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

In: Information and Communication Technologies for Agriculture—Theme I: Sensors

Author

Listed:
  • Jon Atherton

    (University of Helsinki)

  • Chao Zhang

    (University of Helsinki)

  • Jaakko Oivukkamäki

    (University of Helsinki)

  • Liisa Kulmala

    (University of Helsinki)

  • Shan Xu

    (University of Helsinki
    Beijing Normal University)

  • Teemu Hakala

    (National Land Survey of Finland)

  • Eija Honkavaara

    (National Land Survey of Finland)

  • Alasdair MacArthur

    (University of Edinburgh)

  • Albert Porcar-Castell

    (University of Helsinki)

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.

Suggested Citation

  • Jon Atherton & Chao Zhang & Jaakko Oivukkamäki & Liisa Kulmala & Shan Xu & Teemu Hakala & Eija Honkavaara & Alasdair MacArthur & Albert Porcar-Castell, 2022. "What Does the NDVI Really Tell Us About Crops? Insight from Proximal Spectral Field Sensors," Springer Optimization and Its Applications, in: Dionysis D. Bochtis & Maria Lampridi & George P. Petropoulos & Yiannis Ampatzidis & Panos Pardalos (ed.), Information and Communication Technologies for Agriculture—Theme I: Sensors, pages 251-265, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-84144-7_10
    DOI: 10.1007/978-3-030-84144-7_10
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