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
Luis González Vilas, Vittorio Brando, Annalisa Di Cicco, Simone Colella, Davide D’Alimonte, Tamito Kajiyama, Jenni Attila, Thomas Schroeder
Abstract
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.