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001037664 1001_ $$0P:(DE-Juel1)196994$$aDogar, Sardar Salar Saeed$$b0$$eCorresponding author$$ufzj
001037664 1112_ $$aAgriculture and geophysics: Illuminating the subsurface$$cZürich$$d2024-02-01 - 2024-02-02$$gAgrogeo 24$$wSwitzerland
001037664 245__ $$aUse of electromagnetic induction and remote sensing datasets to characterize spatial variability in soil properties for sustainable farming
001037664 260__ $$c2024
001037664 3367_ $$033$$2EndNote$$aConference Paper
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001037664 520__ $$aWithin-field soil variability significantly influences water and nutrients availability, which in turn affects crop growth and yield. A comprehensive understanding of soil characteristics is thus necessary in sustainable agriculture, which demands both above and below-surface soil sensing. Commonly used sensing methods include electromagnetic induction (EMI) mapping and remote sensing of the normalized difference vegetation index (NDVI). Previous studies have harnessed EMI data to characterize the impact of soil heterogeneity on crop production, utilizing classification techniques in combination with soil maps and remote sensing data. However, there is further potential in combining proximal sensing, remote sensing, and yield maps in a fully integrated manner. This combination may result in the delineation of agricultural management zones that can account for a more holistic range of factors that affect crop development. This study focuses on a 70-hectare field of the PatchCrop living lab in Tempelberg, Brandenburg. EMI measurements were performed with two systems recording nine different coil separations that provide information on different subsurface depth ranges. Three field campaigns between August 2022 and 2023 have been conducted. The analysis presented here is focused on the 2019 growing season, where 19 NDVI images obtained from high-resolution PlanetScope satellite were available. In addition, historical yield maps from 2011 to 2019 are available. In this study, we used unsupervised classification approaches to derive more holistic management zones using a combination of NDVI maps and normalized EMI maps. The results of clustering are compared with yield maps to assess the efficacy of the derived management zones.
001037664 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x0
001037664 536__ $$0G:(GEPRIS)390732324$$aDFG project G:(GEPRIS)390732324 - EXC 2070: PhenoRob - Robotik und Phänotypisierung für Nachhaltige Nutzpflanzenproduktion (390732324)$$c390732324$$x1
001037664 7001_ $$0P:(DE-Juel1)168418$$aBrogi, Cosimo$$b1$$ufzj
001037664 7001_ $$0P:(DE-HGF)0$$aDonat, Marco$$b2
001037664 7001_ $$0P:(DE-Juel1)129549$$aVereecken, Harry$$b3$$ufzj
001037664 7001_ $$0P:(DE-Juel1)129472$$aHuisman, Johan Alexander$$b4$$ufzj
001037664 8564_ $$uhttps://doi.org/10.62329/YNGN5617
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001037664 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)196994$$aForschungszentrum Jülich$$b0$$kFZJ
001037664 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)168418$$aForschungszentrum Jülich$$b1$$kFZJ
001037664 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Leibniz Centre for Agricultural Landscape Research, 15374 Müncheberg, Germany$$b2
001037664 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129549$$aForschungszentrum Jülich$$b3$$kFZJ
001037664 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129472$$aForschungszentrum Jülich$$b4$$kFZJ
001037664 9131_ $$0G:(DE-HGF)POF4-217$$1G:(DE-HGF)POF4-210$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-2173$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lErde im Wandel – Unsere Zukunft nachhaltig gestalten$$vFür eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten$$x0
001037664 9141_ $$y2024
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