Enhanced POLYMER atmospheric correction algorithm for water-leaving radiance retrievals from hyperspectral/multispectral remote sensing data in inland and coastal waters

Accurate retrieval of the water -leaving radiance from hyperspectral/multispectral remote sensing data in optically complex inland and coastal waters remains a challenge due to the excessive concentrations of phytoplankton and suspended sediments as well as the inaccurate estimation and extrapolation of aerosol radiance over the visible wavelengths. In recent years, reasonably accurate methods were established to estimate the enhanced contribution of suspended sediments in the near -infrared (NIR) and shortwave infrared (SWIR) bands to enable atmospheric correction in coastal waters, but solutions to derive the dominant phytoplankton contribution in the NIR and SWIR bands are less generalizable and subject to large uncertainties in the remotelyderived water color products. These issues are not only associated with the standard atmospheric correction algorithm in the SeaDAS processing system but with the non-traditional algorithms such as POLYMER (POLYnomial-based approach established for the atmospheric correction of MERIS data). This study attempts to enhance the POLYMER algorithm to enable atmospheric correction of hyperspectral and multispectral remote sensing data over a wide range of inland and ocean waters. The original POLYMER algorithm is less suitable owing to its complete reliance on a polynomial approach to model the atmospheric reflectance as a function of the wavelength and retrieve the water -leaving reflectance using two semi -analytical models (MM01 and PR05). The polynomial functions calculate the bulk atmospheric contribution instead of using an explicit method to estimate aerosol radiance separately, resulting the erroneous water color products in inland and coastal waters. The modified POLYMER algorithm (mPOLYMER) employs more realistic approaches to estimate aerosol contributions with a combination of UV and Visible-NIR bands and enables accurate retrievals of water -leaving radiance from both hyperspectral and multispectral remote sensing data. To assess the relative performance and wider applicability of mPOLYMER, the original and enhanced algorithms were tested on a variety of HICO, MSI and MODIS-Aqua data and the retrieved Lwn products were compared with AERONET-OC and OOIL-regional in -situ data. Expectedly, the mPOLYMER algorithm greatly improved the accuracy of Lwn (in terms of magnitude and spectral shape) when applied to MODIS-Aqua and HICO data in highly turbid productive waters (with higher concentrations of phytoplankton or with dense algal blooms) in Muttukadu Lagoon, Lake Erie, Yangtze River Estuary, Baltic Sea and Arabian Sea. In contrast, the original POLYMER algorithm overestimated Lwn in the visible and NIR bands and produced unphysical negative Lwn or distorted Lwn spectra in turbid productive waters. The mPOLYMER yielded a relative mean error reduction of more than 50% (i.e., from 79% to 34%) in Lwn for a large number of matchup data. The improved accuracy and data quality is because the mPOLYMER algorithm's funio and coefficients sufficiently accounted for the enhanced backscattering contribution of phytoplankton and suspended sediments in optically complex waters.