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.
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.
the Creative Commons Attribution 4.0 License.
BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands
Anatol Helfenstein
CORRESPONDING AUTHOR
Soil Geography and Landscape Group, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 RC Wageningen, the Netherlands
Vera L. Mulder
Soil Geography and Landscape Group, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Mirjam J. D. Hack-ten Broeke
Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 RC Wageningen, the Netherlands
Maarten van Doorn
Nutriënten Management Instituut, Nieuwe Kanaal 7C, 6709 PA, Wageningen, the Netherlands
Environmental Systems Analysis Group, Wageningen University & Research, P.O. Box 47, 6700 AA, Wageningen, the Netherlands
Kees Teuling
Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 RC Wageningen, the Netherlands
Dennis J. J. Walvoort
Soil, Water and Land Use Team, Wageningen Environmental Research, Droevendaalsesteeg 3, 6708 RC Wageningen, the Netherlands
Gerard B. M. Heuvelink
Soil Geography and Landscape Group, Wageningen University & Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
ISRIC – World Soil Information, P.O. Box 353, 6700 AJ, Wageningen, the Netherlands
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Cited
10 citations as recorded by crossref.
- Using the phosphorus saturation degree as a guide for sustainable phosphorus management balancing crop production and water quality objectives M. van Doorn et al. 10.1016/j.jenvman.2025.125617
- Mapping soil fertility properties in central Ethiopia at 100 m spatial resolution M. Redi et al. 10.1016/j.geodrs.2025.e00952
- Four-dimensional modelling reveals decline in cropland soil pH during last four decades in China’s Mollisols region J. Chen et al. 10.1016/j.geoderma.2024.117135
- An Open Framework for Downscaling Soil Carbon and Clay Maps Using Sensor Data: Five Case Studies Across Diverse European Landscapes L. Gomes et al. 10.1111/ejss.70132
- Soil Science-Informed Machine Learning B. Minasny et al. 10.1016/j.geoderma.2024.117094
- Gridded, temporally referenced spatial information on soil organic carbon for Hungary G. Szatmári et al. 10.1038/s41597-024-04158-3
- A China dataset of soil properties for land surface modelling (version 2, CSDLv2) G. Shi et al. 10.5194/essd-17-517-2025
- Fine-resolution baseline maps of soil nutrients in farmland of Jiangxi Province using digital soil mapping and interpretable machine learning B. Hu et al. 10.1016/j.catena.2024.108635
- Interpreting and evaluating digital soil mapping prediction uncertainty: A case study using texture from SoilGrids L. Lilburne et al. 10.1016/j.geoderma.2024.117052
- BIS-4D: mapping soil properties and their uncertainties at 25 m resolution in the Netherlands A. Helfenstein et al. 10.5194/essd-16-2941-2024
9 citations as recorded by crossref.
- Using the phosphorus saturation degree as a guide for sustainable phosphorus management balancing crop production and water quality objectives M. van Doorn et al. 10.1016/j.jenvman.2025.125617
- Mapping soil fertility properties in central Ethiopia at 100 m spatial resolution M. Redi et al. 10.1016/j.geodrs.2025.e00952
- Four-dimensional modelling reveals decline in cropland soil pH during last four decades in China’s Mollisols region J. Chen et al. 10.1016/j.geoderma.2024.117135
- An Open Framework for Downscaling Soil Carbon and Clay Maps Using Sensor Data: Five Case Studies Across Diverse European Landscapes L. Gomes et al. 10.1111/ejss.70132
- Soil Science-Informed Machine Learning B. Minasny et al. 10.1016/j.geoderma.2024.117094
- Gridded, temporally referenced spatial information on soil organic carbon for Hungary G. Szatmári et al. 10.1038/s41597-024-04158-3
- A China dataset of soil properties for land surface modelling (version 2, CSDLv2) G. Shi et al. 10.5194/essd-17-517-2025
- Fine-resolution baseline maps of soil nutrients in farmland of Jiangxi Province using digital soil mapping and interpretable machine learning B. Hu et al. 10.1016/j.catena.2024.108635
- Interpreting and evaluating digital soil mapping prediction uncertainty: A case study using texture from SoilGrids L. Lilburne et al. 10.1016/j.geoderma.2024.117052
1 citations as recorded by crossref.
Latest update: 30 Jun 2025
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.
Earth system models and decision support systems greatly benefit from high-resolution soil...
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