Sun Glint-Aware Restoration (SUGAR): a robust sun glint correction algorithm for UAV imagery to enhance monitoring of turbid coastal environments

Sun glint contamination on unmanned aerial vehicles (UAV) imagery is a ubiquitous problem and poses a significant impediment in the retrieval of water quality parameters for coastal monitoring applications. Previous studies using near-infrared (NIR) and regression-based sun glint corrections have shown overcorrection at turbid regions as water-leaving NIR radiance is non-negligible. A spatial shift in the band channels would also result in suboptimal correction in the visible spectrum. Recent total variation (TV) methods show promise in reducing spectral variation associated with glint-affected regions and achieve effective correction of sun glint while leaving non-glint regions largely unaltered. To that end, this study proposes an open-source Sun Glint-Aware Restoration (SUGAR) algorithm that bridges principles in NIR and TV methods for the effective correction of sun glint in multispectral and hyperspectral UAV imagery. The present study shows that SUGAR achieves the best sun glint correction performance among existing regression and pixel-based sun glint correction methods when applied on UAV imagery of turbid and shallow regions. Around 40–80% of the total variation at glint-affected regions have been reduced while preserving features in non-glint regions. Validation of SUGAR with in situ UAV flight surveys and turbidity measurements in the coastal region of Singapore demonstrated significant improvement in turbidity retrieval, with root-mean-squared error (RMSE) reducing from 0.464 to 0.183 FNU and 0.551 to 0.285 FNU for multispectral and hyperspectral imagery, respectively.