Suspended particulate matter (SPM) causes most of the scattering in natural waters and thus has a strong influence on the underwater light field, and consequently on the whole ecosystem. Turbidity is related to the concentration of SPM which usually is measured gravimetrically, a rather time-consuming method. Measuring turbidity is quick and easy, and therefore also more cost-effective. When derived from remote sensing data the method becomes even more cost-effective because of the good spatial resolution of satellite data and the synoptic capability of the method. Turbidity is also listed in the European Union's Marine Strategy Framework Directive as a supporting monitoring parameter, especially in the coastal zone. In this study, we aim to provide a new Baltic Sea algorithm to retrieve SPM concentration from in situ turbidity and investigate how this can be applied to satellite data. An in situ dataset was collected in Swedish coastal waters to develop a new SPM model. The model was then tested against independent datasets from both Swedish and Lithuanian coastal waters. Despite the optical variability in the datasets, SPM and turbidity were strongly correlated (r = 0.97). The developed model predicts SPM reliably from in situ turbidity (R-2 = 0.93) with a mean normalized bias (MNB) of 2.4% for the Swedish and 14.0% for the Lithuanian datasets, and a relative error (RMS) of 25.3% and 37.3%, respectively. In the validation dataset, turbidity ranged from 0.3 to 49.8 FNU (Formazin Nephelometric Unit) and correspondingly, SPM concentration ranged from 0.3 to 34.0 g m(-3) which covers the ranges typical for Baltic Sea waters. Next, the medium-resolution imaging spectrometer (MERIS) standard SPM product MERIS Ground Segment (MEGS) was tested on all available match-up data (n = 67). The correlation between SPM retrieved from MERIS and in situ SPM was strong for the Swedish dataset with r = 0.74 (RMS = 47.4 and MNB = 11.3%; n = 32) and very strong for the Lithuanian dataset with r = 0.94 (RMS = 29.5% and MNB = -1.5%; n = 35). Then, the turbidity was derived from the MERIS standard SPM product using the new in situ SPM model, but retrieving turbidity from SPM instead. The derived image was then compared to existing in situ data and showed to be in the right range of values for each sub-area. The new SPM model provides a robust and cost-efficient method to determine SPM from in situ turbidity measurements (or vice versa). The developed SPM model predicts SPM concentration with high quality despite the high coloured dissolved organic matter (CDOM) range in the Baltic Sea. By applying the developed SPM model to already existing remote sensing data (MERIS/Envisat) and most importantly to a new generation of satellite sensors (in particular OLCI on board the Sentinel-3), it is possible to derive turbidity for the Baltic Sea.

A field programme on light conditions in ice-covered lakes and optical properties of lake ice was performed in seven lakes of Finland and Estonia in February–April 2009. On the basis of irradiance measurements above and below ice, spectral reflectance and transmittance were determined for the ice sheet; time evolution of photosynthetically active radiation (PAR) transmittance was examined from irradiance recordings at several levels inside the ice sheet. Snow cover was the dominant factor for transmission of PAR into the lake water body. Reflectance was 0.74–0.92 in winter, going down to 0.18–0.22 in the melting season. The bulk attenuation coefficient of dry snow was 14–25 m^–1; the level decreased as the spring was coming. The reflectance and bulk attenuation coefficient of snow-free ice were 0.1–0.4 and 1–5 m^–1. Both were considerably smaller than those of snow cover. Seasonal evolution of light transmission was mainly due to snow melting. Snow and ice cover not only depress the PAR level in a lake but also influence the spectral and directional distribution of light.

Seasonal sea ice cover reduces wind-driven mixing and allows for under-ice stratification to develop. These under-ice plumes are a common phenomenon in the seasonal sea ice zone. They stabilize stratification and concentrate terrestrial runoff in the top layer, transporting it further offshore than during ice-free seasons. In this study, the effect of sea ice on spring stratification is investigated in Himmerfjärden bay in the NW Baltic Sea. Distinct under-ice plumes were detected during long ice seasons. The preconditions for the development of the under-ice plumes are described as well as the typical spatial and temporal dimensions of the resulting stratification patterns. Furthermore, the effect of the under-ice plume on the timing of the onset and the maximum of the phytoplankton spring bloom were investigated, in terms of chlorophyll-a (Chl-a) concentrations. At the head of the bay, bloom onset was delayed on average by 18 days in the event of an under-ice plume. However, neither the maximum concentration of Chl-a nor the timing of the Chl-a maximum were affected, implying that the growth period was shorter with a higher daily productivity. During this period from spring bloom onset to maximum Chl-a, the diatom biomass was higher and Mesodinium rubrum biomass was lower in years with under-ice plumes compared to years without under-ice plumes. Our results thus suggest that the projected shorter ice seasons in the future will reduce the probability of under-ice plume development, creating more dynamic spring bloom conditions. These dynamic conditions and the earlier onset of the spring bloom seem to favor the M. rubrum rather than diatoms.