Satellite-based estimation of phytoplankton functional types in the Baltic Sea using a regionalized algorithm

In the last decades, remote sensing and diagnostic pigment (DP)-based methods have significantly advanced the estimation of phytoplankton functional types (PFTs). However, most existing models are optimized for open-ocean conditions and are not directly transferable to optically complex environments, such as the Baltic Sea. This basin presents unique bio-optical challenges that hinder accurate PFTs estimation using global algorithms. Although regional DP-based models have been developed for this region, they are primarily limited to the Southern Baltic, restricting their applicability to the entire basin. The objective of this study was to develop a regionalized empirical algorithm for estimating PFTs and phytoplankton size classes (PSCs) across the entire Baltic Sea by integrating in situ High Performance Liquid Chromatography (HPLC) pigment observations with satellite-derived chlorophyll-a concentration (Chl-a). The algorithm was trained using pigment data collected from multiple sub-basins and subsequently applied to Chl-a satellite observations to map the basin-wide spatial distribution of PSCs and key functional types, including cryptophytes, green algae, and dinoflagellates. The results shown that nanoplankton were the dominant size class across the basin, accounting for up to 46% of Chl-a, particularly in coastal waters. Picoplankton dominated offshore regions, contributing up to 21% of Chl-a, while microplankton reached peak proportions (∼37.5%) in nearshore areas of the Gulf of Finland. Among functional groups, cryptophytes and dinoflagellates exhibited strong coastal and northern basin dominance, whereas green algae and prochlorophytes were more prevalent offshore. By coupling regional HPLC-based empirical relationships with satellite data, this study provides a spatially explicit and internally consistent assessment of PFTs distributions across the Baltic Sea, offering a valuable tool for ecosystem monitoring.

CANUTI Elisabetta; 

2026-03-09

ELSEVIER SCI LTD

JRC141825

1463-5011 (online),   

https://www.sciencedirect.com/science/article/pii/S1463500326000442,    https://publications.jrc.ec.europa.eu/repository/handle/JRC141825,   

10.1016/j.ocemod.2026.102720 (online),