Analysis of natural background and dredging-induced changes in TSM concentration from MERIS images near commercial harbours in the Estonian coastal sea

Medium Resolution Imaging Spectrometer (MERIS) products with 300 m resolution from 2006 to 2011 were used to evaluate the local background of total suspended matter (TSM) in the vicinity of commercial harbours located along the Estonian coastline in the Baltic Sea. The difference between background TSM maps (mainly influenced by spring bloom, cyanobacterial bloom, resuspension, and river inflow) and dredging period mean maps was used for the estimation of dredging-induced turbidity at the time of dredging operations. Validation of Case II Regional (C2 R) and Free University of Berlin (FUB) MERIS processors with point measurements showed that both processors represent the changes in TSM concentration adequately. C2 R processors showed better statistics (R² = 0.61, root mean square error = 0.82 mg l^–1, SD = 0.77 mg l^–1, mean bias = –0.28 mg l^–1) compared to the FUB processor. Analysis of monthly mean TSM maps revealed that the variability of TSM concentration, showing the resilience level of the local ecosystem, is very different along the Estonian coastline – varying between 0.75 and 2.60 mg l^–1 near the Port of Tallinn, located in the Gulf of Finland, and between 10.04 and 24.23 mg l^–1 near the Port of Pärnu, located in the Gulf of Riga. The viability of the method for dredging impact detection was tested by evaluating the dredging-induced turbidity on monthly mean TSM maps for the dredging period in autumn 2008 in Pakri Bay, which is an environmentally sensitive area. A threshold TSM concentration value of >2.26 mg l^–1 difference from background TSM was defined as a criterion for dredging impact detection for Pakri Bay. The area of dredging-induced turbidity was between 0.56 and 1.25 km² and did not reach the environmentally sensitive NATURA 2000 region adjoining Paldiski South Harbour.     

Parallel and interacting Markov chain Monte Carlo algorithm

In many situations it is important to be able to propose N independent realizations of a given distribution law. We propose a strategy for making N parallel Monte Carlo Markov chains (MCMC) interact in order to get an approximation of an independent N-sample of a given target law. In this method each individual chain proposes candidates for all other chains. We prove that the set of interacting chains is itself a MCMC method for the product of N target measures. Compared to independent parallel chains this method is more time consuming, but we show through examples that it possesses many advantages. This approach is applied to a biomass evolution model.     

Applicability of SAR-based wave retrieval for wind–wave interaction analysis in the fetch-limited Baltic

In this article, a method for the detection of wave field parameters from synthetic aperture radar (SAR) imagery in the fetch-limited Baltic Sea is presented. Over the Baltic Sea region, common southwest (SW) and west (W) winds induce steep waves with shorter wavelengths compared with ocean waves. Thus, with the use of previous SAR sensors (e.g. ENVISAT/ASAR), it was not possible to detect individual waves and retrieve image wave number spectra. Since the year 2007, when TerraSAR-X (TS-X) reached its orbit, high spatial resolution data is available for measuring the sea-state parameters: the individual waves up to 30 m wavelength and their refraction can be distinguished. The main objective of this work was to demonstrate the capability of detecting wave field parameter from (TS-X) imagery in the Baltic Sea. The wave field parameters obtained from the SAR imagery were compared with in situ measurements and the Simulating WAves Nearshore (SWAN) wave model. The comparison of SAR-based wave field information with buoy measurements showed high agreement in case of wave propagation direction (r = 0.95) and wavelength (r = 0.83). A significant correlation is also seen between SWAN- and SAR-derived wave propagation direction (r = 0.87) and wavelengths (r = 0.91). With the case studies, it is shown that SAR data enables one to detect land shadow effects and small-scale wave field variations in the coastal zone. It was shown that SAR data is also valuable for improving and interpreting the wave model results. In consequence of common slanting fetch cases over the Baltic Sea region, it was demonstrated that the peak wave directions differ from the mean wind directions up to 43°.     

Sea state in the Baltic Sea from space-borne high-resolution synthetic aperture radar imagery

In this work, remote sensing synthetic aperture radar (SAR) data from X-band TerraSAR-X and TanDEM-X (TS-X and TD-X) satellites have been used to adopt the algorithms for estimating sea state parameters in the specific condition of the Baltic Sea with archipelago islands and where short steep sea state dominates. Since the moving targets can be defocused and shifted in SAR images, sea state consisting of short windsea waves with strong local orbital velocities and wave breaking needs additional effort for accurate estimation of the total significant wave height that consists of swell and windsea parts. The XWAVE_C algorithm, developed for the North Sea, where the long swell waves coming from the Atlantic Ocean are present during storms, was further enhanced for the short steep windsea which dominates under ordinary storm conditions in the Baltics. For the empirical XWAVE_C model function, based on the spectral analysis of subscenes as well as on local wind information, an additional term was incorporated for assessment the minimal windsea significant wave height by applying JONSWAP wave spectra. A term to compensate spectral distortions triggered by windsea waves moving in SAR flight direction has also been introduced. In total, 95 TS-X/TD-X StripMap scenes between 2012 and 2017 were acquired in Eastern Baltic Sea, processed and analysed. The wave height results from SAR images were compared with collocated in situ data from 11 available buoys. The analysed data include both high and low windsea conditions. The comparison of SAR-derived wave heights with measured wave heights shows high agreement with a correlation coefficient r of 0.88. The wind speed, estimated from SAR images, was compared to measurements from 14 collocated in situ stations, yielding a high agreement with an r value of 0.90. This article is focused on the algorithm developments; however, it is also the first study of sea state retrieval in the Baltic Sea using high-resolution satellite-based techniques. The results show the local variability in the wave fields connected to atmospheric features. The observed local wave height can increase by 1–2 m in kilometre-size cells that are accompanied by wind gusts. The developed algorithms are installed in the German Aerospace Center’s (DLR) ground station Neustrelitz and can also be used in near-real-time.