Decision tree algorithms such as Random Forest have become a widely adapted method for mapping soil properties in geographic space. However, implementing explicit geographic relationships into these methods has proven problematic. Using x- and y-coordinates as covariates gives orthogonal artefacts in the maps, and alternative methods using distances as covariates can be inflexible and difficult to interpret. We propose instead the use of coordinates along several axes tilted at oblique angles to provide an easily interpretable method for obtaining a realistic prediction surface.

We test the method for mapping topsoil organic matter contents in an agricultural field in Denmark. The results show that the method provides accuracies on par with the most reliable alternative methods, namely kriging and the use of buffer distances to the training points. Furthermore, the proposed method is highly flexible, scalable and easily interpretable. This makes it a promising tool for mapping soil properties with complex spatial variation. We believe that the method will be highly useful for mapping soil properties in larger areas, and testing it for this purpose is a logical next step.