Atmospheric correction in coastal region using same-day observations of different sun-sensor geometries with a revised POLYMER model

In this paper, with a revised POLYMER (POLYnomial based approach applied to MERIS data) atmospheric correction model, we present a novel scheme (two-angle atmospheric correction algorithm, termed as TAACA) to remove atmospheric contributions in satellite ocean color measurements for coastal environments, especially when there are absorbing aerosols. TAACA essentially uses the same water properties as a constraint to determine oceanic and atmospheric properties simultaneously using two same-day consecutive satellite images having different sun-sensor geometries. The performance of TAACA is first evaluated with a synthetic dataset, where the retrieved remote-sensing reflectance (Rrs) by TAACA matches very well (the coefficient of determination (R2) ≥ 0.98) with the simulated Rrs for each wavelength, and the unbiased root mean square error (uRMSE) is ∼12.2% for cases of both non-absorbing and strongly absorbing aerosols. When this dataset is handled by POLYMER, for non-absorbing aerosol cases, the R2 and uRMSE values are ∼0.99 and ∼7.5%, respectively, but they are ∼0.92 and ∼39.5% for strongly absorbing aerosols. TAACA is further assessed using co-located VIIRS measurements for waters in Boston Harbor and Massachusetts Bay, and the retrieved Rrs from VIIRS agrees with in situ measurements within ∼27.3% at the visible wavelengths. By contrast, a traditional algorithm resulted in uRMSE as 3890.4% and 58.9% at 410 and 443 nm, respectively, for these measurements. The Rrs products derived from POLYMER also show large deviations from in situ measurements. It is envisioned that more reliable Rrs products in coastal waters could be obtained from satellite ocean color measurements with a scheme like TAACA, especially when there are strongly absorbing aerosols.