Mitigating sun glint effects in Landsat OLI imagery for enhanced macroalgae biomass estimation

Landsat-8 and Landsat-9 Operational Land Imager (OLI) sensors offer high-resolution observations critical for coastal ocean color monitoring, yet their utility is often compromised by sun glint contamination. Sun glint can severely reduce the accuracy of retrieved Rayleigh-corrected reflectance (Rrc), leading to uncertainties in the macroalgae biomass estimation. To mitigate this issue, we developed a sun glint correction method that uses Rrc at the shortwave infrared (SWIR) band to model glint intensity and applies a regression-based correction to the visible and near-infrared (NIR) bands. Validation using <i>in-situ</i> measurements from AERONET-OC stations showed strong consistency, with correlation coefficients (r) ranging from 0.733 to 0.793 and root mean square errors (RMSE) as low as 0.007. We further applied this approach to evaluate the impact of sun glint on the Floating Algae Index (FAI) and the macroalgae biomass in a case study in the Gulf of Mexico. The results showed improved spatial uniformity in the corrected FAI and a 32% increase in estimated biomass after correction. This increase is primarily attributed to the restoration of spectral contrast in the FAI relevant bands after sun glint correction, which enhanced algae-related variations. Overall, these results demonstrate the effectiveness of the proposed method in mitigating sun glint impacts on Rrc products and highlight the significant impact of sun glint on macroalgae biomass estimation.