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.
| Original language | English |
|---|---|
| Title of host publication | Springer Optimization and Its Applications |
| Number of pages | 15 |
| Publisher | Springer |
| Publication date | 2022 |
| Pages | 251-265 |
| ISBN (Print) | 978-3-030-84143-0 |
| ISBN (Electronic) | 978-3-030-84144-7 |
| DOIs | |
| Publication status | Published - 2022 |
| MoE publication type | A3 Book chapter |
Publication series
| Name | Springer Optimization and Its Applications |
|---|---|
| Volume | 182 |
| ISSN (Print) | 1931-6828 |
| ISSN (Electronic) | 1931-6836 |
Bibliographical note
Funding Information: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”.
Funding Information:
Acknowledgments Laura Heimsch is thanked for her help in the f ld 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”.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Fields of Science
- Remote sensing
- Spectral reflectance sensors
- UAV
- Vegetation index
- 4112 Forestry