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| Hauptseite > Publikationsdatenbank > Use of electromagnetic induction and remote sensing datasets to characterize spatial variability in soil properties for sustainable farming > print |
| Hauptseite > Publikationsdatenbank > Use of electromagnetic induction and remote sensing datasets to characterize spatial variability in soil properties for sustainable farming > print |
| 001 | 1037664 | ||
| 005 | 20250203103239.0 | ||
| 037 | _ | _ | |a FZJ-2025-00829 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Dogar, Sardar Salar Saeed |0 P:(DE-Juel1)196994 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a Agriculture and geophysics: Illuminating the subsurface |g Agrogeo 24 |c Zürich |d 2024-02-01 - 2024-02-02 |w Switzerland |
| 245 | _ | _ | |a Use of electromagnetic induction and remote sensing datasets to characterize spatial variability in soil properties for sustainable farming |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 520 | _ | _ | |a Within-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. |
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| 536 | _ | _ | |a DFG project G:(GEPRIS)390732324 - EXC 2070: PhenoRob - Robotik und Phänotypisierung für Nachhaltige Nutzpflanzenproduktion (390732324) |0 G:(GEPRIS)390732324 |c 390732324 |x 1 |
| 700 | 1 | _ | |a Brogi, Cosimo |0 P:(DE-Juel1)168418 |b 1 |u fzj |
| 700 | 1 | _ | |a Donat, Marco |0 P:(DE-HGF)0 |b 2 |
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| 700 | 1 | _ | |a Huisman, Johan Alexander |0 P:(DE-Juel1)129472 |b 4 |u fzj |
| 856 | 4 | _ | |u https://doi.org/10.62329/YNGN5617 |
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