Retrieval of suspended particulate matter from turbidity – model development, validation, and application to MERIS data over the Baltic Sea

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 (R2 = 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

Advancing cyanobacteria biomass estimation from hyperspectral observations: Demonstrations with HICO and PRISMA imagery

Retrieval of the phycocyanin concentration (PC), a characteristic pigment of, and proxy for, cyanobacteria biomass, from hyperspectral satellite remote sensing measurements is challenging due to uncertainties in the remote sensing reflectance (∆Rrs) resulting from atmospheric correction and instrument radiometric noise. Although several individual algorithms have been proven to capture local variations in cyanobacteria biomass in specific regions, their performance has not been assessed on hyperspectral images from satellite sensors. Our work leverages a machine-learning model, Mixture Density Networks (MDNs), trained on a large (N = 939) dataset of collocated in situ chlorophyll-a concentrations (Chla), PCs, and remote sensing reflectance (Rrs) measurements to estimate PC from all relevant spectral bands. The performance of the developed model is demonstrated via PC maps produced from select images of the Hyperspectral Imager for the Coastal Ocean (HICO) and Italian Space Agency's PRecursore IperSpettrale della Missione Applicativa (PRISMA) using a matchup dataset. As input to the MDN, we incorporate a combination of widely used band ratios (BRs) and line heights (LHs) taken from existing multispectral algorithms, that have been proven for both Chla and PC estimation, as well as novel BRs and LHs to increase the overall cyanobacteria biomass estimation accuracy and reduce the sensitivity to ∆Rrs. When trained on a random half of the dataset, the MDN achieves uncertainties of 44.3%, which is less than half of the uncertainties of all viable optimized multispectral PC algorithms. The MDN is notably better than multispectral algorithms at preventing overestimation on low (10 mg m−3). According to our extensive assessments, the developed model is anticipated to enable practical PC products from PRISMA and HICO, therefore the model is promising for planned hyperspectral missions, such as the Plankton Aerosol and Cloud Ecosystem (PACE). This advancement will enhance the complementary roles of hyperspectral radiometry from satellite and low-altitude platforms for quantifying and monitoring cyanobacteria harmful algal blooms at both large and local spatial scales

Satellite-assisted monitoring of water quality to support the implementation of the Water Framework Directive

The EU Water Framework Directive1 (WFD) is an ambitious legislation framework to achieve good ecological and chemical status for all surface waters and good quantitative and chemical status for groundwater by 2027. A total of 111,062 surface waterbodies are presently reported on under the Directive, 46% of which are actively monitored for ecological status. Of these waterbodies 80% are rivers, 16% are lakes, and 4% are coastal and transitional waters. In the last assessment, 4% (4,442) of waterbodies still had unknown ecological status, while in 23% monitoring did not include in situ water sampling to support ecological status assessment2. For individual (mainly biological) assessment criteria the proportion of waterbodies without observation data is much larger; the full scope of monitoring under the WFD is therefore still far from being realised. At the same time, 60% of surface waters did not achieve ‘good’ status in the second river basin management plan and waterbodies in Europe are considered to be at high risk of having poor water quality based on combined microbial, physical and physicochemical indicators3

Spatial patterns of potential toxic planktonic cyanobacteria occurrence in northern part of the coronian lagoon

Curonian Lagoon is a shallow transitional water basin located in the south-eastern part of the Baltic Sea. The southern and central parts of the lagoon contain freshwater due to discharge from the Nemunas River, while the salinity in the northern part varies from 0 to 8 PSU, depending on winds activity affecting brackish water inflow from the Baltic Sea. The investigation was carried out in the fresh-brackish water mixing zone (Influence zone of Baltic Sea), in the central part and Nemunas River influence zone in July-August 2004 - 2006. Changes in physico-chemical parameters, chlorophyll a concentration, phytoplankton and toxic algae cell density were monitored. Totally 223 species and varieties mainly belonging to Chlorophyceae (43 %) and Cyanophyceae (32 %) were found. 26 algae species from 3 algae classes (Cyanophyceae, Chlorophyceae and Dinophyceae) were identified as potential toxic species in the northern part of Curonian Lagoon during 2004 and 2006 summer time. Dominated toxic species Ahpanizomenon flos-aquae, Microcystis aeruginosa, M. viridis, M. wesenbergii, Woronichinia compacta. Phytoplankton biomass in Curonian Lagoon surface ranged from 12,27 to 50,22 mg/l. The peak of phytoplankton (33,11 mg/l) and potential toxic algae (28,67 mg/l) biomass in 2004 summer time was observed near by Klaipeda Strait, were Aphanizomenon flos-aquae contain 36 % from total biomass. In 2005 summer time the highest phytoplankton (50,22 mg/l) and toxic algae (21.46 mg//l) biomass were in the influence zone of Nemunas River. Marine toxic cyanobacteria species Nodularia spumigena were found in all investigating Curonian Lagoon areas. All toxic algae species and Microcystis cyanobacteria recruitment in water column per day was investigated in the littoral sites located by Juodkrante. The highest toxic algae biomass (3,84 mg/l and 6,25 mg/l) at dark period (2:00) was near bottom. Baltic Sea brackish water (salinity was till 4,4 PSU) influence vertical distribution of toxic algae during daytime. Their largest biomass (7,83 mg/l). Twice highest Microcystis biomass (4.91 mg/l) during dark period was in the surface layer. Phytoplankton growth is controlled by the supply of limiting nutrients, usually nitrogen or phosphorus. The Curonian Lagoon may be N- or P-limited depending on the volume of inputs from marine sources. The experiment of enclosure nutrient enrichment shows, that nutrient limitation plays an important role in succession of phytoplankton. Experiment using different nutrient (N and P) manipulations were performed in 70 liter mesocosms of 4 days. Results revealed that phytoplankton and also toxic algae and heterocystous forming cyanobacteria development in the lagoon is strongly affected by nutrient concentration in the water. On the third day of experiment in the mesocosm with phosphorus (biomass – 5,04 mg/l) and with nitrogen (biomass – 4,58 mg/l) biomass of toxic algae species three times greater then in control. The same situation was estimated with heterocystous forming cyanobacteria. Using the algae species composition state, phytoplankton and toxic algae species abundance and biomass data, was indicated hypertrophic-eutrophic status of Curonian Lagoon water in 2004-2005 summer

Evaluation of common reed (Phragmites australis) bed changes in the context of management using earth observation and automatic threshold

ABSTRACTThere is no easy in situ way to monitor large waterbodies for their aquatic vegetation change, especially during mowing works. The objective of this study is to choose the best automatic workflow that would estimate a change in the reed bed area and density over time. This workflow will assess the mowing effect on reeds over 3 years in the Plateliai Lake (Lithuania). Sentinel-2/MSI images were used to derive reed beds using water adjusted vegetation index (WAVI) and normalised difference water index (NDWI). The indices were classified using seven different binary thresholding algorithms. Results were validated with orthophotos gathered from unmanned aerial vehicle surveys in mowed regions and one reference area. Analysis demonstrated that using the NDWI together with the Yen thresholding algorithm generated the best accuracy results, with the highest accuracy resulting with high vegetation areas where the area under the curve values were 0.85 ± 0.17. The changes in estimated density did not show a significant correlation between mowed and reference areas and years. The results indicate that Sentinel-2/MSI is a feasible tool for the evaluation of reed bed change. On this basis, it is recommended to implement it as an additional monitoring tool that covers larger areas than in situ monitoring

Earth Observation-Based Cyanobacterial Bloom Index Testing for Ecological Status Assessment in the Open, Coastal and Transitional Waters of the Baltic and Black Seas

The use of Earth Observation (EO) for water quality monitoring has substantially raised in the recent decade; however, harmonisation of EO-based indicators across the seas to support environmental policies is in great demand. EO-based Cyanobacterial Bloom Index (CyaBI) originally developed for open waters, was tested for transitional and coastal waters of the Lithuanian Baltic Sea and the Ukrainian Black Sea during 2006–2019. Among three tested neural network-based processors (FUB-CSIRO, C2RCC, standard Level-2 data), the FUB-CSIRO applied to Sentinel-3 OLCI images was the most appropriate for the retrieval of chlorophyll-a in both seas (R2 = 0.81). Based on 147 combined MERIS and OLCI synoptic satellite images for the Baltic Sea and 234 for the Black Sea, it was shown that the CyaBI corresponds to the eutrophication patterns and trends over the open, coastal and transitional waters. In the Baltic Sea, the cyanobacteria blooms mostly originated from the central part and the outflow of the Curonian Lagoon. In the Black Sea, they occurred in the coastal region and shelf zone. The recent decrease in bloom presence and its severity were revealed in the areas with riverine influence and coastal waters. Intensive blooms significantly enhanced the short-term increase in sea surface temperature (mean ≤ 0.7 °C and max ≤ 7.0 °C) compared to surrounding waters, suggesting that EO data originating from thermal infrared sensors could also be integrated for the ecological status assessment

U-Net Performance for Beach Wrack Segmentation: Effects of UAV Camera Bands, Height Measurements, and Spectral Indices

This study delves into the application of the U-Net convolutional neural network (CNN) model for beach wrack (BW) segmentation and monitoring in coastal environments using multispectral imagery. Through the utilization of different input configurations, namely, “RGB”, “RGB and height”, “5 bands”, “5 bands and height”, and “Band ratio indices”, this research provides insights into the optimal dataset combination for the U-Net model. The results indicate promising performance with the “RGB” combination, achieving a moderate Intersection over Union (IoU) of 0.42 for BW and an overall accuracy of IoU = 0.59. However, challenges arise in the segmentation of potential BW, primarily attributed to the dynamics of light in aquatic environments. Factors such as sun glint, wave patterns, and turbidity also influenced model accuracy. Contrary to the hypothesis, integrating all spectral bands did not enhance the model’s efficacy, and adding height data acquired from UAVs decreased model precision in both RGB and multispectral scenarios. This study reaffirms the potential of U-Net CNNs for BW detection, emphasizing the suitability of the suggested method for deployment in diverse beach geomorphology, requiring no high-end computing resources, and thereby facilitating more accessible applications in coastal monitoring and management

Macrophytes and their wrack as a habitat for faecal indicator bacteria and Vibrio in coastal marine environments

Waterborne pathogenic bacteria, including faecal indicator bacteria and potentially pathogenic Vibrio, are a global concern for diseases transmitted through water. A systematic review was conducted to analyse publications that investigated these bacteria in relation to macrophytes (seagrasses and macroalgae) in coastal marine environments. The highest quantities of FIB were found on brown algae and seagrasses, and the highest quantities of Vibrio bacteria were on red algae. The most extensively studied macrophyte group was brown algae, green algae were the least researched. Macrophyte wrack was found to favor the presence of FIB, but there is a lack of information about Vibrio quantities in this environment. To understand the role of Vibrio bacteria that are pathogenic to humans, molecular methods complementary to cultivation methods should be used. Further research is needed to understand the underlying mechanisms of FIB and potentially pathogenic Vibrio with macrophytes and their microbiome in the coastal marine environment

An Evaluation of Sun-Glint Correction Methods for UAV-Derived Secchi Depth Estimations in Inland Water Bodies

This study investigates the application of unoccupied aerial vehicles (UAVs) equipped with a Micasense RedEdge-MX multispectral camera for the estimation of Secchi depth (SD) in inland water bodies. The research analyzed and compared five sun-glint correction methodologies—Hedley, Goodman, Lyzenga, Joyce, and threshold-removed glint—to model the SD values derived from UAV multispectral imagery, highlighting the role of reflectance accuracy and algorithmic precision in SD modeling. While Goodman’s method showed a higher correlation (0.92) with in situ SD measurements, Hedley’s method exhibited the smallest average deviation (0.65 m), suggesting its potential in water resource management, environmental monitoring, and ecological modeling. The study also underscored the quasi-analytical algorithm (QAA) potential in estimating SD due to its flexibility to process data from various sensors without requiring in situ measurements, offering scalability for large-scale water quality surveys. The accuracy of SD measures calculated using QAA was related to variability in water constituents of colored dissolved organic matter and the solar zenith angle. A practical workflow for SD acquisition using UAVs and multispectral data is proposed for monitoring inland water bodies