Articles | Volume 6, issue 2
SOIL, 6, 269–289, 2020
SOIL, 6, 269–289, 2020

Original research article 14 Jul 2020

Original research article | 14 Jul 2020

Oblique geographic coordinates as covariates for digital soil mapping

Anders Bjørn Møller et al.

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Cited articles

Behrens, T., Schmidt, K., Viscarra Rossel, R., Gries, P., Scholten, T., and MacMillan, R.: Spatial modelling with Euclidean distance fields and machine learning, Eur. J. Soil Sci., 69, 757–770,, 2018. 
De Alba, S.: Simulating long-term soil redistribution generated by different patterns of mouldboard ploughing in landscapes of complex topography, Soil Tillage Res., 71, 71–86,, 2003. 
Dubois, G., Malczewski, J., and De Cort, M.: Mapping radioactivity in the environment: Spatial interpolation comparison 97, Office for Official Publications of the European Communities, 280 pp., 2003. 
Esri: World Imagery, Scale not given, September 27, 2016, available at:, last access: 19 June 2019. 
Geurts, P., Ernst, D., and Wehenkel, L.: Extremely randomized trees, Mach. Learn, 63, 3–42,, 2006. 
Short summary
Decision trees have become a widely adapted tool for mapping soil properties in geographic space. However, it is problematic to implement spatial relationships in the models. We present a new method which uses geographic coordinates along several axes tilted at oblique angles in the models. We test this method on four spatial datasets. The results show that the new method is at least as accurate as other proposed alternatives, has a computational advantage and is flexible and interpretable.