What does the NDVI really tell us about crops? Insight from proximal spectral field sensors

This is a Preprint and has not been peer reviewed. This is version 4 of this Preprint.


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Jon Atherton, Chao Zhang, jaakko Oivukkamäki, Liisa Kulmala, Shan Xu, Teemu Hakala, Eija Honkavaara, Alasdair MacArthur, Albert Porcar-Castell


The use of remote sensing in agriculture is expanding due to innovation in sensors and platforms. Drones, high resolution instruments on 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 spearheading this drive. Importantly, there are decades of research on the physical principals of the NDVI, relating to soil, structural and measurement geometry effects. Here we bridge the gap between the historical research, grounded in physically based theory, and the recent field-based developments, to ask the question: What does field sensed NDVI tell us about crops? We answer this question with data from two crop field sites featuring field mounted spectral reflectance sensors and a drone-based spectroscopy system. The results show how ecosystem processes can be followed using the NDVI, but also how crop structure and soil reflectance controls data collected in wavelength space.




Environmental Monitoring, Environmental Sciences, Other Physical Sciences and Mathematics, Physical Sciences and Mathematics


crops, drone, NDVI, remote sensing


Published: 2020-09-01 15:54

Last Updated: 2020-11-25 13:28

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CC BY Attribution 4.0 International

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