Phytoplankton Bloom Dynamics in the Baltic Sea Using a Consistently Reprocessed Time Series of Multi-Sensor Reflectance and Novel Chlorophyll-a Retrievals

A relevant indicator for the eutrophication status in Baltic Sea is Chlorophyll-a concentration (Chl-a). Alas, ocean color remote sensing applications to estimate Chl-a this brackish basin, characterized by large gradients salinity and dissolved organic matter, are hampered its optical complexity atmospheric correction limits. This study presents retrieval improvements a fully reprocessed multi-sensor time series of remote-sensing reflectances (Rrs) at ~1 km spatial resolution Sea. new ensemble scheme based on multilayer perceptron neural net (MLP) bio-optical algorithms has been implemented end. The documents that approach outperforms band-ratio when compared situ datasets, reducing gross overestimates observed literature basin. Rrs were then exploited monitoring, providing quantitative description spring summer phytoplankton blooms over 1998–2019. analysis dynamics enabled identification latitudinal variations bloom phenology across early blooming last two decades, spatiotemporal coverage cyanobacterial central southern