Articles | Volume 8, issue 2
https://doi.org/10.5194/soil-8-587-2022
© 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-8-587-2022
© 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
| 
22 Sep 2022
Original research article |
| 22 Sep 2022
| 22 Sep 2022
Spatial prediction of organic carbon in German agricultural topsoil using machine learning algorithms
Ali Sakhaee
CORRESPONDING AUTHOR
Thünen Institute of Climate-Smart Agriculture, Braunschweig,
Germany
Anika Gebauer
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
Thünen Institute of Climate-Smart Agriculture, Braunschweig,
Germany
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The CNN is a powerful algorithm to generate three-dimensional multivariate data products. Its high potential is demonstrated by generating a data product for the agricultural soil landscape of Germany. It comprises soil particle size fractions in a vertical resolution of one centimeter. Many soil functions and processes are controlled by the soil particle size distribution. The developed data product is of particular importance to model agricultural processes.
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The CNN is a powerful algorithm to generate three-dimensional multivariate data products. Its high potential is demonstrated by generating a data product for the agricultural soil landscape of Germany. It comprises soil particle size fractions in a vertical resolution of one centimeter. Many soil functions and processes are controlled by the soil particle size distribution. The developed data product is of particular importance to model agricultural processes.
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Short summary
As soil carbon has become a key component of climate-smart agriculture, the demand for high-resolution maps has increased drastically. Meanwhile, machine learning algorithms are becoming more widely used and are opening up new solutions in soil mapping. This paper shows which algorithms perform best, how soil inventory data can be most efficiently used for digital soil mapping, and the different available options and methods to derive high-resolution soil carbon data at the large regional scale.
As soil carbon has become a key component of climate-smart agriculture, the demand for...
