Atmospheric correction over coastal waters with aerosol properties constrained by multi-pixel observations

We propose an innovative multi-pixel atmospheric correction approach (MPACA) to process high-spatialresolution satellite measurements over coastal waters based on a revised POLYMER model. MPACA assumes the aerosol type to be uniform within a relatively small region, while the aerosol load and water properties are allowed to vary. Landsat-8 OLI images over six coastal locations with various turbidities were utilized to evaluate the performance of MPACA. The retrieved remote sensing reflectance (R-rs(lambda)) by MPACA is validated with in situ matchups obtained from two sources: ship-based field campaigns and the AERONET-OC networks. It is found that, at each of OLI's four visible bands, MPACA provided accurate R-rs(lambda) products over such coastal environments, with the Root Mean Square Difference (RMSD) and Mean Absolute Percentage Difference (MAPD) less than 0.0006 sr(-1) and 16.2%, respectively. In contrast, the R-rs(lambda) values retrieved with NASA's SeaDAS (v7.5), where each pixel was treated independently, showed RMSD and MAPD as similar to 0.0018 sr(-1) and similar to 38.8%, respectively. Acolite-DSF, which assumed some spatial dependency, obtained MAPD almost two times that of SeaDAS for each visible band. Further, it appears that Acolite-EXP did not perform well for this evaluation dataset, where RMSD is similar to 0.0062 sr(-1) and MAPD is similar to 228.2%. These results suggest that MPACA is a promising scheme for atmospheric correction in coastal waters, especially for measurements from multi-band satellites that have a high spatial resolution along with at least two bands in the NIR or SWIR domain.

Automated summary

The innovative MPACA approach for atmospheric correction in coastal waters, based on a revised POLYMER model, showed promising results in accurately retrieving remote sensing reflectance (R-rs(lambda)) from high-spatial-resolution satellite measurements. The performance of MPACA, which assumed uniform aerosol types and varying aerosol loads and water properties, was validated using Landsat-8 OLI images and in-situ matchups, outperforming other methods like SeaDAS and Acolite.