Phenomics is an area of biology and crop science concerned with the measurement of phenomes - the physical and biochemical traits of organisms - as they change in response to genetic mutation and environmental influences. Next to very advanced laboratory techniques (PET, NMR, MRI) to measure root and shoot traits of vegetation, increasingly very-high resolution techniques optical remote sensing techniques are used to characterize especially shoot traits (e.g., height, specific leaf area, nitrogen, chlorophyll) directly in the field. Unmanned Aerial Vehicles (UAV) with hyperspectral camera’s provide good opportunities to map and monitor these field traits. Based on the spatial-temporal difference in crop traits and knowledge on the variation in environmental factors, understanding of crop phenomes is developed.
During the growing seasons of 2015 and 2016 a detailed field experiment on different organic grown crops (potato, wheat) has been and will be executed on an experimental field near Wageningen. Next to a large number of field observations of crop traits, also a time-series of remote sensing images was acquired. The images were acquired using the Hyperspectral Mapping System (HYMSY) attached to an UAV. See for details on the image data: http://www.mdpi.com/2072-4292/6/11/11013
In this research, we want to compare different approaches taking advantage of VHR RGB images and hyperspectral cube using vegetation indices with multivariate regression techniques (e.g., partial least squares regression, support vector regression) and evaluate if these methods can be adopted to retrieve crop traits for the different images over the growing season. The best approach will be used to develop a time-series of crop properties for the 60 fields within the experiment.
- Evaluate the contribution of UAV based imaging spectroscopy to characterize crop trait variation to support the field of phenomics.
- Assess the accuracy of both vegetation index and multivariate regression techniques for the retrieval of crop traits from imaging spectroscopy data.
- Suomalainen, J.; Anders, N.; Iqbal, S.; Roerink, G.; Franke, J.; Wenting, P.; Hünniger, D.; Bartholomeus, H.; Becker, R.; Kooistra, L. A Lightweight Hyperspectral Mapping System and Photogrammetric Processing Chain for Unmanned Aerial Vehicles. Remote Sens. 2014, 6, 11013-11030 (http://www.mdpi.com/2072-4292/6/11/11013)
- Deery, D.; Jimenez-Berni, J.; Jones, H.; Sirault, X.; Furbank, R.Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping. Agronomy 2014, 4, 349-379. (http://www.mdpi.com/2073-4395/4/3/349)
Theme(s): Sensing & measuring, Integrated Land Monitoring