Articles | Volume 7, issue 1
https://doi.org/10.5194/soil-7-125-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/soil-7-125-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Added value of geophysics-based soil mapping in agro-ecosystem simulations
Agrosphere (IBG-3), Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany
Johan A. Huisman
Agrosphere (IBG-3), Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany
Lutz Weihermüller
Agrosphere (IBG-3), Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany
Michael Herbst
Agrosphere (IBG-3), Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany
Harry Vereecken
Agrosphere (IBG-3), Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany
Viewed
Total article views: 3,733 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 28 Dec 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 2,228 | 1,369 | 136 | 3,733 | 144 | 181 |
- HTML: 2,228
- PDF: 1,369
- XML: 136
- Total: 3,733
- BibTeX: 144
- EndNote: 181
Total article views: 3,048 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 18 May 2021)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,878 | 1,058 | 112 | 3,048 | 127 | 165 |
- HTML: 1,878
- PDF: 1,058
- XML: 112
- Total: 3,048
- BibTeX: 127
- EndNote: 165
Total article views: 685 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 28 Dec 2020)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 350 | 311 | 24 | 685 | 17 | 16 |
- HTML: 350
- PDF: 311
- XML: 24
- Total: 685
- BibTeX: 17
- EndNote: 16
Viewed (geographical distribution)
Total article views: 3,733 (including HTML, PDF, and XML)
Thereof 3,523 with geography defined
and 210 with unknown origin.
Total article views: 3,048 (including HTML, PDF, and XML)
Thereof 2,861 with geography defined
and 187 with unknown origin.
Total article views: 685 (including HTML, PDF, and XML)
Thereof 662 with geography defined
and 23 with unknown origin.
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
1
Cited
14 citations as recorded by crossref.
- Minimally Invasive Approaches to the High‐Resolution Mapping of Colluvial Deposits at the Battlefield of Waterloo: Implications for Archaeological Practice D. Williams et al. https://doi.org/10.1002/gea.70001
- Digital soil mapping and crop modeling to define the spatially-explicit influence of soils on water-limited sugarcane yield N. dos Santos et al. https://doi.org/10.1007/s11104-024-06587-w
- Influence of small-scale spatial variability of soil properties on yield formation of winter wheat J. Groß et al. https://doi.org/10.1007/s11104-023-06212-2
- Solar-induced chlorophyll fluorescence (SIF) tracks variations in the soil-plant available water (PAW): a multiyear analysis on three crops J. Quiros-Vargas et al. https://doi.org/10.1016/j.srs.2026.100367
- Simulating Soil Moisture Dynamics in a Diversified Cropping System Under Heterogeneous Soil Conditions A. Engels et al. https://doi.org/10.3390/agronomy15020407
- Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering D. O’Leary et al. https://doi.org/10.3389/fsoil.2024.1346028
- Enhancing carbon flux estimation in a crop growth model by integrating UAS-derived leaf area index X. Guo et al. https://doi.org/10.1016/j.agrformet.2025.110776
- Field-scale assessment of soil water dynamics using distributed modeling and electromagnetic conductivity imaging T. Ramos et al. https://doi.org/10.1016/j.agwat.2023.108472
- Comparison of soil property predictions in Lithuanian croplands using UAV, satellite, EMI data and machine learning R. Žydelis et al. https://doi.org/10.1016/j.compag.2026.111543
- Assessing soil fertilization effects using time-lapse electromagnetic induction M. Kaufmann et al. https://doi.org/10.5194/soil-11-267-2025
- Cosmic-ray neutron sensors provide scale-appropriate soil water content and vegetation observations for eddy covariance stations in agricultural ecosystems C. Brogi et al. https://doi.org/10.1016/j.agrformet.2025.110731
- Combining electromagnetic induction and satellite-based NDVI data for improved determination of management zones for sustainable crop production S. Dogar et al. https://doi.org/10.5194/soil-11-655-2025
- Response to soil compaction of the electrical resistivity tomography, induced polarisation, and electromagnetic induction methods: a case study in Belgium D. Mansourian et al. https://doi.org/10.1071/SR22260
- Robust automated processing of continuous electrical resistivity measurements for soil texture mapping in support of optimized precision farming M. Roudsari et al. https://doi.org/10.1007/s11119-026-10383-0
14 citations as recorded by crossref.
- Minimally Invasive Approaches to the High‐Resolution Mapping of Colluvial Deposits at the Battlefield of Waterloo: Implications for Archaeological Practice D. Williams et al. https://doi.org/10.1002/gea.70001
- Digital soil mapping and crop modeling to define the spatially-explicit influence of soils on water-limited sugarcane yield N. dos Santos et al. https://doi.org/10.1007/s11104-024-06587-w
- Influence of small-scale spatial variability of soil properties on yield formation of winter wheat J. Groß et al. https://doi.org/10.1007/s11104-023-06212-2
- Solar-induced chlorophyll fluorescence (SIF) tracks variations in the soil-plant available water (PAW): a multiyear analysis on three crops J. Quiros-Vargas et al. https://doi.org/10.1016/j.srs.2026.100367
- Simulating Soil Moisture Dynamics in a Diversified Cropping System Under Heterogeneous Soil Conditions A. Engels et al. https://doi.org/10.3390/agronomy15020407
- Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering D. O’Leary et al. https://doi.org/10.3389/fsoil.2024.1346028
- Enhancing carbon flux estimation in a crop growth model by integrating UAS-derived leaf area index X. Guo et al. https://doi.org/10.1016/j.agrformet.2025.110776
- Field-scale assessment of soil water dynamics using distributed modeling and electromagnetic conductivity imaging T. Ramos et al. https://doi.org/10.1016/j.agwat.2023.108472
- Comparison of soil property predictions in Lithuanian croplands using UAV, satellite, EMI data and machine learning R. Žydelis et al. https://doi.org/10.1016/j.compag.2026.111543
- Assessing soil fertilization effects using time-lapse electromagnetic induction M. Kaufmann et al. https://doi.org/10.5194/soil-11-267-2025
- Cosmic-ray neutron sensors provide scale-appropriate soil water content and vegetation observations for eddy covariance stations in agricultural ecosystems C. Brogi et al. https://doi.org/10.1016/j.agrformet.2025.110731
- Combining electromagnetic induction and satellite-based NDVI data for improved determination of management zones for sustainable crop production S. Dogar et al. https://doi.org/10.5194/soil-11-655-2025
- Response to soil compaction of the electrical resistivity tomography, induced polarisation, and electromagnetic induction methods: a case study in Belgium D. Mansourian et al. https://doi.org/10.1071/SR22260
- Robust automated processing of continuous electrical resistivity measurements for soil texture mapping in support of optimized precision farming M. Roudsari et al. https://doi.org/10.1007/s11119-026-10383-0
Saved (final revised paper)
Latest update: 30 Aug 2026
Short summary
There is a need in agriculture for detailed soil maps that carry quantitative information. Geophysics-based soil maps have the potential to deliver such products, but their added value has not been fully investigated yet. In this study, we compare the use of a geophysics-based soil map with the use of two commonly available maps as input for crop growth simulations. The geophysics-based product results in better simulations, with improvements that depend on precipitation, soil, and crop type.
There is a need in agriculture for detailed soil maps that carry quantitative information....