the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 28 Feb 2025
Combining electromagnetic induction and remote sensing data for improved determination of management zones for sustainable crop production
Abstract. Accurate delineation of management zones is essential for optimizing resource use and improving yield in precision agriculture. Electromagnetic induction (EMI) provides a rapid, non-invasive method to map soil variability, while the Normalized Difference Vegetation Index (NDVI) obtained with remote sensing captures above-ground crop dynamics. Integrating these datasets may enhance management zone delineation but presents challenges in data harmonization and analysis. This study presents a workflow combining unsupervised classification (clustering) and statistical validation to delineate management zones using EMI and NDVI data in a single 70 ha field of the patchCROP experiment in Tempelberg, Germany. Three datasets were investigated: (1) EMI maps, (2) NDVI maps, and (3) a combined EMI-NDVI dataset. Historical yield data and soil samples were used to refine the clusters through statistical analysis. The results demonstrate that four EMI-based zones effectively captured subsurface soil heterogeneity, while three NDVI-based zones better represented yield variability. A combination of EMI and NDVI data resulted in three zones that provided a balanced representation of both subsurface and above-ground variability. The final EMI-NDVI derived map demonstrates the potential of integrating multi-source datasets for field management. It provides actionable insights for precision agriculture, including optimized fertilization, irrigation, and targeted interventions, while also serving as a valuable resource for environmental modelling and soil surveying.
Competing interests: A co-author (Dave O'Leary) of this article is a member of the guest editorial board for the EGU SOIL Special Issue on AgroGeophysics, to which this article was submitted.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.- Preprint
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Status: open (until 01 May 2025)
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RC1: 'Comment on egusphere-2025-827', Anonymous Referee #1, 04 Apr 2025
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General comments:
The paper presents a relevant contribution to precision agriculture by coupling NDVI and EMI for management zones’ delineation. The methodology is generally sound, especially the ue of the SOM and MCASD for cluster optimisation. The study presents a robust workflow that could inform both research and practice. However, some aspects need clarification to improve generalisability and interpretability.
Specific Comments
- Lines 64-122: The review of EMI and NDVI s largely descriptive. It would be stronger if the authors synthesised how of the previous studies succeed or failed in integrating these data types. I suggest adding a short synthesis paragraph summarising what’s missing in prior work and how this study fills the gap.
- Lines 304- 309: the use of min-max scaling prior to clustering is appropriate for ensuring feature comparability. However, the authors should briefly justify this choice over alternatives (e.g., standardisation, robust scaling), especially given the potential presence of outliers in EMI and NDVI data. Min-max is sensitive to extreme values, which may distort the input space and affect cluster geometry in SOM.
- Lines 431-351: the authors perform 100 SOM runs per candidate cluster number and use the MCASD to select the optimal k. While this addresses compactness, there is no assessment of cluster stability. Please clarify whether variability across SOM runs was quantified (ARI or some cluster overlap metrics).
- To enhance the clarity of the manuscript. The authors should consider including a workflow diagram summarizing the complete methodology.
- Lines 93-96: while NDVI is common vegetation index, it is well-known to saturate under high biomass or dense canopy conditions, which may limit its ability to capture within field variability during peak crop growth. The authors should justify why NDVI was selected over alternatives such as EVI or SAVI.
- Lines 370-395: The initial presentation of yield maps provides useful spatial context. However, since the 2012 and 2013 data are acknowledged to be lower in quality. the authors should discuss whether these data were weighted differently or excluded from statistical validation to avoid introducing boas in zone validation.
- Given the spatial nature of EMI and NDVI data and the use of kriging interpolation, spatial autocorrelation is likely present in the dataset. While the current clustering is sound, the authors may consider briefly acknowledging the presence of spatial structure and its potential influence on post-hoc tests.
Citation: https://doi.org/10.5194/egusphere-2025-827-RC1
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