Articles | Volume 3, issue 4
SOIL, 3, 191–210, 2017
https://doi.org/10.5194/soil-3-191-2017
SOIL, 3, 191–210, 2017
https://doi.org/10.5194/soil-3-191-2017
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.

Related authors

Mapping soil micronutrient concentration at national-scale: an illustration of a decision process framework
Christopher Chagumaira, Joseph G. Chimungu, Patson C. Nalivata, Martin R. Broadley, Madlene Nussbaum, Alice E. Milne, and R. Murray Lark
EGUsphere, https://doi.org/10.5194/egusphere-2022-583,https://doi.org/10.5194/egusphere-2022-583, 2022
Preprint archived
Short summary
Uncertainty indication in soil function maps – transparent and easy-to-use information to support sustainable use of soil resources
Lucie Greiner, Madlene Nussbaum, Andreas Papritz, Stephan Zimmermann, Andreas Gubler, Adrienne Grêt-Regamey, and Armin Keller
SOIL, 4, 123–139, https://doi.org/10.5194/soil-4-123-2018,https://doi.org/10.5194/soil-4-123-2018, 2018
Short summary
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
SOIL, 4, 1–22, https://doi.org/10.5194/soil-4-1-2018,https://doi.org/10.5194/soil-4-1-2018, 2018
Short summary
Estimating soil organic carbon stocks of Swiss forest soils by robust external-drift kriging
M. Nussbaum, A. Papritz, A. Baltensweiler, and L. Walthert
Geosci. Model Dev., 7, 1197–1210, https://doi.org/10.5194/gmd-7-1197-2014,https://doi.org/10.5194/gmd-7-1197-2014, 2014

Related subject area

Soil and methods
Spatial prediction of organic carbon in German agricultural topsoil using machine learning algorithms
Ali Sakhaee, Anika Gebauer, Mareike Ließ, and Axel Don
SOIL, 8, 587–604, https://doi.org/10.5194/soil-8-587-2022,https://doi.org/10.5194/soil-8-587-2022, 2022
Short summary
On the benefits of clustering approaches in digital soil mapping: an application example concerning soil texture regionalization
István Dunkl and Mareike Ließ
SOIL, 8, 541–558, https://doi.org/10.5194/soil-8-541-2022,https://doi.org/10.5194/soil-8-541-2022, 2022
Short summary
An open Soil Structure Library based on X-ray CT data
Ulrich Weller, Lukas Albrecht, Steffen Schlüter, and Hans-Jörg Vogel
SOIL, 8, 507–515, https://doi.org/10.5194/soil-8-507-2022,https://doi.org/10.5194/soil-8-507-2022, 2022
Short summary
Identification of thermal signature and quantification of charcoal in soil using differential scanning calorimetry and benzene polycarboxylic acid (BPCA) markers
Brieuc Hardy, Nils Borchard, and Jens Leifeld
SOIL, 8, 451–466, https://doi.org/10.5194/soil-8-451-2022,https://doi.org/10.5194/soil-8-451-2022, 2022
Short summary
Estimating soil fungal abundance and diversity at a macroecological scale with deep learning spectrotransfer functions
Yuanyuan Yang, Zefang Shen, Andrew Bissett, and Raphael A. Viscarra Rossel
SOIL, 8, 223–235, https://doi.org/10.5194/soil-8-223-2022,https://doi.org/10.5194/soil-8-223-2022, 2022
Short summary

Cited articles

Adhikari, K., Kheir, R., Greve, M., Bøcher, P., Malone, B., Minasny, B., McBratney, A., and Greve, M.: High-resolution 3-D mapping of soil texture in Denmark, Soil Sci. Soc. Am. J., 77, 860–876, https://doi.org/10.2136/sssaj2012.0275, 2013.
ALN: Historische Feuchtgebiete der Wildkarte 1850. Amt für Landschaft und Natur des Kantons Zürich, available at: http://www.aln.zh.ch/internet/baudirektion/aln/de/naturschutz/naturschutzdaten/geodaten.html (last access: 29 March 2017), 2002.
ALN: Geologische Karte des Kantons Zürich nach Hantke et al. 1967, GIS-ZH Nr. 41. Amt für Landschaft und Natur des Kantons Zürich, available at: http://www.gis.zh.ch/Dokus/Geolion/gds_41.pdf (last access: 15 February 2015), 2014a.
ALN: Meliorationskataster des Kantons Zürich, GIS-ZH Nr. 148. Amt für Landschaft und Natur des Kantons Zürich, available at: http://www.geolion.zh.ch/geodatensatz/show?nbid=387 (last access: 29 March 2017), 2014b.
AWEL: Hinweisflächen für anthropogene Böden, GIS-ZH Nr. 260. Amt für Abfall, Wasser, Energie und Luft des Kanton Zürich, available at: http://www.geolion.zh.ch/geodatensatz/show?nbid=985 (last access: 29 March 2017), 2012.
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.