000851722 001__ 851722
000851722 005__ 20210129235006.0
000851722 0247_ $$2doi$$a10.1016/j.geoderma.2018.08.001
000851722 0247_ $$2ISSN$$a0016-7061
000851722 0247_ $$2ISSN$$a1872-6259
000851722 0247_ $$2Handle$$a2128/19724
000851722 0247_ $$2WOS$$aWOS:000447095700014
000851722 037__ $$aFZJ-2018-05254
000851722 082__ $$a550
000851722 1001_ $$0P:(DE-Juel1)168418$$aBrogi, C.$$b0$$eCorresponding author
000851722 245__ $$aLarge-scale soil mapping using multi-configuration EMI and supervised image classification
000851722 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2019
000851722 3367_ $$2DRIVER$$aarticle
000851722 3367_ $$2DataCite$$aOutput Types/Journal article
000851722 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1538046745_7583
000851722 3367_ $$2BibTeX$$aARTICLE
000851722 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000851722 3367_ $$00$$2EndNote$$aJournal Article
000851722 520__ $$aReliable 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.
000851722 536__ $$0G:(DE-HGF)POF3-255$$a255 - Terrestrial Systems: From Observation to Prediction (POF3-255)$$cPOF3-255$$fPOF III$$x0
000851722 536__ $$0G:(DE-Juel1)IRTG-GRADUATE-20170406$$aIRTG, Graduate School - Patterns in Soil-Vegetation-Atmosphere-Systems: Monitoring, Modelling and Data Assimilation (TR32) (IRTG, Graduate School) (IRTG-GRADUATE-20170406)$$cIRTG-GRADUATE-20170406$$x1
000851722 588__ $$aDataset connected to CrossRef
000851722 7001_ $$0P:(DE-Juel1)129472$$aHuisman, J. A.$$b1$$ufzj
000851722 7001_ $$0P:(DE-Juel1)133221$$aPätzold, S.$$b2
000851722 7001_ $$0P:(DE-Juel1)145932$$avon Hebel, C.$$b3$$ufzj
000851722 7001_ $$0P:(DE-Juel1)129553$$aWeihermüller, L.$$b4$$ufzj
000851722 7001_ $$0P:(DE-Juel1)168553$$aKaufmann, Manuela$$b5$$ufzj
000851722 7001_ $$0P:(DE-Juel1)129561$$avan der Kruk, J.$$b6$$ufzj
000851722 7001_ $$0P:(DE-Juel1)129549$$aVereecken, H.$$b7$$ufzj
000851722 773__ $$0PERI:(DE-600)2001729-7$$a10.1016/j.geoderma.2018.08.001$$gVol. 335, p. 133 - 148$$p133 - 148$$tGeoderma$$v335$$x0016-7061$$y2019
000851722 8564_ $$uhttps://juser.fz-juelich.de/record/851722/files/1-s2.0-S0016706117315641-main.pdf$$yRestricted
000851722 8564_ $$uhttps://juser.fz-juelich.de/record/851722/files/Brogi_2018.pdf$$yPublished on 2018-08-21. Available in OpenAccess from 2020-08-21.
000851722 8564_ $$uhttps://juser.fz-juelich.de/record/851722/files/Brogi_2018.pdf?subformat=pdfa$$xpdfa$$yPublished on 2018-08-21. Available in OpenAccess from 2020-08-21.
000851722 8564_ $$uhttps://juser.fz-juelich.de/record/851722/files/1-s2.0-S0016706117315641-main.pdf?subformat=pdfa$$xpdfa$$yRestricted
000851722 909CO $$ooai:juser.fz-juelich.de:851722$$pdnbdelivery$$pVDB$$pVDB:Earth_Environment$$pdriver$$popen_access$$popenaire
000851722 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)168418$$aForschungszentrum Jülich$$b0$$kFZJ
000851722 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129472$$aForschungszentrum Jülich$$b1$$kFZJ
000851722 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145932$$aForschungszentrum Jülich$$b3$$kFZJ
000851722 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129553$$aForschungszentrum Jülich$$b4$$kFZJ
000851722 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)168553$$aForschungszentrum Jülich$$b5$$kFZJ
000851722 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129561$$aForschungszentrum Jülich$$b6$$kFZJ
000851722 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129549$$aForschungszentrum Jülich$$b7$$kFZJ
000851722 9131_ $$0G:(DE-HGF)POF3-255$$1G:(DE-HGF)POF3-250$$2G:(DE-HGF)POF3-200$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bErde und Umwelt$$lTerrestrische Umwelt$$vTerrestrial Systems: From Observation to Prediction$$x0
000851722 9141_ $$y2019
000851722 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000851722 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews
000851722 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000851722 915__ $$0StatID:(DE-HGF)0530$$2StatID$$aEmbargoed OpenAccess
000851722 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bGEODERMA : 2015
000851722 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000851722 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index
000851722 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000851722 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5
000851722 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000851722 915__ $$0StatID:(DE-HGF)1060$$2StatID$$aDBCoverage$$bCurrent Contents - Agriculture, Biology and Environmental Sciences
000851722 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database
000851722 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000851722 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz
000851722 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bThomson Reuters Master Journal List
000851722 920__ $$lyes
000851722 9201_ $$0I:(DE-Juel1)IBG-3-20101118$$kIBG-3$$lAgrosphäre$$x0
000851722 980__ $$ajournal
000851722 980__ $$aVDB
000851722 980__ $$aUNRESTRICTED
000851722 980__ $$aI:(DE-Juel1)IBG-3-20101118
000851722 9801_ $$aFullTexts