was calibrated and validated by the 10-fold cross-validation approach. Root mean square error (RMSE) and coefficient of determination (R-2) were used to determine the performance of the models, and relative RMSE (RMSE%) was used to define prediction accuracy. According to the RMSE and R-2, Cu and RF resulted in the most accurate predictions for CCE (R-2 = 0.30 and RMSE = 9.52) and clay contents (R-2 = 0.15 and RMSE = 7.86), respectively, while both of RF and Cu models showed the highest performance to predict SOC content (R-2 = 0.55). Results showed that remote sensing covariates (Ratio Vegetation Index and band 4) were the most important variables to explain the variability of SOC and CCE content, but only topographic attributes were responsible for clay content variation. According to RMSE% results, it could be concluded that the best model is not necessarily able to make the most accurate estimation. This study recommended that more observations and denser sampling should be carried out in the entire study area. Alternatively, stratified sampling by elevation in homogeneous sub-areas was recommended. The stratified sampling probably will increase the performance of models.
英文关键词	Environmental covariates Soil characteristics Spatial prediction Modeling accuracy
类型	Article
语种	英语
国家	Iran ; Peoples R China ; Belgium
收录类别	SCI-E
WOS记录号	WOS:000457657000044
WOS关键词	ORGANIC-CARBON ; SPATIAL VARIABILITY ; GEOSTATISTICAL METHODS ; RANDOM FORESTS ; MANAGEMENT ; DEPTH ; CLASSIFICATION ; PREDICTION ; VARIABLES ; TERRAIN
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/2XILL650/215923
作者单位	1.Isfahan Univ Technol, Dept Soil Sci, Coll Agr, Esfahan 8415683111, Iran; 2.Henan Univ, Key Lab Geospatial Technol Middle & Lower Yellow, Coll Environm & Planning, Kaifeng, Henan, Peoples R China; 3.Shahid Bahonar Univ Kerman, Dept Soil Sci, Coll Agr, Kerman, Iran; 4.Univ Ghent, Dept Soil Management, Coupure Links 653, B-9000 Ghent, Belgium
推荐引用方式 GB/T 7714	Zeraatpisheh, Mojtaba,Ayoubi, Shamsollah,Jafari, Azam,et al. Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran[J],2019,338:445-452.
APA	Zeraatpisheh, Mojtaba,Ayoubi, Shamsollah,Jafari, Azam,Tajik, Samaneh,&Finke, Peter.(2019).Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran.GEODERMA,338,445-452.
MLA	Zeraatpisheh, Mojtaba,et al."Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran".GEODERMA 338(2019):445-452.