Articles | Volume 3, issue 4
https://doi.org/10.5194/soil-3-191-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/soil-3-191-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Original research article
| 
16 Nov 2017
Original research article |
| 16 Nov 2017
Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models
Madlene Nussbaum
CORRESPONDING AUTHOR
Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Universitätstrasse 16, 8092 Zürich, Switzerland
Lorenz Walthert
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
Marielle Fraefel
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
Lucie Greiner
Research Station Agroscope Reckenholz-Taenikon ART, Reckenholzstrasse 191, 8046 Zürich, Switzerland
Andreas Papritz
Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, Universitätstrasse 16, 8092 Zürich, Switzerland
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Cited
20 citations as recorded by crossref.
- Benefits of hierarchical predictions for digital soil mapping—An approach to map bimodal soil pH M. Nussbaum et al. 10.1016/j.geoderma.2023.116579
- High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic D. Žížala et al. 10.1016/j.catena.2022.106024
- Transferability of pedotransfer functions for estimating soil hydraulic properties: An analysis of controlling factors for forest soils in Switzerland J. Schoch et al. 10.1016/j.geoderma.2025.117397
- Inference of forest soil nutrient regimes by integrating soil chemistry with fuzzy-logic: Regionwide application for stakeholders of Hesse, Germany F. Heitkamp et al. 10.1016/j.geodrs.2020.e00340
- Application of Ordinary Kriging and Regression Kriging Method for Soil Properties Mapping in Hilly Region of Central Vietnam T. Gia Pham et al. 10.3390/ijgi8030147
- Uncertainty indication in soil function maps – transparent and easy-to-use information to support sustainable use of soil resources L. Greiner et al. 10.5194/soil-4-123-2018
- Assessment of soil multi-functionality to support the sustainable use of soil resources on the Swiss Plateau L. Greiner et al. 10.1016/j.geodrs.2018.e00181
- Evaluation of digital soil mapping approaches with large sets of environmental covariates M. Nussbaum et al. 10.5194/soil-4-1-2018
- Tier 4 maps of soil pH at 25 m resolution for the Netherlands A. Helfenstein et al. 10.1016/j.geoderma.2021.115659
- Soil Reflectance Composite for Digital Soil Mapping in a Mediterranean Cropland District M. Zanini et al. 10.3390/rs17010089
- Snow mechanical property variability at the slope scale – implication for snow mechanical modelling F. Meloche et al. 10.5194/tc-18-1359-2024
- Modeling and Forecasting Vibrio Parahaemolyticus Concentrations in Oysters P. Namadi & Z. Deng 10.1016/j.watres.2020.116638
- A framework for the predictive mapping of forest soil properties in mountain areas A. Simon et al. 10.1016/j.geoderma.2020.114383
- BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands A. Helfenstein et al. 10.5194/essd-16-2941-2024
- Energy input, habitat heterogeneity and host specificity drive avian haemosporidian diversity at continental scales O. Darío Hernandes Córdoba et al. 10.1098/rspb.2023.2705
- Scientometric Analysis for Spatial Autocorrelation-Related Research from 1991 to 2021 Q. Luo et al. 10.3390/ijgi11050309
- Filling the European blank spot—Swiss soil erodibility assessment with topsoil samples S. Schmidt et al. 10.1002/jpln.201800128
- Enhancing soil organic carbon estimation accuracy: Integrating spatial vegetation dynamics and temporal analysis with Sentinel 2 imagery P. Mruthyunjaya et al. 10.1016/j.geomat.2024.100002
- Geostatistics or machine learning for mapping soil attributes and agricultural practices W. Mendes et al. 10.1590/0034-737x202067040010
- Barest Pixel Composite for Agricultural Areas Using Landsat Time Series S. Diek et al. 10.3390/rs9121245
19 citations as recorded by crossref.
- Benefits of hierarchical predictions for digital soil mapping—An approach to map bimodal soil pH M. Nussbaum et al. 10.1016/j.geoderma.2023.116579
- High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic D. Žížala et al. 10.1016/j.catena.2022.106024
- Transferability of pedotransfer functions for estimating soil hydraulic properties: An analysis of controlling factors for forest soils in Switzerland J. Schoch et al. 10.1016/j.geoderma.2025.117397
- Inference of forest soil nutrient regimes by integrating soil chemistry with fuzzy-logic: Regionwide application for stakeholders of Hesse, Germany F. Heitkamp et al. 10.1016/j.geodrs.2020.e00340
- Application of Ordinary Kriging and Regression Kriging Method for Soil Properties Mapping in Hilly Region of Central Vietnam T. Gia Pham et al. 10.3390/ijgi8030147
- Uncertainty indication in soil function maps – transparent and easy-to-use information to support sustainable use of soil resources L. Greiner et al. 10.5194/soil-4-123-2018
- Assessment of soil multi-functionality to support the sustainable use of soil resources on the Swiss Plateau L. Greiner et al. 10.1016/j.geodrs.2018.e00181
- Evaluation of digital soil mapping approaches with large sets of environmental covariates M. Nussbaum et al. 10.5194/soil-4-1-2018
- Tier 4 maps of soil pH at 25 m resolution for the Netherlands A. Helfenstein et al. 10.1016/j.geoderma.2021.115659
- Soil Reflectance Composite for Digital Soil Mapping in a Mediterranean Cropland District M. Zanini et al. 10.3390/rs17010089
- Snow mechanical property variability at the slope scale – implication for snow mechanical modelling F. Meloche et al. 10.5194/tc-18-1359-2024
- Modeling and Forecasting Vibrio Parahaemolyticus Concentrations in Oysters P. Namadi & Z. Deng 10.1016/j.watres.2020.116638
- A framework for the predictive mapping of forest soil properties in mountain areas A. Simon et al. 10.1016/j.geoderma.2020.114383
- BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands A. Helfenstein et al. 10.5194/essd-16-2941-2024
- Energy input, habitat heterogeneity and host specificity drive avian haemosporidian diversity at continental scales O. Darío Hernandes Córdoba et al. 10.1098/rspb.2023.2705
- Scientometric Analysis for Spatial Autocorrelation-Related Research from 1991 to 2021 Q. Luo et al. 10.3390/ijgi11050309
- Filling the European blank spot—Swiss soil erodibility assessment with topsoil samples S. Schmidt et al. 10.1002/jpln.201800128
- Enhancing soil organic carbon estimation accuracy: Integrating spatial vegetation dynamics and temporal analysis with Sentinel 2 imagery P. Mruthyunjaya et al. 10.1016/j.geomat.2024.100002
- Geostatistics or machine learning for mapping soil attributes and agricultural practices W. Mendes et al. 10.1590/0034-737x202067040010
1 citations as recorded by crossref.
Latest update: 26 Jul 2025
Short summary
Digital soil mapping (DSM) relates soil property data to environmental data that describe soil-forming factors. With imagery sampled from satellites or terrain analysed at multiple scales, large sets of possible input to DSM are available. We propose a new statistical framework (geoGAM) that selects parsimonious models for DSM and illustrate the application of geoGAM to two study regions. Straightforward interpretation of the modelled effects likely improves end-user acceptance of DSM products.
Digital soil mapping (DSM) relates soil property data to environmental data that describe...
