Articles | Volume 3, issue 4
SOIL, 3, 191–210, 2017
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.
SOIL, 3, 191–210, 2017
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 et al.
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Cited
12 citations as recorded by crossref.
- 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
- 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
- High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic D. Žížala et al. 10.1016/j.catena.2022.106024
- 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
- Filling the European blank spot—Swiss soil erodibility assessment with topsoil samples S. Schmidt et al. 10.1002/jpln.201800128
- 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
- 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
11 citations as recorded by crossref.
- 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
- 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
- High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic D. Žížala et al. 10.1016/j.catena.2022.106024
- 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
- Filling the European blank spot—Swiss soil erodibility assessment with topsoil samples S. Schmidt et al. 10.1002/jpln.201800128
- 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
- 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: 01 Jul 2022
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...
