Articles | Volume 8, issue 2
SOIL, 8, 587–604, 2022
https://doi.org/10.5194/soil-8-587-2022
SOIL, 8, 587–604, 2022
https://doi.org/10.5194/soil-8-587-2022
Original research article
22 Sep 2022
Original research article | 22 Sep 2022

Spatial prediction of organic carbon in German agricultural topsoil using machine learning algorithms

Ali Sakhaee et al.

Data sets

Erste Bodenzustandserhebung Landwirtschaft – Kerndatensatz C. Poeplau, A. Don, H. Flessa, A. Heidkamp, A. Jacobs, and R. Prietz https://doi.org/10.3220/DATA20200203151139

LUCAS 2009 TOPSOIL data European Soil Data Centre (ESDAC) https://esdac.jrc.ec.europa.eu/content/lucas-2009-topsoil-data

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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.