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| 001 | 851722 | ||
| 005 | 20210129235006.0 | ||
| 024 | 7 | _ | |a 10.1016/j.geoderma.2018.08.001 |2 doi |
| 024 | 7 | _ | |a 0016-7061 |2 ISSN |
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| 037 | _ | _ | |a FZJ-2018-05254 |
| 082 | _ | _ | |a 550 |
| 100 | 1 | _ | |a Brogi, C. |0 P:(DE-Juel1)168418 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Large-scale soil mapping using multi-configuration EMI and supervised image classification |
| 260 | _ | _ | |a Amsterdam [u.a.] |c 2019 |b Elsevier Science |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a 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. |
| 536 | _ | _ | |a 255 - Terrestrial Systems: From Observation to Prediction (POF3-255) |0 G:(DE-HGF)POF3-255 |c POF3-255 |f POF III |x 0 |
| 536 | _ | _ | |0 G:(DE-Juel1)IRTG-GRADUATE-20170406 |x 1 |c IRTG-GRADUATE-20170406 |a IRTG, Graduate School - Patterns in Soil-Vegetation-Atmosphere-Systems: Monitoring, Modelling and Data Assimilation (TR32) (IRTG, Graduate School) (IRTG-GRADUATE-20170406) |
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| 700 | 1 | _ | |a Huisman, J. A. |0 P:(DE-Juel1)129472 |b 1 |u fzj |
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| 700 | 1 | _ | |a Weihermüller, L. |0 P:(DE-Juel1)129553 |b 4 |u fzj |
| 700 | 1 | _ | |a Kaufmann, Manuela |0 P:(DE-Juel1)168553 |b 5 |u fzj |
| 700 | 1 | _ | |a van der Kruk, J. |0 P:(DE-Juel1)129561 |b 6 |u fzj |
| 700 | 1 | _ | |a Vereecken, H. |0 P:(DE-Juel1)129549 |b 7 |u fzj |
| 773 | _ | _ | |a 10.1016/j.geoderma.2018.08.001 |g Vol. 335, p. 133 - 148 |0 PERI:(DE-600)2001729-7 |p 133 - 148 |t Geoderma |v 335 |y 2019 |x 0016-7061 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/851722/files/1-s2.0-S0016706117315641-main.pdf |y Restricted |
| 856 | 4 | _ | |y Published on 2018-08-21. Available in OpenAccess from 2020-08-21. |u https://juser.fz-juelich.de/record/851722/files/Brogi_2018.pdf |
| 856 | 4 | _ | |y Published on 2018-08-21. Available in OpenAccess from 2020-08-21. |x pdfa |u https://juser.fz-juelich.de/record/851722/files/Brogi_2018.pdf?subformat=pdfa |
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