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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. [ABSTRACT FROM AUTHOR]

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 compared to traditional approaches where, e.g., CDOM is estimated from salinity. 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). [ABSTRACT FROM AUTHOR]

Optical-biogeochemical relationships of particulate and dissolved organic matter are presented in support of remote sensing of the Baltic Sea pelagic. This system exhibits strong seasonality in phytoplankton community composition and wide gradients of chromophoric dissolved organic matter (CDOM), properties which are poorly handled by existing remote sensing algorithms. Absorption and scattering properties of particulate matter reflected the seasonality in biological (phytoplankton succession) and physical (thermal stratification) processes. Inherent optical properties showed much wider variability when normalized to the chlorophyll-a concentration compared to normalization to either total suspended matter dry weight or particulate organic carbon. The particle population had the largest optical variability in summer and was dominated by organic matter in both seasons. The geographic variability of CDOM and relationships with dissolved organic carbon (DOC) are also presented. CDOM dominated light absorption at blue wavelengths, contributing 81% (median) of the absorption by all water constituents at 400 nm and 63% at 442 nm. Consequentially, 90% of water-leaving radiance at 412 nm originated from a layer (z90) no deeper than approximately 1.0 m. With water increasingly attenuating light at longer wavelengths, a green peak in light penetration and reflectance is always present in these waters, with z90 up to 3.0–3.5 m depth, whereas z90 only exceeds 5 m at biomass < 5 mg Chla m-3. High absorption combined with a weakly scattering particle population (despite median phytoplankton biomass of 14.1 and 4.3 mg Chla m-3 in spring and summer samples, respectively), characterize this sea as a dark water body for which dedicated or exceptionally robust remote sensing techniques are required. Seasonal and regional optical-biogeochemical models, data distributions, and an extensive set of simulated remote-sensing reflectance spectra for testing of remote sensing algorithms are provided as supplementary data. [ABSTRACT FROM AUTHOR]

To analyze the applicability of direct insertion of total suspended matter (TSM) concentration field based on turbidity derived from satellite data to numerical simulation, dispersion studies of suspended matter in Lake Säkylän Pyhäjärvi (lake area 154 km2; mean depth 5.4 m) were conducted using the 3D COHERENS simulation model. To evaluate the practicality of direct insertion, five cases with different initialization frequencies were conducted: (1) every time, when satellite data were available; (2) every 10 days; (3) 20 days; (4) 30 days; and (5) control run without repeated initialization. To determine the effectiveness of initialization frequency, three methods of comparison were used: simple spatial differences of TSM concentration without biomass in the lake surface layer; averaged spatial differences between initialization data and the forecasts; and time series of TSM concentration and observation data at 1 m depth at the deepest point of the lake. Results showed that direct insertion improves the forecast significantly, even if it is applied less often. [ABSTRACT FROM AUTHOR]

New tools, such as intensive measurements, together with advanced mathematical models, are increasingly needed in water management and environmental research. The new approaches are being developed at Pyhajarvi, a large (155 km2) lake in southwest Finland. Pyhajarvi is highly valuable in terms of water supply, fisheries and recreational use. The ecological state of Pyhajarvi has been closely monitored for decades, particularly since eutrophication became a major concern in the late 1980s. Two relatively new research methods were used to assess the spatial water quality of Pyhajarvi: (i) transect measurements from a moving boat; and (ii) remote sensing data based estimates. First, a flow-through method from a moving boat was successfully used to collect high resolution transect water quality data from the lake over six field campaigns. The method is relatively accurate but costly, and its use is mostly limited to special campaigns and intensive research, but not for long-term monitoring. Second, remote sensing methods were used to retrieve water quality information which was found consistent with the surface measurements from the boat. The estimation of parameters such as turbidity and humic substance concentration is possible with simple algorithms when using remote sensing (MERIS) data. The quantitative estimation of water quality by the methods used here requires concurrent in situ measurements for algorithm training. These methods will be further developed utilizing frequent on-line water quality and weather data from a recently installed lake float. [ABSTRACT FROM AUTHOR]

The aim of the study was to develop a method for assessing phosphorus transport from a large drainage basin. A tentative conceptual model of watershed dynamics was developed consisting of two components: a runoff model and a phosphorus transport model. A similar approach has also been used in previous studies (e.g. Kallio 1992). As the runoff model, a modification of the HBV-model (Vehviläinen 1992) was used. The statistical P transport model was developed in this study. The models were tested against the data of eight subcatchments of the watercourse of Lake Lägelmävesi, central Finland. Runoff calibration was made against total discharges, though in the output data the runoff from forested and agricultural areas were presented separately. A good fit was obtained, the correlation coefficient squared R² against water level being 0.964. The calibration against total phosphorus transport was made using simulated discharge values. In the present version the correlation coefficient squared R² was 0.901. [ABSTRACT FROM AUTHOR]

The colored dissolved organic matter (CDOM) variable is the standard measure of humic substance in waters optics. CDOM is optically characterized by its spectral absorption coefficient, a C D O M at at reference wavelength (e.g., ≈ 440 nm). Retrieval of CDOM is traditionally done using bio-optical models. As an alternative, this paper presents a comparison of five machine learning methods applied to Sentinel-2 and Sentinel-3 simulated reflectance ( R r s ) data for the retrieval of CDOM: regularized linear regression (RLR), random forest regression (RFR), kernel ridge regression (KRR), Gaussian process regression (GPR) and support vector machines (SVR). Two different datasets of radiative transfer simulations are used for the development and training of the machine learning regression approaches. Statistics comparison with well-established polynomial regression algorithms shows optimistic results for all models and band combinations, highlighting the good performance of the methods, especially the GPR approach, when all bands are used as input. Application to an atmospheric corrected OLCI image using the reflectance derived form the alternative neural network (Case 2 Regional) is also shown. Python scripts and notebooks are provided to interested users. [ABSTRACT FROM AUTHOR]

Abstract Canopy-forming macroalgae living on rocky bottoms provide valuable ecosystem services but long-term eutrophication has narrowed their distribution and depth zonation in the Baltic Sea. The spatial distribution of macroalgae is shaped by many factors, such as light, salinity, nutrients and wave exposure. In addition, the lack of suitable hard substrates limits the distribution of algae in many areas. Analysing how the spatial distribution of macroalgae is modified by changes in environmental conditions is relevant for focusing management actions. To quantify the resultant distribution under various environmental and management scenarios, both current environmental conditions and substrate limitation need to be considered. We estimated the potential distribution area of bladderwrack Fucus spp. under 11 water transparency scenarios in 9 Finnish sea areas differing in morphology and eutrophication status. The prevailing averaged long-term water transparency conditions were interpreted from satellite images. Ten scenarios were calculated based on hypothetical changes in euphotic depth from −50% to +50% of the present. Species distribution modelling was used to assess the potential distribution areas of Fucus. In addition, to quantify the influence of substrate limitation, we estimated the average substrate limitation with two correction methods: (i) by using field data from underwater videos within the predicted distribution areas and (ii) by using a habitat model representing the distribution of reefs (i.e. rocky bottoms) in the study area. The decrease of euphotic depth by 50% from the present level narrowed the distribution area of Fucus by 24–53% in the Southwestern archipelago, 55–70% in the Gulf of Finland, 37–66% in the Bothnian Sea and 59–100% in Kvarken. An increase in euphotic depth significantly broadened the spatial distribution of Fucus. Decreasing share of suitable hard substrate along depth gradient however hinders broadening of the distribution area. If all areas were suitable for growth, a 50% increase in euphotic depth would expand the distribution area by 124–803%, depending on area. When only suitable substrates were taken into account, this percentage remained at 9–270%. We conclude that substrate limitation needs to be taken into account when estimating macroalgal species distribution in the marine environment. We show how this can be done also when comprehensive bottom substrate maps are not available. Our results are valuable when setting the targets for environmental management plans, and for balancing the local management measures in a cost effective manner. Graphical abstract Image 1 Highlights • Benthic light conditions affect the distribution of canopy-forming macroalgae Fucus. • Also substrate limits the distribution and zonation of Fucus. • Scenario modelling and substrate correction methods were utilised. • Sea areas differ in substrate composition which affects potential distribution area. • Substrate limitation is more pronounced in inner than in outer archipelago. [ABSTRACT FROM AUTHOR]

Abstract: Given the importance of coloured dissolved organic matter (CDOM) for the structure and function of lake ecosystems, a method to estimate the amount of CDOM in lake waters over large geographic areas would be highly desirable. Advanced Land Imager (ALI) images were acquired in southern Finland (in 2002) and southern Sweden (in 2003) together with in situ measurements of bio-optical properties of 34 lakes (39 measuring stations). Based on this dataset, a band-ratio type algorithm was developed using ALI band 2 and band 3 for estimating CDOM content (absorption of filtrated water at 420 nm) in lakes. Correlation between in situ measured CDOM and the remote sensing estimate of CDOM was high, r 2=0.73. The CDOM retrieval algorithm obtained on the basis of two images and in situ data was validated on a third ALI image (eastern Finland, 2002) that was available in the ALI image archive. In situ water-colour monitoring data from 22 lakes (27 measuring stations) in the third image were available in a database of the Finnish Environment Administration. The water-colour data were converted to CDOM absorption values, which were then compared to the results from a third ALI image. The correlation between remotely estimated and in situ CDOM values in the algorithm validation image was high, r 2=0.83. These results support the conclusion that CDOM content in lakes over a wide range of concentrations (a CDOM(420) between 0.68 and 11.13 m−1) can be mapped using Advanced Land Imager data. [Copyright &y& Elsevier]

We study the use of airborne and simulated satellite remote sensing data for classification of three water quality variables: Secchi depth, turbidity, and chlorophyll a. An extensive airborne spectrometer and ground truth data set obtained in four lake water quality measurement campaigns in southern Finland during 1996-1998 was used in the analysis. The class limits for the water quality variables were obtained from two operational classification standards. When remote sensing data is used, a combination of them proved to be the most suitable. The feasibility of the system for operational use was tested by training and testing the retrieval algorithms with separate data sets. In this case, the classification accuracy is 90% for three Secchi depth classes, 79% for five turbidity classes, and 78% for five chlorophyll a classes. When Airborne hnaging Spectrometer for Applications (AISA) data was spectrally averaged corresponding to Envisat Medium Resolution Imaging Spectrometer (MERIS) channels, the classification accuracy was about the same as in the case of the original AISA channels. [ABSTRACT FROM AUTHOR]

The worldwide economic downturn and the climate change in the beginning of 21st century have stressed the need for cost efficient and systematic operations model for the monitoring and management of surface waters. However, these processes are still all too fragmented and incapable to respond these challenges. For example in Finland, the estimation of the costs and benefits of planned management measures is insufficient. On this account, we present a new operations model to streamline these processes and to ensure the lucid decision making and the coherent implementation which facilitate the participation of public and all the involved stakeholders. The model was demonstrated in the real world management of a lake. The benefits, pitfalls and development needs were identified. After the demonstration, the operations model was put into operation and has been actively used in several other management projects throughout Finland. [ABSTRACT FROM AUTHOR]

Abstract: The concentrations of chlorophyll-a (chl-a), total suspended solids (TSS) and the absorption coefficient of colored dissolved organic matter (a CDOM(400)) are estimated in Case II waters using medium resolution imaging spectrometer (MERIS) satellite (full resolution [FR] level 1b, 300m resolution) and AISA airborne spectrometer data acquired during a spring bloom in the Gulf of Finland, Baltic Sea on April 27, 2004. The accuracy of the estimation is analyzed using empirical band-ratio algorithms together with in situ observations that include water samples analyzed in a laboratory (variation ranges: 22–130μg/l, 2.9–20mg/l, and 1.29–2.61m−1 for chl-a, TSS and a CDOM(400), respectively). Additional in situ estimates (transects) on these characteristics are available through absorption and scattering coefficients measured with an ac-9 absorption and attenuation meter installed in a flow-through system. The retrieval accuracy (R 2) of all three water quality characteristics with MERIS data is close to or above 0.9, while the RMSE is 7.8μg/l (22%), 0.74mg/l (16%) and 0.08m−1 (5%), for chl-a, TSS and a CDOM(400), respectively. The validity of the chl-a algorithm is tested using nine additional data points. The BIAS-error for these points is 5.2μg/l and the RMSE is 10.6μg/l. The effects of changes in the atmospheric characteristics on band-ratio algorithms in cases where no concurrent in situ reference data are available are analyzed using the MODerate spectral resolution atmospheric TRANSmittance algorithm and computer model (MODTRAN). The additional error due to these changes is estimated to be below 20% for the applied ratio algorithms. The water quality data available in the level 2 MERIS-product distributed by the European Space Agency did not include valid results for the date investigated here. [Copyright &y& Elsevier]