Application of a national soil spectrum library for the prediction of primary soil properties using machine learning &ndash; the first results

Zsófia Kovács; János Mészáros; Nóra Szűcs-Vásárhelyi; Péter László; Gábor Szatmári; Mátyás Árvai; László Pásztor

doi:https://doi.org/10.5194/egusphere-egu22-7245

[Back] [Session SSS10.4]

EGU22-7245, updated on 09 Jan 2024

https://doi.org/10.5194/egusphere-egu22-7245

EGU General Assembly 2022

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Application of a national soil spectrum library for the prediction of primary soil properties using machine learning – the first results

Zsófia Kovács^1,2, János Mészáros¹, Nóra Szűcs-Vásárhelyi¹, Péter László¹, Gábor Szatmári¹, Mátyás Árvai¹, and László Pásztor¹

Zsófia Kovács et al.

¹Institute for Soil Sciences, Centre for Agricultural Research, Department of Soil Mapping and Environmental Informatics, Budapest, Hungary
²ELTE Doctoral School of Environmental Sciences, Budapest, Hungary

How to cite: Kovács, Z., Mészáros, J., Szűcs-Vásárhelyi, N., László, P., Szatmári, G., Árvai, M., and Pásztor, L.: Application of a national soil spectrum library for the prediction of primary soil properties using machine learning – the first results, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7245, https://doi.org/10.5194/egusphere-egu22-7245, 2022.

This abstract has been withdrawn on 27 May 2022.