Assessment of Remote Sensing Reflectance Glint Correction Methods from Fixed Automated Above-Water Hyperspectral Radiometric Measurement in Highly Turbid Coastal Waters

Abstract

Fixed automated (unmanned) above-water radiometric measurements are subject to unavoidable sky conditions and surface perturbations, leading to significant uncertainties in retrieved water surface remote sensing reflectances (Rrs(λ), sr−1). This study evaluates various above-water Rrs(λ) glint correction methods using a comprehensive dataset collected at the Royal Netherlands Institute for Sea Research (NIOZ) Jetty Station located in the Marsdiep tidal inlet of the Dutch Wadden Sea, the Netherlands. The dataset includes in-situ water constituent concentrations (2006–2020), inherent optical properties (IOPs) (2006–2007), and above-water hyperspectral (ir)radiance observations collected every 10 min (2006–2023). The bio-optical models were validated using in-situ IOPs and utilized to generate glint-free remote sensing reflectances, Rrs,ref(λ), using a robust IOP-to-Rrs forward model. The Rrs,ref(λ) spectra were used as a benchmark to assess the accuracy of glint correction methods under various environmental conditions, including different sun positions, wind speeds, cloudiness, and aerosol loads. The results indicate that the three-component reflectance model (3C) outperforms other methods across all conditions, producing the highest percentage of high-quality Rrs(λ) spectra with minimal errors. Methods relying on fixed or lookup-table-based glint correction factors exhibited significant errors under overcast skies, high wind speeds, and varying aerosol optical thickness. The study highlights the critical importance of surface-reflected skylight corrections and wavelength-dependent glint estimations for accurate above-water Rrs(λ) retrievals. Two showcases on chlorophyll-a and total suspended matter retrieval further demonstrate the superiority of the 3C model in minimizing uncertainties. The findings highlight the importance of adaptable correction models that account for environmental variability to ensure accurate Rrs(λ) retrieval and reliable long-term water quality monitoring from hyperspectral radiometric measurements.