Articles | Volume 16, issue 5
https://doi.org/10.5194/essd-16-2367-2024
https://doi.org/10.5194/essd-16-2367-2024
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

Viewed

Total article views: 2,162 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,671 411 80 2,162 57 64
  • HTML: 1,671
  • PDF: 411
  • XML: 80
  • Total: 2,162
  • BibTeX: 57
  • EndNote: 64
Views and downloads (calculated since 18 Jan 2024)
Cumulative views and downloads (calculated since 18 Jan 2024)

Viewed (geographical distribution)

Total article views: 2,162 (including HTML, PDF, and XML) Thereof 2,079 with geography defined and 83 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 12 Nov 2024
Download
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.
Altmetrics
Final-revised paper
Preprint