Assessment of ocean color atmospheric correction methods and development of a regional ocean color operational dataset for the Baltic Sea based on Sentinel-3 OLCI

• Published in: Frontiers in Marine Science
• Main Authors: González Vilas, Luis, Brando, Vittorio Ernesto, Di Cicco, Annalisa, Colella, Simone, D’Alimonte, Davide, Kajiyama, Tamito, Attila, Jenni, Schroeder, Thomas
• Other Authors: H2020 Excellent Science
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Frontiers Media SA 2024
• Subjects: Ocean Engineering; Water Science and Technology; Aquatic Science; Global and Planetary Change; Oceanography; Aerosol Robotic Network
• Online Access: http://dx.doi.org/10.3389/fmars.2023.1256990; https://www.frontiersin.org/articles/10.3389/fmars.2023.1256990/full

The Baltic Sea is characterized by large gradients in salinity, high concentrations of colored dissolved organic matter, and a phytoplankton phenology with two seasonal blooms. Satellite retrievals of chlorophyll- a concentration (chl- a ) are hindered by the optical complexity of this basin and the reduced performance of the atmospheric correction in its highly absorbing waters. Within the development of a regional ocean color operational processing chain for the Baltic Sea based on Sentinel-3 Ocean and Land Colour Instrument (OLCI) full-resolution data, the performance of four atmospheric correction processors for the retrieval of remote-sensing reflectance ( Rrs ) was analyzed. Assessments based on three Aerosol Robotic Network-Ocean Color (AERONET-OC) sites and shipborne hyperspectral radiometers show that POLYMER was the best-performing processor in the visible spectral range, also providing a better spatial coverage compared with the other processors. Hence, OLCI Rrs spectra retrieved with POLYMER were chosen as input for a bio-optical ensemble scheme that computes chl- a as a weighted sum of different regional multilayer perceptron neural nets. This study also evaluated the operational Rrs and chl- a datasets for the Baltic Sea based on OC-CCI v.6. The chl- a retrievals based on OC-CCI v.6 and OLCI Rrs , assessed against in-situ chl- a measurements, yielded similar results (OC-CCI v.6: R 2 = 0.11, bias = −0.22; OLCI: R 2 = 0.16, bias = −0.03) using a common set of match-ups for the same period. Finally, an overall good agreement was found between chl- a retrievals from OLCI and OC-CCI v.6 although differences between Rrs were amplified in terms of chl- a estimates.