Faculty of Technical Sciences

TY - JOUR

T1 - Oblique geographic coordinates as covariates for digital soil mapping

AU - Møller, Anders Bjørn

AU - Beucher, Amélie Marie

AU - Pouladi, Nastaran

AU - Greve, Mogens Humlekrog

PY - 2020/7

Y1 - 2020/7

N2 - Decision tree algorithms, such as random forest, have become a widely adapted method for mapping soil properties in geographic space. However, implementing explicit spatial trends into these algorithms has proven problematic. Using x and y coordinates as covariates gives orthogonal artifacts 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 on four spatial datasets and compare it to similar methods. The results show that the method provides accuracies better than or on par with the most reliable alternative methods, namely kriging and distance-based covariates. 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.

AB - Decision tree algorithms, such as random forest, have become a widely adapted method for mapping soil properties in geographic space. However, implementing explicit spatial trends into these algorithms has proven problematic. Using x and y coordinates as covariates gives orthogonal artifacts 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 on four spatial datasets and compare it to similar methods. The results show that the method provides accuracies better than or on par with the most reliable alternative methods, namely kriging and distance-based covariates. 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.

KW - PREDICTION

KW - REDISTRIBUTION

KW - REGRESSION

KW - TILLAGE

U2 - 10.5194/soil-6-269-2020

DO - 10.5194/soil-6-269-2020

M3 - Journal article

VL - 6

SP - 269

EP - 289

JO - SOIL

JF - SOIL

IS - 2

ER -