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

Jon Atherton (), Chao Zhang, Jaakko Oivukkamäki, Liisa Kulmala, Shan Xu, Teemu Hakala, Eija Honkavaara, Alasdair MacArthur and Albert Porcar-Castell
Additional contact information
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
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

A chapter in Information and Communication Technologies for Agriculture—Theme I: Sensors, 2022, pp 251-265 from Springer

Abstract: 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.

Keywords: Remote sensing; Spectral reflectance sensors; Vegetation index; UAV (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-84144-7_10

Ordering information: This item can be ordered from
http://www.springer.com/9783030841447

DOI: 10.1007/978-3-030-84144-7_10

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().