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

Fenner H. Holman,Martin J. Wooster,March Castle,Malcolm J. Hawkesford,Andrew B. Riche

doi:10.3390/rs11141657

Abstract

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

Highlights

In crop phenotyping, vegetation indices (e.g., Normalised Difference Vegetation Index (NDVI)) derived from canopy reflectance are commonly used to assess certain physiological traits of interest [1], including (i) plant vigour [2,3], (ii) plant biomass [4,5], (iii) plant nitrogen status [6], (iv) plant Leaf Area Index (LAI) [7,8] and (v) final crop yield [9]
Unmanned Aerial Vehicle (UAV) based remote sensing systems may, offer this capability, and are becoming a prominent method for high throughput phenotyping of field-based crop trials, largely thanks to their very high spatial resolution imagery [12]
We focused on the Defra-funded Wheat Genetic Improvement Network (WGIN) Diversity Field Experiment [24], whose aim is to test the influence of applying different nitrogen fertiliser treatments to different wheat cultivars

Summary

Introduction

Vegetation indices (e.g., NDVI) derived from canopy reflectance are commonly used to assess certain physiological traits of interest [1], including (i) plant vigour [2,3], (ii) plant biomass [4,5], (iii) plant nitrogen status [6], (iv) plant Leaf Area Index (LAI) [7,8] and (v) final crop yield [9] These indices are typically influenced by both the target vegetation condition and variables such as background soil properties and canopy cover/density [10]. Unmanned Aerial Vehicle (UAV) based remote sensing systems may, offer this capability, and are becoming a prominent method for high throughput phenotyping of field-based crop trials, largely thanks to their very high spatial resolution imagery [12]

Objectives

Methods

Findings

Discussion

Conclusion