Articles | Volume 8, issue 2
SOIL, 8, 541–558, 2022
SOIL, 8, 541–558, 2022
Original research article
24 Aug 2022
Original research article | 24 Aug 2022

On the benefits of clustering approaches in digital soil mapping: an application example concerning soil texture regionalization

István Dunkl and Mareike Ließ

Related authors

Process-based analysis of terrestrial carbon flux predictability
István Dunkl, Aaron Spring, Pierre Friedlingstein, and Victor Brovkin
Earth Syst. Dynam., 12, 1413–1426,,, 2021
Short summary
Trivial improvements in predictive skill due to direct reconstruction of the global carbon cycle
Aaron Spring, István Dunkl, Hongmei Li, Victor Brovkin, and Tatiana Ilyina
Earth Syst. Dynam., 12, 1139–1167,,, 2021
Short summary

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,,, 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,,, 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,,, 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,,, 2022
Short summary
An underground, wireless, open-source, low-cost system for monitoring oxygen, temperature, and soil moisture
Elad Levintal, Yonatan Ganot, Gail Taylor, Peter Freer-Smith, Kosana Suvocarev, and Helen E. Dahlke
SOIL, 8, 85–97,,, 2022
Short summary

Cited articles

Adhikari, K., Kheir, R. B., Greve, M. B., Bøcher, P. K., Malone, B. P., Minasny, B., McBratney, A. B., and Greve, M. H.: High-resolution 3-D mapping of soil texture in Denmark, Soil Sci. Soc. Am. J., 77, 860–876,, 2013. a, b, c
Behrens, T., Zhu, A.-X., Schmidt, K., and Scholten, T.: Multi-scale digital terrain analysis and feature selection for digital soil mapping, Geoderma, 155, 175–185,, 2010. a, b
Behrens, T., Schmidt, K., Viscarra Rossel, R. A., Gries, P., Scholten, T., and MacMillan, R. A.: Spatial modelling with Euclidean distance fields and machine learning, Eur. J. Soil Sci., 69, 757–770,, 2018. a
Benjamini, Y. and Yekutieli, D.: The control of the false discovery rate in multiple testing under dependency, Ann. Stat., 29, 1165–1188, 2001. a
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area model of basin hydrology, Hydrolog. Sci. J., 24, 43–69,, 1979. a
Short summary
Digital soil mapping (DSM) allows us to regionalize soil properties by relating them to environmental covariates with the help of an empirical model. Legacy soil data provide a valuable basis to generate high-resolution soil maps with DSM. We studied the usefulness of data-clustering methods to tackle potential sampling bias in legacy soil data while applying DSM for soil texture regionalization. Clustering has proved to be useful in various steps of the DSM process.