European topsoil bulk density and organic carbon stock database (0–20&thinsp;cm) using machine-learning-based pedotransfer functions

Chen, Songchao; Chen, Zhongxing; Zhang, Xianglin; Luo, Zhongkui; Schillaci, Calogero; Arrouays, Dominique; Richer-de-Forges, Anne Christine; Shi, Zhou

doi:https://doi.org/10.5194/essd-16-2367-2024

Articles | Volume 16, issue 5

https://doi.org/10.5194/essd-16-2367-2024

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

https://doi.org/10.5194/essd-16-2367-2024

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

Articles | Volume 16, issue 5

Data description paper

|

16 May 2024

Data description paper |

| 16 May 2024

European topsoil bulk density and organic carbon stock database (0–20 cm) using machine-learning-based pedotransfer functions

Songchao Chen, Zhongxing Chen, Xianglin Zhang, Zhongkui Luo, Calogero Schillaci, Dominique Arrouays, Anne Christine Richer-de-Forges, and Zhou Shi

Data sets

European soil bulk density and organic carbon stock database using LUCAS Soil 2018 S. Chen et al. https://doi.org/10.5281/zenodo.10211884

LUCAS 2009 TOPSOIL data European Commission https://esdac.jrc.ec.europa.eu/content/lucas-2009-topsoil-data

LUCAS 2015 TOPSOIL data European Commission https://esdac.jrc.ec.europa.eu/content/lucas2015-topsoil-data

LUCAS 2018 TOPSOIL data European Commission https://esdac.jrc.ec.europa.eu/content/lucas-2018-topsoil-data

Short summary

A new dataset for topsoil bulk density (BD) and soil organic carbon (SOC) stock (0–20 cm) across Europe using machine learning was generated. The proposed approach performed better in BD prediction and slightly better in SOC stock prediction than earlier-published PTFs. The outcomes present a meaningful advancement in enhancing the accuracy of BD, and the resultant topsoil BD and SOC stock datasets across Europe enable more precise soil hydrological and biological modeling.