title: The effectiveness of digital soil mapping to predict soil properties over low-relief areas.
abstract: This study investigates the ability of different digital soil mapping (DSM) approaches to predict some of physical and chemical topsoil properties in the Shahrekord plain of Chaharmahal-Va-Bakhtiari province, Iran. According to a semi-detailed soil survey, 120 soil samples were collected from 0 to 30 cm depth with approximate distance of 750 m. Particle size distribution, coarse fragments (CFs), electrical conductivity (EC), pH, organic carbon (OC), and calcium carbonate equivalent (CCE) were determined. Four machine learning techniques, namely, artificial neural networks (ANNs), boosted regression tree (BRT), generalized linear model (GLM), and multiple linear regression (MLR), were used to identify the relationship between soil properties and auxiliary information (terrain attributes, remote sensing indices, geology map, existing soil map, and geomorphology map). Root-mean-square error (RMSE) and mean error (ME) were considered to determine the performance of the models. Among the studied models, GLM showed the highest performance to predict pH, EC, clay, silt, sand, and CCE, whereas the best model is not necessarily able to make accurate estimation. According to RMSE%, DSM has a good efficiency to predict soil properties with low and moderate variabilities. Terrain attributes were the main predictors among different studied auxiliary information. The accuracy of the estimations with more observations is recommended to give a better understanding about the performance of DSM approach over low-relief areas.
authors.affiliation: Soil Science Department, College of Agriculture, Shahrekord University, Shahrekord, Iran. mosleh.zohreh@yahoo.com.
authors.forename: Zohreh
authors.initials: Z
authors.lastname: Mosleh
authors.affiliation: Soil Science Department, College of Agriculture, Shahrekord University, Shahrekord, Iran. mehsalehi@yahoo.com.
authors.forename: Mohammad Hassan
authors.initials: MH
authors.lastname: Salehi
authors.affiliation: Soil Science Department, College of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran. a.jafari@uk.ac.ir.
authors.forename: Azam
authors.initials: A
authors.lastname: Jafari
authors.affiliation: Soil Science Department, College of Agriculture, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran. esfandiarpoor@vru.ac.ir.
authors.forename: Isa Esfandiarpoor
authors.initials: IE
authors.lastname: Borujeni
authors.affiliation: Agricultural and Natural Resources Research Center, Shahrekord, Iran. a_mehnatkesh@yahoo.com.
authors.forename: Abdolmohammad
authors.initials: A
authors.lastname: Mehnatkesh
mesh_terms: D000538:Aluminum Silicates; D000077215:Clay; D004784:Environmental Monitoring / Q000379:methods*; D007492:Iran; D008962:Models, Theoretical; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D010316:Particle Size; D012044:Regression Analysis; D012987:Soil / Q000737:chemistry*
chemical_list: D000538:Aluminum Silicates; D012987:Soil; D000077215:Clay
keywords: Auxiliary information; Machine learning; Performance of models; Terrain attributes