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The effectiveness of digital soil mapping to predict soil properties over low-relief areas

Environmental Monitoring and Assessment volume 188, Article number: 195 (2016) Cite this article

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.

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Affiliations

  1. Soil Science Department, College of Agriculture, Shahrekord University, Shahrekord, Iran

    Zohreh Mosleh & Mohammad Hassan Salehi

  2. Soil Science Department, College of Agriculture, Shahid Bahonar University of Kerman, Kerman, Iran

    Azam Jafari

  3. Soil Science Department, College of Agriculture, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran

    Isa Esfandiarpoor Borujeni

  4. Agricultural and Natural Resources Research Center, Shahrekord, Iran

    Abdolmohammad Mehnatkesh

Authors
  1. Zohreh Mosleh
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  2. Mohammad Hassan Salehi
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  3. Azam Jafari
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  4. Isa Esfandiarpoor Borujeni
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  5. Abdolmohammad Mehnatkesh
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Correspondence to Mohammad Hassan Salehi.

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Mosleh, Z., Salehi, M.H., Jafari, A. et al. The effectiveness of digital soil mapping to predict soil properties over low-relief areas. Environ Monit Assess 188, 195 (2016). https://doi.org/10.1007/s10661-016-5204-8

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  • DOI: https://doi.org/10.1007/s10661-016-5204-8

Keywords

  • Auxiliary information
  • Machine learning
  • Performance of models
  • Terrain attributes