This title appears in the Scientific Report :
2019
Please use the identifier:
http://hdl.handle.net/2128/19724 in citations.
Please use the identifier: http://dx.doi.org/10.1016/j.geoderma.2018.08.001 in citations.
Large-scale soil mapping using multi-configuration EMI and supervised image classification
Large-scale soil mapping using multi-configuration EMI and supervised image classification
Reliable and high-resolution subsurface characterization beyond the field scale is of great interest for precision agriculture and agro-ecological modelling because the shallow soil (~1–2m depth) is responsible for the storageof moisture and nutrients that are accessible to crops. This can potential...
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Personal Name(s): | Brogi, C. (Corresponding author) |
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Huisman, J. A. / Pätzold, S. / von Hebel, C. / Weihermüller, L. / Kaufmann, Manuela / van der Kruk, J. / Vereecken, H. | |
Contributing Institute: |
Agrosphäre; IBG-3 |
Published in: | Geoderma, 335 (2019) S. 133 - 148 |
Imprint: |
Amsterdam [u.a.]
Elsevier Science
2019
|
DOI: |
10.1016/j.geoderma.2018.08.001 |
Document Type: |
Journal Article |
Research Program: |
Patterns in Soil-Vegetation-Atmosphere-Systems: Monitoring, Modelling and Data Assimilation (TR32) (IRTG, Graduate School) Terrestrial Systems: From Observation to Prediction |
Link: |
Restricted Published on 2018-08-21. Available in OpenAccess from 2020-08-21. Published on 2018-08-21. Available in OpenAccess from 2020-08-21. Restricted |
Publikationsportal JuSER |
Please use the identifier: http://dx.doi.org/10.1016/j.geoderma.2018.08.001 in citations.
Reliable and high-resolution subsurface characterization beyond the field scale is of great interest for precision agriculture and agro-ecological modelling because the shallow soil (~1–2m depth) is responsible for the storageof moisture and nutrients that are accessible to crops. This can potentially be achieved with a combination of direct sampling and Electromagnetic Induction (EMI) measurements, which have shown great potential for soilcharacterization due to their non-invasive nature and high mobility. However, only a few studies have used EMI beyond the field scale because of the challenges associated with a consistent interpretation of EMI data frommultiple fields and acquisition days. In this study, we performed a detailed EMI survey of an area of 1 km2 divided in 51 agricultural fields where previous studies showed a clear connection between crop performanceand soil properties. In total, nine apparent electrical conductivity (ECa) values were measured at each location with a depth of investigation ranging between 0–0.2 to 0–2.7 m. Based on the combination of ECa maps andavailable soil maps, an a priori interpretation was performed and four sub-areas with characteristic sediments and ECa were identified. Then, a supervised classification methodology was used to divide the ECa maps intoareas with similar soil properties. In a next step, soil profile descriptions to a depth of 2m were obtained at 100 sampling locations and 552 samples were analyzed for textural characteristics. The combination of the classifiedmap and ground truth data resulted in a 1m resolution soil map with eighteen units with a typical soil profile and texture information. It was found that the soil profile descriptions and texture of the EMI-based soil classes were significantly different when compared using a two-tailed t-test. Moreover, the high-resolution soil map corresponded well with patterns in crop health obtained from satellite imagery. It was concluded that this novel EMI data processing approach provides a reliable and cost-effective tool to obtain high-resolution soil maps to support precision agriculture and agro-ecological modelling. |