Articles | Volume 4, issue 1
https://doi.org/10.5194/soil-4-1-2018
https://doi.org/10.5194/soil-4-1-2018
Original research article
 | 
10 Jan 2018
Original research article |  | 10 Jan 2018

Evaluation of digital soil mapping approaches with large sets of environmental covariates

Madlene Nussbaum, Kay Spiess, Andri Baltensweiler, Urs Grob, Armin Keller, Lucie Greiner, Michael E. Schaepman, and Andreas Papritz

Data sets

geoGAM: Select Sparse Geoadditive Models for Spatial Prediction M. Nussbaum https://CRAN.R-project.org/package=geoGAM

Model code and software

geoGAM: Select Sparse Geoadditive Models for Spatial Prediction M. Nussbaum https://CRAN.R-project.org/package=geoGAM

Short summary
This paper presents an extensive evaluation of digital soil mapping (DSM) tools. Recently, large sets of environmental covariates (e.g. from analysis of terrain on multiple scales) have become more common for DSM. Many DSM studies, however, only compared DSM methods using less than 30 covariates or tested approaches on few responses. We built DSM models from 300–500 covariates using six approaches that are either popular in DSM or promising for large covariate sets.