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Volume 4, issue 1
SOIL, 4, 1–22, 2018
https://doi.org/10.5194/soil-4-1-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
SOIL, 4, 1–22, 2018
https://doi.org/10.5194/soil-4-1-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

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 et al.

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

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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.
This paper presents an extensive evaluation of digital soil mapping (DSM) tools. Recently, large...
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