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Coastal lagoons are important sites for nitrogen (N) removal via sediment burial and denitrification. Blooms of heterocystous cyanobacteria may diminish N retention as dinitrogen (N2) fixation offsets atmospheric losses via denitrification. We measured N2 fixation in the Curonian Lagoon, Europe's largest coastal lagoon, to better understand the factors controlling N2 fixation in the context of seasonal changes in phytoplankton community composition and external N inputs. Temporal patterns in N2 fixation were primarily determined by the abundance of heterocystous cyanobacteria, mainly Aphanizomenon flos-aquae, which became abundant after the decline in riverine nitrate inputs associated with snowmelt. Heterocystous cyanobacteria dominated the summer phytoplankton community resulting in strong correlations between chlorophyll a (Chl a) and N2 fixation. We used regression models relating N2 fixation to Chl a, along with remote-sensing-based estimates of Chl a to derive lagoon-scale estimates of N2 fixation. N2 fixation by pelagic cyanobacteria was found to be a significant component of the lagoon's N budget based on comparisons to previously derived fluxes associated with riverine inputs, sediment–water exchange, and losses via denitrification. To our knowledge, this is the first study to derive ecosystem-scale estimates of N2 fixation by combining remote sensing of Chl a with empirical models relating N2 fixation rates to Chl a.

The aim of the H2020 EOMORES project was to develop user-relevant and sustainable commercial downstream services for operational inland and coastal water quality monitoring and reporting. This service has now been developed based on a combination of satellite Earth Observation (EO), in situ and modelling components, with user feedback guiding product and service development throughout. Validation is a key part of the operational service development, in order to ensure the users can be confident in the service provided. Thus, this deliverable presents all validation results, using data collected during the three years of the EOMORES project (2017-2019) to validate the final service products (T5.1). Additionally, this deliverable evaluates the product fitness for purpose (T5.2) and the service functionalities and user satisfaction (T5.3). Overall, the EOMORES service products indicated good performance according to the quantitative uncertainty metrics. EO product validation has been presented by region for each of the service products, including Chl-a, TSM, turbidity, Secchi depth, CDOM, LSWT and macrophyte coverage. From user assessment of the product fitness for purpose, most users received the products and found they were as expected and spatial patterns typically agreed with user expectations. Users highlighted that temporal frequency and spatial coverage was mostly sufficient and an improvement on their routine monitoring programmes. In general, the users were also (very) content with the quality of the service, with scores of 6+ out of 10 from all users for all criteria queried (Timely delivery, Format/interface, Ease of use, Operational capability). Some users are already implementing the EOMORES service for (bathing) water quality monitoring or to inform WFD and MSFD, while many more indicated they hope to use the EOMORES service in the future for a range of purposes. Overall, users were satisfied with the EOMROES service (7+ out of 10). These results indicate there are robust products included in the EOMORES service, and that users are typically satisfied with the service, highlighting the spatial coverage as a key improvement to current monitoring methods for inland and coastal waters.

In this study, we are aiming to test suitability of S2 MSI data for mapping water quality over four morphologically and optically different water bodies, as discovered from in situ measured remote sensing reflectance, in Lithuania: Plateliai and Dusia lakes – categorized as oligo-mesotrophic water bodies; Kaunas reservoir and the Curonian Lagoon – categorized as eutrophic water bodies with massive growth of harmful cyanobacteria blooms. We demonstrate Earth Observation (EO) as a valuable tool for i) water quality assessment according WFD, ii) cyanobacteria scum and anoxia risk mapping and iii) bathing conditions. Research was Co-funded by the Government of Lithuania through an ESA Contract under the PECS (Plan for European Cooperating States), TODAY project.

In recent years satellite water quality information have been used for several purposes, such as: 1) regular and specific monitoring, 2) matters that require Near Real Time information (e.g. when cyanobacterial blooms are threatening the swimming water quality) and 3) for the spatial optimisation of monitoring networks. To the aim, image processing chains need to be set up to transform the radiance recorded by satellite sensors into information on water quality, such as the concentrations of water constituents, the physical values on water surface temperature or maps depicting e.g. the spatial extent of aquatic vegetation. Despite these techniques have been developed since about three decades, there are still ongoing developments to produce accurate and consistent products across all water bodies. This is particularly the case for EOMORES, whose target lakes include a wide range of water types. Moreover, as EOMORES makes use of the latest generation of satellite sensors, consolidated image processing techniques need to be adapted according to the resolutions and spectral bands of these newest sensors. This is especially relevant for Sentinel-2 MSI, since for Sentinel-3 OLCI most of the developments are based on its precursor MERIS on Envisat (2002-2012). This document hence illustrates the image processing chain that have been adapted to produce relevant water quality information for the EOMORES study sites. A first section is dedicated to the description of the methods while the last part describes how the processors have been applied to produce maps (both of the past and recent years, including 2018) of water quality. The selected processors are now being used to set up the operational EOMORES service according to WP6.

Poster presentation at the Globolakes and GEO Aquawatch meeting 28-08-2018

Cyanobacteria blooms have recently been one of the major impact factors compromising lakes and lagoons ecosystems equilibrium. Extremely developed bloom conditions appear as a thick surface cyanobacteria layer (scum) completely covering the water surface. In this study a series of Sentinel-2A (MSI) and LANDSAT-8 (OLI) satellites images acquired in summer periods of most recent years were used to investigate the distribution of cyanobacteria scum in the hypereutrophic Curonian Lagoon (Lithuanian and Russian part). Surface accumulations of algae can be highly complex and variable phenomena: some algae produce thick foams, while others show marked changes in their primary and secondary pigments, and others form aggregations of colloidal inorganic material with complex derived chemical constituents. Presence/absence of cyanobacteria scum was estimated by semi-empirical band ratio algorithm applied to images after atmospheric correction with the 6SV. Simply thresholds algorithms based on band ratio use the bands across the red and near-infrared spectral regions. Radiometric measurements acquired with WISP-3, resampled according to spectral characteristics of MSI bands, compared with atmospheric corrected Rrs products acquired synchronously, show a good agreement. Considering the ratio between two bands (705 nm and 665 nm) the in situ average value was 1.55 (±0.16) respect to 1.61 (±0.08) from MSI. In addition, in order to compare OLI and MSI products, we compared band ratio calculated between band 4 and 3 on one MSI and one OLI synchronous images. Again good agreement was shown by the analysis (R2=0.93). We discovered MSI data to be a valuable tool that can be integrated together with EO data of other missions for operational monitoring of cyanobacteria blooms. The analysis indicated pronounced spatial variability of cyanobacteria scum mainly occurring in early July-late October with the max area of 40,000 ha. Different accumulation features is mainly influenced by ambient weather conditions and hydrodynamics. Respect to the ecological role of scum event, its formation, temporal and spatial evolution is of great interest and therefore the retrospective analysis covering more than 30 years, i.e. nearly entire series of Landsat missions and integrating Sentinel-2 data, will be performed in the near future.

Presentation was given during the meeting of BONUS COCOA (Nutrient cocktails in the coastal zone of the Baltic Sea) project in Warnemunde (Germany) during 23-26 January 2017. The presentation aims to introduce EO-based products for ecological status investigation developed under the H2020 EOMORES and FP7 INFORM projects.

The presentation was presented during “Copernicus Training and Information Session in Vilnius (Lithuania) organized by The European Commission and the Agency for Science, Innovation and Technology in Lithuania. The presentation aims to present the ongoing activities related to Earth Observation for water quality in Lithuania and introduce H2020 project EOMORES.

The H2020 project EOMORES will develop operational monitoring and reporting services for inland and coastal water quality based on a combination of the most up-to-date satellite data, innovative in situ instruments and ecological models. Lakes, reservoirs and coastal water bodies constitute essential components of the hydrological and biogeochemical water cycles, and influence many aspects of ecology, economy, and human welfare, providing ecosystem services in multiple and sometimes conflicting ways. Knowledge about the state of inland and coastal water bodies is therefore of great interest. Project time: 3 year, started 1 December 2016. EOMORES has 9 partners from 6 EU countries, and 13 users

The aim of the study is to test the suitability of Sentinel-2 MSI for mapping phytoplankton in turbid extremely productive inland waters with recurrent cyanobacterial bloom that represent a major concern. These blooms can have an influence on health care costs, on the costs associated with the treatment of water intended for human consumption and on the tourism business. The study sites investigated in this work include the three lakes of Mantua (Italy), a system of fluvial lakes characterised by a patchy distribution of phytoplankton. These shallow, nutrient-enriched and turbid waters host different primary producers and undergo pronounced discharge variations. The other site is the Curonian lagoon (Lithuania and Russian Federation) and with a surface of 1584 km2 is largest lagoon of Europe. It is also very shallow with waters readily mixed by wind action, although weak stratification can occur temporarily during the summer. The system is also highly eutrophic due to external nutrient loads from the tributary Nemunas River. The presence of cyanobacterial toxic metabolites, is a major concern for water quality and human health in the region. The study areas are part of two research projects (FP-7 INFORM (copernicus-inform.eu) and BLASCO (projectblasco.it)) which have been supporting field activities in the last three years gathering fundamental data for evaluating Sentinel-2 data. Five days match-ups with satellite acquisitions resulted available for evaluating satellite products in terms of concentrations of chlorophyll-a (chl-a), as proxy of phytoplankton. Field measurements include aquatic optical properties and limnological data that allowed the development of semi-empirical adaptive algorithms and of spectral inversion techniques of bio-optical modelling to retrieve chl-a. Depending on water leaving reflectance the approaches were integrated because bio-optical modelling inversion performed better for lower chl-a concentrations (

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.