Oblique geographic coordinates as covariates for digital soil mapping

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...

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Bibliographic Details
Published inSoil Vol. 6; no. 2; pp. 269 - 289
Main Authors Møller, Anders Bjørn, Beucher, Amélie Marie, Pouladi, Nastaran, Greve, Mogens Humlekrog
Format Journal Article
LanguageEnglish
Published Göttingen Copernicus GmbH 14.07.2020
Copernicus Publications
Subjects
Algorithms
Datasets
Decision trees
Digital mapping
Mapping
Methods
Researchers
Soil mapping
Soil properties
Soils
Spatial variations
Statistical methods
Trends
Denmark
Europe
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Summary: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.
ISSN:2199-398X
2199-3971
2199-398X
2199-3971
DOI:10.5194/soil-6-269-2020