UAV multispectral remote sensing for agriculture: A comparative study of radiometric correction methods under varying illumination conditions

Abstract

Unmanned aerial vehicles (UAVs) equipped with multispectral cameras have been widely used in precision agriculture. However, a notable challenge is the variation in ambient illumination, which affects the accuracy and reliability of UAV-based spectral-data acquisition. In this study, the aim is to evaluate and enhance the performance of existing radiometric correction techniques under varying illumination conditions, primarily concerning radiometric accuracy and homogeneity. Seven methods including three conventional methods and four new methods were employed for correcting the MicaSense Altum multispectral system which equips with a downwelling light sensor (DLS). Two specific strategies were adopted: (1) capturing reference panels at UAV flying altitudes, and (2) strategically placing multiple sets of reference panels throughout the study area. The result shows that calibrating images one time, for instance, the empirical line method (ELM), is seriously affected by the variable illumination. The commercial solution that using the DLS helps improve the uniformity of orthomosaics but lower its radiometric accuracy. Optimising the use of the DLS by capturing panels at the UAV's flight altitude can greatly improve accuracy. Additionally, when the DLS is unavailable, strategically placing multiple reference panels across the field and correcting calibration parameters for each image can effectively help mitigate the impact of varying illumination on generated reflectance orthomosaics. In conclusion, selecting suitable radiometric correction methods is crucial for UAV multi-spectral data collection when facing variable illumination conditions.