Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 16, issue 6
Articles | Volume 16, issue 6
https://doi.org/10.5194/essd-16-2941-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-2941-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 16, issue 6
Data description paper
 | 
25 Jun 2024
Data description paper |  | 25 Jun 2024

BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands

Anatol Helfenstein, Vera L. Mulder, Mirjam J. D. Hack-ten Broeke, Maarten van Doorn, Kees Teuling, Dennis J. J. Walvoort, and Gerard B. M. Heuvelink

Viewed

Total article views: 929 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
723 151 55 929 75 38 34
  • HTML: 723
  • PDF: 151
  • XML: 55
  • Total: 929
  • Supplement: 75
  • BibTeX: 38
  • EndNote: 34
Views and downloads (calculated since 01 Feb 2024)
Cumulative views and downloads (calculated since 01 Feb 2024)

Viewed (geographical distribution)

Total article views: 929 (including HTML, PDF, and XML) Thereof 900 with geography defined and 29 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 29 Jun 2024
Download
Short summary
Earth system models and decision support systems greatly benefit from high-resolution soil information with quantified accuracy. Here we introduce BIS-4D, a statistical modeling platform that predicts nine essential soil properties and their uncertainties at 25 m resolution in surface 2 m across the Netherlands. Using machine learning informed by up to 856 000 soil observations coupled with 366 spatially explicit environmental variables, prediction accuracy was the highest for clay, sand and pH.
Read more
Altmetrics
Final-revised paper
Preprint