Abstract. Colored dissolved organic matter (CDOM) in marine environments impacts primary production due to its absorption effect on the photosynthetically active radiation. In coastal seas, CDOM originates from terrestrial sources predominantly and causes spatial and temporal changing patterns of light absorption which should be considered in marine biogeochemical models. We propose a model approach in which Earth Observation (EO) products are used to define boundary conditions of CDOM concentrations in an ecosystem model of the Baltic Sea. CDOM concentrations in riverine water derived from EO products serve as forcing for the ecosystem model. For this reason, we introduced an explicit CDOM state variable in the model. We show that the light absorption by CDOM in the model can be improved considerably in comparison to approaches where CDOM is estimated from salinity. The model performance increases especially with respect to spatial CDOM patterns due to the consideration of single river properties. A prerequisite is high-quality CDOM data with sufficiently high spatial resolution which can be provided by the new generation of ESA satellite sensor systems (Sentinel 2 MSI and Sentinel 3 OLCI). Such data are essential, especially when local differences in riverine CDOM concentrations exist.

Marginal seas are a dynamic and still to large extent uncertain component of the global carbon cycle. The large temporal and spatial variations of sea-surface partial pressure of carbon dioxide (pCO2) in these areas are driven by multiple complex mechanisms. In this study, we analyzed the variable importance for the sea surface pCO2 estimation in the Baltic Sea and derived monthly pCO2 maps for the marginal sea during the period of July 2002–October 2011. We used variables obtained from remote sensing images and numerical models. The random forest algorithm was employed to construct regression models for pCO2 estimation and produce the importance of different input variables. The study found that photosynthetically available radiation (PAR) was the most important variable for the pCO2 estimation across the entire Baltic Sea, followed by sea surface temperature (SST), absorption of colored dissolved organic matter (aCDOM), and mixed layer depth (MLD). Interestingly, Chlorophyll-a concentration (Chl-a) and the diffuse attenuation coefficient for downwelling irradiance at 490 nm (Kd_490nm) showed relatively low importance for the pCO2 estimation. This was mainly attributed to the high correlation of Chl-a and Kd_490nm to other pCO2-relevant variables (e.g., aCDOM), particularly in the summer months. In addition, the variables’ importance for pCO2 estimation varied between seasons and sub-basins. For example, the importance of aCDOM were large in the Gulf of Finland but marginal in other sub-basins. The model for pCO2 estimate in the entire Baltic Sea explained 63% of the variation and had a root of mean squared error (RMSE) of 47.8 µatm. The pCO2 maps derived with this model displayed realistic seasonal variations and spatial features of sea surface pCO2 in the Baltic Sea. The spatially and seasonally varying variables’ importance for the pCO2 estimation shed light on the heterogeneities in the biogeochemical and physical processes driving the carbon cycling in the Baltic Sea and can serve as an important basis for future pCO2 estimation in marginal seas using remote sensing techniques. The pCO2 maps derived in this study provided a robust benchmark for understanding the spatiotemporal patterns of CO2 air-sea exchange in the Baltic Sea.

Wetlands are the most fragile and threatened ecosystems worldwide, and also one of the most rapidly declining. At the same time wetlands are typically biodiversity hotspots and provide a range of valuable ecosystem services, such as water supply and purification, disaster risk reduction, climate change adaptation, and carbon sequestration.Pressures on wetlands are likely to further intensify in the coming decades due to increased global demand for land and water, and due to climate change. Stakeholders at all levels of governance have to be involved to slow, stop and reverse these processes. However, the information they need on wetland extent, their ecological character, and their ecosystem services is often scattered, sparse and difficult to find and access.The freely available Sentinel satellite data of the Copernicus Programme, as well as the Landsat archive, provide a comprehensive basis to map and inventory wetland areas (extent), to derive information on the ecological status, as well as long- and short-term trends in wetland characteristics. However, making use of these Earth Observation (EO) resources for robust and standardized wetland monitoring requires expert knowledge on often complex data processing techniques, which impedes practical implementation. In this respect, the Satellite-based Wetland Observation Service (SWOS), a Horizon 2020 funded project (www.swos-service.eu) has developed and made disseminated monitoring approaches based on EO data, specifically designed for less experienced satellite data users.The SWOS monitoring tools aim at assisting countries in conducting national wetland inventories for their Sustainable Development Goals (SDG) reporting and monitoring obligations, and additionally facilitates other monitoring obligations such as those required by the Ramsar Convention and supports decision-making in local conservation activities. The four main components of the SWOS approach are: map and indicator production; software development; capacity building; and initializing the GEO Wetlands Community Portal. Wetland managers and data analysists from more than fifty wetland sites and river basins across Europe, the Middle East, and Africa investigated the benefits and limitations of this EO-based wetland mapping and monitoring approach.We describe research that applies the SWOS tools to test their potential for the mapping of wetlands in a case study based in Albania, and show its effectiveness to derive metrics relevant to the monitoring of SDG indicator 6.6.1.

Monthly CHL-a and Secchi Depth (SD) data derived from the full mission data of the Medium Resolution Imaging Spectrometer (MERIS; 2002–2012) were analysed along a horizontal transect from the inner Bråviken bay and out into the open sea. The CHL-a values were calibrated using an algorithm derived from Swedish lakes. Then, calibrated Chl-a and Secchi Depth (SD) estimates were extracted from MERIS data along the transect and compared to conventional monitoring data as well as to data from the Swedish Coastal zone Model (SCM), providing physico-biogeochemical parameters such as temperature, nutrients, Chlorophyll-a (CHL-a) and Secchi depth (SD). A high negative correlation was observed between satellite-derived CHL-a and SD (ρ = −0.91), similar to the in situ relationship established for several coastal gradients in the Baltic proper. We also demonstrate that the validated MERIS-based estimates and data from the SCM showed strong correlations for the variables CHL-a, SD and total nitrogen (TOTN), which improved significantly when analysed on a monthly basis across basins. The relationship between satellite-derived CHL-a and modelled TOTN was also evaluated on a monthly basis using least-square linear regression models. The predictive power of the models was strong for the period May-November (R2: 0.58–0.87), and the regression algorithm for summer was almost identical to the algorithm generated from in situ data in Himmerfjärden bay. The strong correlation between SD and modelled TOTN confirms that SD is a robust and reliable indicator to evaluate changes in eutrophication in the Baltic proper which can be assessed using remote sensing data. Amongst all three assessed methods, only MERIS CHL-a was able to correctly depict the pattern of phytoplankton phenology that is typical for the Baltic proper. The approach of combining satellite data and physio-biogeochemical models could serve as a powerful tool and value-adding complement to the scarcely available in situ data from national monitoring programs. In particular, satellite data will help to reduce uncertainties in long-term monitoring data due to its improved measurement frequency.

Abstract. The use of ground sampled water quality information for global studies is limited due to practical and financial constraints. Remote sensing is a valuable means to overcome such limitations and to provide synoptic views of ambient water quality at appropriate spatio-temporal scales. In past years several large data processing efforts were initiated to provide corresponding data sources. The Diversity II water quality dataset consists of several monthly, yearly and 9-year averaged water quality parameters for 340 lakes worldwide and is based on data from the full ENVISAT MERIS operation period (2002–2012). Existing retrieval methods and datasets were selected after an extensive algorithm intercomparison exercise. Chlorophyll-a, total suspended matter, turbidity, coloured dissolved organic matter, lake surface water temperature, cyanobacteria and floating vegetation maps, as well as several auxiliary data layers, provide a generically specified database that can be used for assessing a variety of locally relevant ecosystem properties and environmental problems. For validation and accuracy assessment, we provide matchup comparisons for 24 lakes and a group of reservoirs representing a wide range of bio-optical conditions. Matchup comparisons for chlorophyll-a concentrations indicate mean absolute errors and bias in the order of median concentrations for individual lakes, while total suspended matter and turbidity retrieval achieve significantly better performance metrics across several lake-specific datasets. We demonstrate the use of the products by illustrating and discussing remotely sensed evidence of lake-specific processes and prominent regime shifts documented in the literature. The Diversity II data are available from https://doi.pangaea.de/10.1594/PANGAEA.871462, and Python scripts for their analysis and visualization are provided at https://github.com/odermatt/diversity/.

Abstract. The use of ground sampled water quality information for global studies is limited due to practical and financial constraints. Remote sensing is a valuable means to overcome such limitations and to provide synoptic views of ambient water quality at appropriate spatio-temporal scales. In past years several large data processing efforts were initiated to provide corresponding data sources. The Diversity II water quality dataset consists of several monthly, yearly and 9-year averaged water quality parameters for 340 lakes worldwide and is based on data from the full ENVISAT MERIS operation period (2002–2012). Existing retrieval methods and datasets were selected after an extensive algorithm intercomparison exercise using in situ reference measurements for more than 40 lakes representing a wide range of bio-optical conditions. Chlorophyll-a, total suspended matter, turbidity, coloured dissolved organic matter, lake surface water temperature, cyanobacteria and floating vegetation maps, as well as several auxiliary data layers, provide a generically specified data basis that can be used for assessing a variety of locally relevant ecosystem properties and environmental problems. We demonstrate the use of the products by illustrating and discussing remotely sensed evidence of lake-specific processes and prominent regime shifts documented in literature. The Diversity II data are available from https://doi.pangaea.de/10.1594/PANGAEA.871462, and Python scripts for their analysis and visualization are provided at https://github.com/odermatt/diversity/.

Abstract Wetlands are a key habitat within the Mediterranean biodiversity hotspot and provide important ecosystem services for human well-being. Remote sensing (RS) has significantly boosted our ability to monitor changes in Mediterranean wetlands, especially in areas where little information is being collected. However, its application to wetlands has sometimes been flawed with uncertainties and unrecognized errors, to a large extent due to the inherent and specific ecological characteristics of Mediterranean wetlands. We present here an overview of the state of the art on RS techniques for mapping and monitoring Mediterranean wetlands, and the remaining challenges: delineating and separating wetland habitat types; mapping water dynamics inside wetlands; and detecting actual wetland trends over time in a context of high, natural variability. The most important lessons learned are that ecologists’ knowledge need to be integrated with RS expertise to achieve a valuable monitoring approach of these ecosystems.

ABSTRACTLake Vänern, Sweden, is one of Europe’s largest lakes and has a historical, cultural, ecological as well as economic importance. Lake water quality monitoring is required by national and international legislations and directives, but present programmes are insufficient to meet the requirements. To complement in situ based monitoring, the possibility to obtain reliable information about spatial and temporal water quality trends in Lake Vänern from the ENVISAT mission’s MERIS instrument was evaluated. The complete archive (2002–2012) of MERIS (Medium Resolution Imaging Spectrometer) full resolution data was processed using the water processor developed by Free University Berlin (FUB) to derive aerosol optical thickness (AOT), remote-sensing reflectance (Rrs) and water quality parameters: chlorophyll-a (chl-a) concentration, coloured dissolved organic matter absorption at 443 nm (CDOM), and total suspended matter (TSM) concentration. The objective was to investigate if, either, FUB reflectance products in combination with potential lake-specific band ratio algorithms for water quality estimation, or directly, FUB water quality products, could complement the existing monitoring programme.Application of lake-specific band ratio algorithms requires high-quality reflectance products based on correctly estimated AOT. The FUB reflectance and AOT products were evaluated using Aerosol Robotic Network – Ocean Color (AERONET-OC) match-up data measured at station Pålgrunden in Lake Vänern. The mean absolute percentage differences (MAPDs) of the final reflectance retrievals at 413, 443, 490, 555, and 665 nm were 510%, 48%, 33%, 34%, and 33%, respectively, corresponding to a large positive bias in 413 nm, positive bias in 443–555 nm, and a negative bias in 665 nm. AOT was strongly overestimated in all bands.The FUB water quality products were evaluated using match-up in situ data of chl-a, filtered absorbance (AbsF(420)) and turbidity as AbsF(420) is related to CDOM and turbidity is strongly related to TSM. The in situ data was collected within the Swedish national and regional monitoring programmes. In order to widen the range of water constituents and add more data to the analysis, data from four large Swedish lakes (Vänern, Vättern, Mälaren, and Hjälmaren) was included in the analysis. High correlation (r ≥ 0.85) between in situ data and MERIS FUB derived water quality estimates were obtained, but the absolute levels were over- (chl-a) or under- (CDOM) estimated. TSM was retrieved without bias.Calibration algorithms were established for chl-a and CDOM based on the match-up data from all four lakes. After calibration of the MERIS FUB data, realistic time series could be derived that were well in line with in situ measurements. The MAPDs of the final retrievals of chl-a, AbsF(420) and Turbidity in Lake Vänern were 37%, 15%, and 35%, respectively, corresponding to mean absolute differences (MADs) of 0.9 µg l−1, 0.17 m−1, and 0.32 mg l−1 in absolute values.The partly inaccurate reflectance estimations in combination with both positive and negative bias imply that successful application of band ratio algorithms is unlikely. The high correlation between MERIS FUB water quality products and in situ data, on the other hand, shows a potential to complement present water quality monitoring programmes and improve the understanding and representability of the temporally and spatially sparse in situ observations. The monitoring potential shown in this study is applicable to the Sentinel-3 mission’s OLCI (Ocean Land Colour Instrument), which was launched by the European Space Agency (ESA) in February 2016 as a part of the EC Copernicus programme.

Remote sensing techniques may provide a higher temporal and spatial resolution than traditional water monitoring methods. We tested if this auxiliary information can be used to (i) explain patterns in fish assemblage composition and (ii) test candidate metrics to assess ecological status in large lake water bodies. We used MERIS-derived layers describing chlorophyll a, total suspended matter, and colored dissolved organic matter (CDOM) overlaid on all available fish monitoring data from the four largest Swedish lakes (Vanern, Vattern, Malaren, and Hjalmaren). We assessed the influence of remote sensing-derived parameters in the pelagic, offshore benthic, and the inshore benthic habitats. Our results demonstrated that chlorophyll a and CDOM together with depth at the sampling site explained a significant part of the variation in the distribution of fish assemblages. These predictors were particularly important not only in pelagic, but also in inshore benthic areas. Furthermore, we identified three potential candidate metrics to assess pressure from eutrophication in large lakes: density of pelagic fishes, biomass of planktivorous species, and the proportion of cyprinids when roach was excluded. Remote sensing was considered a useful tool to support analyses of fish community composition and dynamics.

The need to restore and protect waterbodies from further degradation has resulted in formulation of the European Union Water Framework Directive 2000/60/EC (WFD). The directive aims to harmonize European legislation on water; and member states shall establish a programme for monitoring the status of all waterbodies larger than 0.5 km2, in order to ensure future quality and quantity of inland waters. The biological and physical–chemical status and ecological potential should be assessed and action plans for a sustainable management and protection of freshwater resources should be established. In practice, this means that extensive and expensive sampling programmes are needed. The ecological status of a waterbody can be described by various biological and physical–chemical quality elements, and several of these important ecological parameters can be monitored by space-based instruments: (1) phytoplankton biomass; (2) chlorophyll-a concentration; (3) water transparency; and (4) frequency and intensity of blooms. The objective of this article is to demonstrate how Environmental Satellite/Medium Resolution Imaging Spectrometer and future Sentinel-3/Ocean Land Colour Instrument data can be effectively used to complement traditional water monitoring programmes by adding information with significantly improved spatial coverage and temporal detail to support the WFD status assessment process. Examples are provided for five large European lakes (Peipsi, Võrtsjärv, Vänern, Vättern, Mälaren). Time series based on satellite data and data collected within national and regional monitoring programmes were compiled and compared, to demonstrate good agreement between the two techniques, but also to discuss natural differences and limitations. Furthermore, the ecological status class based on satellite and in situ data for each waterbody was calculated and analysed.