Hyperspectral reflectance measurements from UAS under intermittent clouds: Correcting irradiance measurements for sensor tilt

Abstract

One great advantage of optical hyperspectral remote sensing from unmanned aerial systems (UAS) compared to satellite missions is the possibility to fly and collect data below clouds. The most typical scenario is flying below intermittent clouds and under turbulent conditions, which causes tilting of the platform. This study aims to advance hyperspectral imaging from UAS in most weather conditions by addressing two challenges: (i) the radiometric and spectral calibrations of miniaturized hyperspectral sensors; and (ii) tilting effects on measured downwelling irradiance. We developed a novel method to correct the downwelling irradiance data for tilting effects. It uses a hybrid approach of minimizing measured irradiance variations for constant irradiance periods and spectral unmixing, to calculate the spectral diffuse irradiance fraction for all irradiance measurements within a flight. It only requires the platform's attitude data and a standard incoming light sensor. We demonstrated the method at the Palo Verde National Park wetlands in Costa Rica, a highly biodiverse area. Our results showed that the downwelling irradiance correction method reduced systematic shifts caused by a change in flight direction of the UAS, by 87% and achieving a deviation of 2.78% relative to a on ground reference in terms of broadband irradiance. High frequency (< 3 s) irradiance variations caused by high-frequency tilting movements of the UAS were reduced by up to 71%. Our complete spectral and radiometric calibration and irradiance correction can significantly remove typical striped illumination artifacts in the surface reflectance-factor map product. The possibility of collecting precise hyperspectral reflectance-factor data from UAS under varying cloud cover makes it more operational for environmental monitoring or precision agriculture applications, being an important step in advancing hyperspectral imaging from UAS.