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Vegetation indices, such as the Normalised Difference Vegetation Index (NDVI), are 13 common metrics used for measuring traits of interest in crop phenotyping. However traditional 14 measurements of these indices are often influenced by multiple confounding factors such as canopy 15 cover and reflectance of underlying soil, visible in canopy gaps. Digital cameras mounted to 16 Unmanned Aerial Vehicles offer the spatial resolution to investigate these confounding factors, 17 however incomplete methods for radiometric calibration into reflectance units limits how the data 18 can be applied to phenotyping. In this study, we assess the applicability of very high spatial 19 resolution (1cm) UAV-based imagery taken with commercial off the shelf (COTS) digital cameras 20 for both deriving calibrated reflectance imagery, and isolating vegetation canopy reflectance from 21 that of the underlying soil. We present new methods for successfully normalising the imagery for 22 exposure and solar irradiance effects, generating multispectral (RGB-NIR) orthomosaics of our 23 target field based wheat crop trial. Validation against measurements from a ground spectrometer 24 showed good results for reflectance (R2 ≥ 0.6) and NDVI (R2 ≥ 0.88). Application of imagery collected 25 through the growing season and masked using the Excess Green Red index was used to assess the 26 impact of canopy cover on NDVI measurements. Results showed the impact of canopy cover 27 artificially reducing plot NDVI values in the early season, where canopy development is low.

Radiometric Calibration of ‘Commercial off the Shelf’ Cameras for UAV-Based High-Resolution Temporal Crop Phenotyping of Reflectance and NDVI