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| 001 | 1037663 | ||
| 005 | 20250203103239.0 | ||
| 037 | _ | _ | |a FZJ-2025-00828 |
| 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 Tereno Workshop 2024 |c Leipzig |d 2024-11-05 - 2024-11-07 |w Germany |
| 245 | _ | _ | |a Evaluating the impact of integrating EMI and remote sensing data in the delineation of management zones in a heterogeneous agricultural field |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
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| 520 | _ | _ | |a Accurate and reliable characterization of intra-field heterogeneity in soil properties, and water content is crucial in precision agriculture, as these factors significantly impact crop performance and yield. Non-invasive hydro-geophysical methods, such as electromagnetic induction (EMI), can be employed to delineate intra-field agricultural management zones, which represent areas with homogeneous field characteristics that have a similar influence on crops. Integrating additional data sources, such as remote sensing imagery and yield maps, has the potential to enhance the quality of these management zones. However, extracting both above-ground and subsurface information from multiple datasets for large agricultural fields presents challenges in data harmonization and analysis. Furthermore, the selection of optimal dataset combinations and the impact of different data products on management zone delineation have not been fully explored. In this study, we present an approach to delineate intra-field management zones using two key indicators: EMI measurements conducted with a CMD Mini-Explorer and a CMD Mini-Explorer Special-Edition (featuring 3 and 6 coil separations, respectively), and the Normalized Difference Vegetation Index (NDVI) derived from PlanetScope satellite imagery. To assess the contribution of each indicator, three scenarios were used for zone delineation: (1) using EMI measurements alone, (2) using NDVI alone, and (3) using a combination of both. The resulting management zones were then evaluated by analyzing differences in multi-year crop yield and soil information using statistical methods. The results revealed that NDVI alone provided strong insights into field characteristics and could serve as a valuable alternative to traditional yield maps, particularly for capturing above-ground variability. However, the integration of NDVI with EMI data was most beneficial, capturing a more comprehensive view of both above and subsurface spatial variability. Overall, the findings demonstrate the advantages of integrating proximal and remote sensing data and suggest a high potential for differential crop fertilization and targeted soil management in the study area. |
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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 |
| 700 | 1 | _ | |a Vereecken, Harry |0 P:(DE-Juel1)129549 |b 3 |u fzj |
| 700 | 1 | _ | |a Huisman, Johan Alexander |0 P:(DE-Juel1)129472 |b 4 |u fzj |
| 856 | 4 | _ | |u https://events.hifis.net/event/1603/abstracts/2781/ |
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