Sammanfattning
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.
Originalspråk | engelska |
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Titel på värdpublikation | Springer Optimization and Its Applications |
Antal sidor | 15 |
Förlag | Springer |
Utgivningsdatum | 2022 |
Sidor | 251-265 |
ISBN (tryckt) | 978-3-030-84143-0 |
ISBN (elektroniskt) | 978-3-030-84144-7 |
DOI | |
Status | Publicerad - 2022 |
MoE-publikationstyp | A3 Del av bok eller annan forskningsbok |
Publikationsserier
Namn | Springer Optimization and Its Applications |
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Volym | 182 |
ISSN (tryckt) | 1931-6828 |
ISSN (elektroniskt) | 1931-6836 |
Bibliografisk information
Publisher Copyright:© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Vetenskapsgrenar
- 4112 Skogsvetenskap