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: Luis González Vilas, Vittorio Ernesto Brando, Annalisa Di Cicco, Simone Colella, Davide D’Alimonte, Tamito Kajiyama, Jenni Attila, Thomas Schroeder
• Format: Article in Journal/Newspaper
• Language: English
• Published: Frontiers Media S.A. 2024
• Subjects: ocean color; atmospheric correction; Baltic Sea; Sentinel-3 OLCI; chlorophyll-a; optically complex waters; Science; Q; General. Including nature conservation; geographical distribution; QH1-199.5; Aerosol Robotic Network
• Online Access: https://doi.org/10.3389/fmars.2023.1256990; https://doaj.org/article/1fae5fd7940446a6adc88a349df9060c

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: R2 = 0.11, bias = −0.22; OLCI: R2 = 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.