Real-time Reflectance Generation for UAV Multispectral Imagery using an Onboard Downwelling Spectrometer in Varied Weather Conditions

TL;DR

This paper presents a real-time, cost-effective method for converting UAV multispectral images to reflectance using onboard spectrometry and a 4-band regression model, significantly improving accuracy under variable weather conditions.

Contribution

It introduces a novel 4-band linear regression model with onboard spectral reference for accurate, real-time reflectance generation in UAV multispectral imaging under diverse weather conditions.

Findings

86.1% RMSE reduction compared to empirical line method

Reflectance prediction RMSE of 2.03% in UAV campaigns

Enhanced vegetation index consistency by 95% in large rice fields

Abstract

Advancements in unmanned aerial vehicle (UAV) remote sensing with spectral imaging enable efficient assessment of critical agronomic traits. However, existing reflectance calibration or generation methods suffer from limited prediction accuracy and practical flexibility. This study explores reliable and cost-efficient methods for the accurate conversion of digital number values acquired from a multispectral imager into reflectance, leveraging real-time solar spectra as references. To ensure consistent measurements of incident light, an upward gimbal-mounted downwelling spectrometer was attached to the UAV, and a sinusoidal model was developed to correct for solar position variability. Using principal component analysis on the reference solar spectrum for band selection, a multiple linear regression model with four sensitive bands (4-Band MLR) and a 30 nm bandwidth achieved performance comparable to the direct correction method. The root mean square error (RMSE) for reflectance prediction improved by 86.1% compared to the empirical line method under fluctuating cloudy conditions and by 59.6% compared to the downwelling light sensor method averaged across different weather conditions. The RMSE was calculated as 2.24% in a ground-based diurnal validation, and 2.03% in a UAV campaign conducted at various times throughout a sunny day. Implementing the 4-Band MLR model enhanced the consistency of canopy reflectance within a homogeneous vegetation area by 95.0% during spectral imaging in a large rice field under significant cloud fluctuations. Additionally, improvements of 86.0% and 90.3% were noted for two vegetation indices: the normalized difference vegetation index (NDVI; a ratio index) and the difference vegetation index (DVI; a non-ratio index), respectively.