Document Type : Research Paper

Authors

• Mehrnaz Neyestani ¹
• Fereydoon Sarmadian ²
• Azam Jafari ³
• Ali Keshavarzi ⁴

¹ Soil Science Dep. University of Tehran

² Soil Science Dep. of University of Tehran

³ Soil Science Dep. Faculty of Agriculture,Shahid Bahonar University, Kerman

⁴ University of Tehran

Abstract

In digital soil mapping, soil characteristic and classes could be extracted truly by numerical and quantitative modelling. Hence, derived rules could be fitted to similar regions for achieving ruled relations on areas without soil information which is called as extrapolation. In the present study, achieving digital soil class map of an area without adequate soil information by Random forest was tested by extrapolation at great group level. The results show overall accuracy 88% and kappa 0.77 of donor area which is able to fit over its similar region. Results of extrapolation show overall accuracy 81% and kappa 0.61 of recipient area which could show logical concordance to produce soil class map of recipient area without applying related soil samples of this area in short time and low cost. Since, extrapolation could be as an efficient way to predict soil classes of unknown areas.

Keywords

• Digital soil mapping
• Donor area
• Recipient area
• Extrapolation
• Random forest
• Soil class