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What Does the NDVI Really Tell Us About Crops? Insight from Proximal Spectral Field Sensors

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Information and Communication Technologies for Agriculture—Theme I: Sensors

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 182))

Abstract

The use of remote sensing in agriculture is expanding due to innovation in sensors and platforms. Uncrewed aerial vehicles (UAVs), CubeSats, and robot mounted proximal phenotyping sensors all feature in this drive. Common threads include a focus on high spatial and spectral resolution coupled with the use of machine learning methods for relating observations to crop parameters. As the best-known vegetation index, the normalized difference vegetation index (NDVI), which quantifies the difference in canopy scattering in the near-infrared and photosynthetic light absorption in the red, is at the front of this drive. Importantly, there are decades of research on the physical principals of the NDVI, relating to soil, structural and measurement geometry effects. Here, the gap between the historical research, grounded in physically based theory, and the recent field-based developments is bridged, to ask the question: What does field sensed NDVI tell us about crops? This question is answered with data from two crop sites featuring field mounted spectral reflectance sensors and a UAV-based spectroscopy system. The results show how ecosystem processes can be followed using the NDVI, but also how crop structure and soil reflectance affects data collected in wavelength space.

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Notes

  1. 1.

    The use of chlorophyll to infer nitrogen is complicated by the fact that the ratio of total nitrogen to chlorophyll nitrogen varies substantially within a plant. More specifically sun leaves have less nitrogen allocated to chlorophyll than shade leaves.

  2. 2.

    As an indirect effect, wilting does reduce absorbed PAR, but this mechanism is a canopy rather than leaf scale process.

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Acknowledgments

Laura Heimsch is thanked for her help in the field and LK acknowledges Business Finland’s (project number 6905/31/2018) funding of the Qvidja study. Niko Viljanen is thanked for processing point clouds. Anu Riikonen is thanked for her help with the Viikki experimental plot. The measurements at Viikki were funded by Academy of Finland project decision number 304097. Regarding satellite data shown in Fig. 2: “The products were generated by the Global Land Service of Copernicus, the Earth Observation programme of the European Commission. The research leading to the current version of the product has received funding from various European Commission Research and Technical Development programs. The product is based on PROBA-V data provided by ESA and distributed by VITO NV”.

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Authors and Affiliations

  1. Optics of Photosynthesis Laboratory, Institute for Atmospheric and Earth System Research/Forest Sciences, University of Helsinki, Helsinki, Finland

    Jon Atherton, Chao Zhang, Jaakko Oivukkamäki, Liisa Kulmala, Shan Xu & Albert Porcar-Castell

  2. Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Shan Xu

  3. Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Masala, Finland

    Teemu Hakala & Eija Honkavaara

  4. School of Geosciences, University of Edinburgh, Edinburgh, UK

    Alasdair MacArthur

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  1. Jon Atherton
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Correspondence to Jon Atherton .

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Editors and Affiliations

  1. Institute for Bio-economy and Agri-technology (iBO), Maria Laura Delle Monache, Department of Civil and Environmental Engineering, University of California, Berkeley, Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece

    Dionysis D. Bochtis

  2. Institute for Bio-economy and Agri-technology (iBO), Maria Laura Delle Monache, Department of Civil and Environmental Engineering, University of California, Berkeley, Centre for Research and Technology Hellas (CERTH), Thessaloniki, Greece

    Maria Lampridi

  3. Department of Geography, Harokopio University of Athens, Athens, Greece

    George P. Petropoulos

  4. Department of Agricultural and Biological Engineering, University of Florida, Immokalee, FL, USA

    Yiannis Ampatzidis

  5. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos Pardalos

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Atherton, J. et al. (2022). What Does the NDVI Really Tell Us About Crops? Insight from Proximal Spectral Field Sensors. In: Bochtis, D.D., Lampridi, M., Petropoulos, G.P., Ampatzidis, Y., Pardalos, P. (eds) Information and Communication Technologies for Agriculture—Theme I: Sensors. Springer Optimization and Its Applications, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-030-84144-7_10

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