Retrospective analysis of spatial and temporal variability of chlorophyll-a in the Curonian Lagoon

The Curonian Lagoon, the largest in Europe (total surface area 1584 km(2)), is located in the southern part of the Baltic Sea, from which it is separated by a narrow sand bar and to which it is connected by the Klaipeda Straits. It is characterised by shallow eutrophic waters (mean depth 3.8 m) with average low salinity (< 5aEuro degrees) due to the nutrient-rich freshwaters discharged by the river Nemunas. Cyanobacteria blooms, including species producing toxic metabolites, have been a frequent phenomenon in summer. In this study a series of MERIS Full Resolution (FR) satellite images acquired between 2004 and 2009 during summer periods were used to assess the temporal evolution and spatial variability of the lagoon water quality in terms of chlorophyll-a. The models/algorithms were calibrated/validated with field data collected in 2009, based on in situ radiometric (remote sensing reflectance -Rrs-) and limnological (chlorophyll-a concentrations -chl-a-) measurements. The chl-a concentrations were estimated by developing a semi-empirical band ratio (Rrs(708)/Rrs(664)) equation applied to MERIS images after correction for atmospheric effects with the 6S code. Results from this study suggest elevated spatial and temporal variability of chl-a. Spatial variability probably arises from the mosaic of situations within the Curonian lagoon in terms of bottom sediment features, nutrient availability in the water column, water depth and hydrodynamics. Temporal variability is probably linked to complex and interplaying metereological and environmental constraints such as the length of the ice cover period, average water temperatures, wind-induced sediment re-suspension, the pattern of precipitations and river-associated nutrient transport to the lagoon. This study stresses the importance of remote sensing as a valid tool for long-term whole ecosystem studies. The preliminary results need more through analyses and intersection with other environmental data in order to better comprehend algal bloom determinants in complex and extremely dynamic systems such as coastal lagoons.