Articles | Volume 8, issue 2
https://doi.org/10.5194/soil-8-541-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-8-541-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Original research article
| 
24 Aug 2022
Original research article |
| 24 Aug 2022
| 24 Aug 2022
On the benefits of clustering approaches in digital soil mapping: an application example concerning soil texture regionalization
Max Planck Institute for Meteorology, Hamburg, Germany
Department Soil System Science, Helmholtz Centre for Environmental Research – UFZ, Halle (Saale), Germany
Mareike Ließ
Department Soil System Science, Helmholtz Centre for Environmental Research – UFZ, Halle (Saale), Germany
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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.
Digital soil mapping (DSM) allows us to regionalize soil properties by relating them to...
