Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria

Monitoring phycocyanin in global inland waters by remote sensing: Progress and future developments

Cyanobacterial blooms are increasingly becoming major threats to global inland aquatic ecosystems. Phycocyanin (PC), a pigment unique to cyanobacteria, can provide important reference for the study of cyanobacterial blooms warning. New satellite technology and cloud computing platforms have greatly improved research on PC, with the average number of studies examining it having increased from 5 per year before 2018 to 17 per year thereafter. Many empirical, semi-empirical, semi-analytical, quasi-analytical algorithm (QAA) and machine learning (ML) algorithms have been developed based on unique absorption characteristics of PC at approximately 620 nm. However, most models have been developed for individual lakes or clusters of them in specific regions, and their applicability at greater spatial scales requires evaluation. A review of optical mechanisms, principles and advantages and disadvantages of different model types, performance advantages and disadvantages of mainstream sensors in PC remote sensing inversion, and an evaluation of global lacustrine PC datasets is needed. We examine 230 articles from the Web of Science citation database between 1900 and 2024, summarize 57 of them that deal with construction of PC inversion models, and compile a list of 6526 PC sampling sites worldwide. This review proposed the key to achieving global lacustrine PC remote sensing inversion and spatiotemporal evolution analysis is to fully use existing multi-source remote sensing big data platforms, and a deep combination of ML and optical mechanisms, to classify the object lakes in advance based on lake optical characteristics, eutrophication level, water depth, climate type, altitude, population density within the watershed. Additionally, integrating data from multi-source satellite sensors, ground-based observations, and unmanned aerial vehicles, will enable future development of global lacustrine PC remote estimation, and contribute to achieving United Nations Sustainable Development Goals inland water goals.

Mapping satellite-derived chlorophyll-a concentrations from 2013 to 2023 in Western Lake Ontario using Landsat 8 and 9 imagery

Algal blooms are a major environmental issue in many freshwater environments. While traditional in-situ measurements remain indispensable to monitor algal dynamics, they offer only limited spatiotemporal coverage, especially when dealing with large water bodies. Satellite remote sensing can help overcome this limitation. Here, a semi-empirical model for retrieving surface water Chlorophyll-a (Chl-a) concentrations, a proxy of phytoplankton biomass, was developed for the western basin of Lake Ontario, one of the Laurentian Great Lakes. ACOLITE-corrected Landsat 8 and 9 imagery between 2013 and 2023 was calibrated and verified with local in-situ Chl-a measurements. The nearshore areas of Western Lake Ontario, including the semi-enclosed Hamilton Harbour, are prone to algal blooms, while oligotrophic conditions prevail in the offshore areas. Three bloom indicators-intensity, extent, and severity-were used to characterize the variability and seasonality of algal blooms in different areas of the lake. Time-series analyses revealed contrasting temporal trends in Chl-a concentrations of the nearshore and offshore waters over the eleven-year period of observation. Analysis of external factors impacting algal blooms in Western Lake Ontario and Hamilton Harbour revealed temperature, wind speed, and cloud cover as the most influential, with around 80 % of blooms occurring under moderate conditions (temperature 4-26 °C and wind speed 2.5-5. m s-1). Overall, our research underlines the great potential for cost-effective monitoring of algal dynamics in large lakes, utilizing publicly available satellite imagery, in order to support eutrophication management.

Potential health risk assessment of cyanobacteria across global lakes

Cyanobacterial blooms pose environmental and health risks due to their production of toxic secondary metabolites. While current methods for assessing these risks have focused primarily on bloom frequency and intensity, the lack of comprehensive and comparable data on cyanotoxins makes it challenging to rigorously evaluate these health risks. In this study, we examined 750 metagenomic data sets collected from 103 lakes worldwide. Our analysis unveiled the diverse distributions of cyanobacterial communities and the genes responsible for cyanotoxin production across the globe. Our approach involved the integration of cyanobacterial biomass, the biosynthetic potential of cyanotoxin, and the potential effects of these toxins to establish potential cyanobacterial health risks. Our findings revealed that nearly half of the lakes assessed posed medium to high health risks associated with cyanobacteria. The regions of greatest concern were East Asia and South Asia, particularly in developing countries experiencing rapid industrialization and urbanization. Using machine learning techniques, we mapped potential cyanobacterial health risks in lakes worldwide. The model results revealed a positive correlation between potential cyanobacterial health risks and factors such as temperature, N2O emissions, and the human influence index. These findings underscore the influence of these variables on the proliferation of cyanobacterial blooms and associated risks. By introducing a novel quantitative method for monitoring potential cyanobacterial health risks on a global scale, our study contributes to the assessment and management of one of the most pressing threats to both aquatic ecosystems and human health.

IMPORTANCE

Our research introduces a novel and comprehensive approach to potential cyanobacterial health risk assessment, offering insights into risk from a toxicity perspective. The distinct geographical variations in cyanobacterial communities coupled with the intricate interplay of environmental factors underscore the complexity of managing cyanobacterial blooms at a global scale. Our systematic and targeted cyanobacterial surveillance enables a worldwide assessment of cyanobacteria-based potential health risks, providing an early warning system.

Expanding the Application of Sentinel-2 Chlorophyll Monitoring across United States Lakes

Eutrophication of inland lakes poses various societal and ecological threats, making water quality monitoring crucial. Satellites provide a comprehensive and cost-effective supplement to traditional in situ sampling. The Sentinel-2 MultiSpectral Instrument (S2 MSI) offers unique spectral bands positioned to quantify chlorophyll a, a water-quality and trophic-state indicator, along with fine spatial resolution, enabling the monitoring of small waterbodies. In this study, two algorithms-the Maximum Chlorophyll Index (MCI) and the Normalized Difference Chlorophyll Index (NDCI)-were applied to S2 MSI data. They were calibrated and validated using in situ chlorophyll a measurements for 103 lakes across the contiguous U.S. Both algorithms were tested using top-of-atmosphere reflectances (ρ t), Rayleigh-corrected reflectances (ρ s), and remote sensing reflectances (R rs ). MCI slightly outperformed NDCI across all reflectance products. MCI using ρ t showed the best overall performance, with a mean absolute error factor of 2.08 and a mean bias factor of 1.15. Conversion of derived chlorophyll a to trophic state improved the potential for management applications, with 82% accuracy using a binary classification. We report algorithm-to-chlorophyll-a conversions that show potential for application across the U.S., demonstrating that S2 can serve as a monitoring tool for inland lakes across broad spatial scales.

Relation between the relative abundance and collapse of Aphanizomenon flos-aquae and microbial antagonism in Upper Klamath Lake, Oregon

Aphanizomenon flos-aquae (AFA) is the dominant filamentous cyanobacterium that develops into blooms in Upper Klamath Lake, Oregon, each year. During AFA bloom and collapse, ecosystem conditions for endangered Lost River and shortnose suckers deteriorate, thus motivating the need to identify processes that limit AFA abundance and decline. Here, we investigate the relations between AFA and other members of the microbial community (photosynthetic and nonphotosynthetic bacteria and archaea), how those relations impact abundance and collapse of AFA, and the types of microbial conditions that suppress AFA. We found significant spatial variation in AFA relative abundance during the 2016 bloom period using 16S rRNA sequencing. The Pelican Marina site had the lowest AFA relative abundance, and this was coincident with increased relative abundance of Candidatus Sericytochromatia, Flavobacterium, and Rheinheimera, some of which are known AFA antagonists. The AFA collapse coincided with phosphorus limitation relative to nitrogen and the increased relative abundance of Cyanobium and Candidatus Sericytochromatia, which outcompete AFA when dissolved inorganic nitrogen is available. The data collected in this study indicate the importance of dissolved inorganic nitrogen combined with microbial community structure in suppressing AFA abundance.

An assessment of a biosensor system for the quantification of microcystins in freshwater cyanobacterial blooms

Microcystin-producing cyanobacterial blooms are a global issue threatening drinking water supplies and recreation on lakes and beaches. Direct measurement of microcystins is the only way to ensure waters have concentrations below guideline concentrations; however, analyzing water for microcystins takes several hours to days to obtain data. We tested LightDeck Diagnostics' bead beater cell lysis and two versions of the quantification system designed to give microcystin concentrations within 20 min and compared it to the standard freeze-thaw cycle lysis method and ELISA quantification. The bead beater lyser was only 30 % effective at extracting microcystins compared to freeze-thaw. When considering freeze-thaw samples analyzed in 2021, there was good agreement between ELISA and LightDeck version 2 (n = 152; R2 = 0.868), but the LightDeck slightly underestimated microcystins (slope of 0.862). However, we found poor relationships between LightDeck version 2 and ELISA in 2022 (n = 49, slopes 0.60 to 1.6; R2 < 0.6) and LightDeck version 1 (slope = 1.77 but also a high number of less than quantifiable concentrations). After the quantification issues are resolved, combining the LightDeck system with an already-proven rapid lysis method (such as microwaving) will allow beach managers and water treatment operators to make quicker, well-informed decisions.

Effects of wastewater treatment plant effluent on microbial risks of pathogens and their antibiotic resistance in the receiving river

The increase in effluent discharge from wastewater treatment plants (WWTPs) into urban rivers has raised concerns about the potential effects on pathogen risks. This study utilized metagenomic sequencing combined with flow cytometry to analyze pathogen concentrations and antibiotic resistance in a typical effluent-receiving river. Quantitative microbial risk assessment (QMRA) was employed to assess the microbial risks of pathogens. The results indicated obvious spatial-temporal differences (i.e., summer vs. winter and effluent vs. river) in microbial composition. Microcystis emerged as a crucial species contributing to these variations. Pathogen concentrations were found to be higher in the river than in the effluent, with the winter exhibiting higher concentrations compared to the summer. The effluent discharge slightly increased the pathogen concentrations in the river in summer but dramatically reduced them in winter. The combined effects of cyanobacterial bloom and high temperature were considered key factors suppressing pathogen concentrations in summer. Moreover, the prevalence of antibiotic resistance of pathogens in the river was inferior to that in the effluent, with higher levels in winter than in summer. Three high-concentration pathogens (Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa) were selected for QMRA. The results showed that the risks of pathogens exceeded the recommended threshold value. Escherichia coli posed the highest risks. And the fishing scenario posed significantly higher risks than the walking scenario. Importantly, the effluent discharge helped reduce the microbial risks in the receiving river in winter. The study contributes to the management and decision-making regarding microbial risks in the effluent-receiving river.

Satellite Remote Sensing: A Tool to Support Harmful Algal Bloom Monitoring and Recreational Health Advisories in a California Reservoir

Cyanobacterial harmful algal blooms (cyanoHABs) can harm people, animals, and affect consumptive and recreational use of inland waters. Monitoring cyanoHABs is often limited. However, chlorophyll-a (chl-a) is a common water quality metric and has been shown to have a relationship with cyanobacteria. The World Health Organization (WHO) recently updated their previous 1999 cyanoHAB guidance values (GVs) to be more practical by basing the GVs on chl-a concentration rather than cyanobacterial counts. This creates an opportunity for widespread cyanoHAB monitoring based on chl-a proxies, with satellite remote sensing (SRS) being a potentially powerful tool. We used Sentinel-2 (S2) and Sentinel-3 (S3) to map chl-a and cyanobacteria, respectively, classified chl-a values according to WHO GVs, and then compared them to cyanotoxin advisories issued by the California Department of Water Resources (DWR) at San Luis Reservoir, key infrastructure in California's water system. We found reasonably high rates of total agreement between advisories by DWR and SRS, however rates of agreement varied for S2 based on algorithm. Total agreement was 83% for S3, and 52%-79% for S2. False positive and false negative rates for S3 were 12% and 23%, respectively. S2 had 12%-80% false positive rate and 0%-38% false negative rate, depending on algorithm. Using SRS-based chl-a GVs as an early indicator for possible exposure advisories and as a trigger for in situ sampling may be effective to improve public health warnings. Implementing SRS for cyanoHAB monitoring could fill temporal data gaps and provide greater spatial information not available from in situ measurements alone.

Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs

This forecasting approach may be useful for water managers and associated public health managers to predict near-term future high-risk cyanobacterial harmful algal blooms (cyanoHAB) occurrence. Freshwater cyanoHABs may grow to excessive concentrations and cause human, animal, and environmental health concerns in lakes and reservoirs. Knowledge of the timing and location of cyanoHAB events is important for water quality management of recreational and drinking water systems. No quantitative tool exists to forecast cyanoHABs across broad geographic scales and at regular intervals. Publicly available satellite monitoring has proven effective in detecting cyanobacteria biomass near-real time within the United States. Weekly cyanobacteria abundance was quantified from the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite as the response variable. An Integrated Nested Laplace Approximation (INLA) hierarchical Bayesian spatiotemporal model was applied to forecast World Health Organization (WHO) recreation Alert Level 1 exceedance >12 μg L-1 chlorophyll-a with cyanobacteria dominance for 2192 satellite resolved lakes in the United States across nine climate zones. The INLA model was compared against support vector classifier and random forest machine learning models; and Dense Neural Network, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Gneural Network (GNU) neural network models. Predictors were limited to data sources relevant to cyanobacterial growth, readily available on a weekly basis, and at the national scale for operational forecasting. Relevant predictors included water surface temperature, precipitation, and lake geomorphology. Overall, the INLA model outperformed the machine learning and neural network models with prediction accuracy of 90% with 88% sensitivity, 91% specificity, and 49% precision as demonstrated by training the model with data from 2017 through 2020 and independently assessing predictions with data from the 2021 calendar year. The probability of true positive responses was greater than false positive responses and the probability of true negative responses was less than false negative responses. This indicated the model correctly assigned lower probabilities of events when they didn't exceed the WHO Alert Level 1 threshold and assigned higher probabilities when events did exceed the threshold. The INLA model was robust to missing data and unbalanced sampling between waterbodies.

Remote estimation of phycocyanin concentration in inland waters based on optical classification

In recent years, under the dual pressure of climate change and human activities, the cyanobacteria blooms in inland waters have become a threat to global aquatic ecosystems and the environment. Phycocyanin (PC), a diagnostic pigment of cyanobacteria, plays an essential role in the detection and early warning of cyanobacterial blooms. In this context, accurate estimation of PC concentration in turbid waters by remote sensing is challenging due to optical complexity and weak optical signal. In this study, we collected a comprehensive dataset of 640 pairs of in situ measured pigment concentration and the Ocean and Land Color Instrument (OLCI) reflectance from 25 lakes and reservoirs in China during 2020-2022. We then developed a framework consisting of the water optical classification algorithm and three candidate algorithms: baseline height, band ratio, and three-band algorithm. The optical classification method used remote sensing reflectance (Rrs) baseline height in three bands: Rrs(560), Rrs(647) and Rrs(709) to classify the samples into five types, each with a specific spectral shape and water quality character. The improvement of PC estimation accuracy for optically classified waters was shown by comparison with unclassified waters with RMSE = 72.6 μg L-1, MAPE = 80.4 %, especially for the samples with low PC concentration. The results show that the band ratio algorithm has a strong universality, which is suitable for medium turbid and clean water. In addition, the three-band algorithm is only suitable for medium turbid water, and the line height algorithm is only suitable for high PC content water. Furthermore, the five distinguished types with significant differences in the value of the PC/Chla ratio well indicated the risk rank assessment of cyanobacteria. In conclusion, the proposed framework in this paper solved the problem of PC estimation accuracy problem in optically complex waters and provided a new strategy for water quality inversion in inland waters.

Recent changes in cyanobacteria algal bloom magnitude in large lakes across the contiguous United States

Cyanobacterial blooms in inland lakes produce large quantities of biomass that impact drinking water systems, recreation, and tourism and may produce toxins that can adversely affect public health. This study analyzed nine years of satellite-derived bloom records and compared how the bloom magnitude has changed from 2008-2011 to 2016-2020 in 1881 of the largest lakes across the contiguous United States (CONUS). We determined bloom magnitude each year as the spatio-temporal mean cyanobacteria biomass from May to October and in concentrations of chlorophyll-a. We found that bloom magnitude decreased in 465 (25 %) lakes in the 2016-2020 period. Conversely, there was an increase in bloom magnitude in only 81 lakes (4 %). Bloom magnitude either didn't change, or the observed change was in the uncertainty range in the majority of the lakes (n = 1335, 71 %). Above-normal wetness and normal or below-normal maximum temperature over the warm season may have caused the decrease in bloom magnitude in the eastern part of the CONUS in recent years. On the other hand, a hotter and dryer warm season in the western CONUS may have created an environment for increased algal biomass. While more lakes saw a decrease in bloom magnitude, the pattern was not monotonic over the CONUS. The variations in temporal changes in bloom magnitude within and across climatic regions depend on the interactions between land use land cover (LULC) and physical factors such as temperature and precipitation. Despite expectations suggested by recent global studies, bloom magnitude has not increased in larger US lakes over this time period.

Microcystins risk assessment in lakes from space: Implications for SDG 6.1 evaluation

Cyanobacterial blooms release a large number of algal toxins (e.g., Microcystins, MCs) and seriously threaten the safety of drinking water sources what the SDG 6.1 pursues (to provide universal access to safe drinking water by 2030, United Nations Sustainable Development Goal). Nevertheless, algal toxins in lake water have not been routinely monitored and evaluated well and frequently so far. In this study, a total of 100 large lakes (>25 km2) in densely populated eastern China were studied, and a remote sensing scheme of human health risks from MCs based on Sentinel-3 OLCI data was developed. The spatial and temporal dynamics of MCs risk in eastern China lakes since OLCI satellite observation data (2016-2021) were first mapped. The results showed that most of the large lakes in eastern China (80 out of 100) were detected with the occurrence of a high risk of more than 1 pixel (300×300 m) at least once. Fortunately, in terms of lake areas, the frequency of high human health risks in most waters (70.93% of total lake areas) was as less as 1%. This indicates that drinking water intakes can be set in most waters from the perspective of MCs, yet the management departments are required to reduce cyanobacterial blooms. This study highlights the potential of satellite in monitoring and assessing the risk of algal toxins and ensuring drinking water safety. It is also an important reference for SDG 6.1 reporting for lakes that lack routine monitoring.

Determining the Spectral Requirements for Cyanobacteria Detection for the CyanoSat Hyperspectral Imager with Machine Learning

This study determines an optimal spectral configuration for the CyanoSat imager for the discrimination and retrieval of cyanobacterial pigments using a simulated dataset with machine learning (ML). A minimum viable spectral configuration with as few as three spectral bands enabled the determination of cyanobacterial pigments phycocyanin (PC) and chlorophyll-a (Chl-a) but may not be suitable for determining cyanobacteria composition. A spectral configuration with about nine ideally positioned spectral bands enabled estimation of the cyanobacteria-to-algae ratio (CAR) and pigment concentrations with almost the same accuracy as using all 300 spectral channels. A narrower spectral band full-width half-maximum (FWHM) did not provide improved performance compared to the nominal 12 nm configuration. In conclusion, continuous sampling of the visible spectrum is not a requirement for cyanobacterial detection, provided that a multi-spectral configuration with ideally positioned, narrow bands is used. The spectral configurations identified here could be used to guide the selection of bands for future ocean and water color radiometry sensors.

Identifying lakes at risk of toxic cyanobacterial blooms using satellite imagery and field surveys across the United States

Harmful algal blooms caused by cyanobacteria are a threat to global water resources and human health. Satellite remote sensing has vastly expanded spatial and temporal data on lake cyanobacteria, yet there is still acute need for tools that identify which waterbodies are at-risk for toxic cyanobacterial blooms. Algal toxins cannot be directly detected through imagery but monitoring toxins associated with cyanobacterial blooms is critical for assessing risk to the environment, animals, and people. The objective of this study is to address this need by developing an approach relating satellite imagery on cyanobacteria with field surveys to model the risk of toxic blooms among lakes. The Medium Resolution Imaging Spectrometer (MERIS) and United States (US) National Lakes Assessments are leveraged to model the probability among lakes of exceeding lower and higher demonstration thresholds for microcystin toxin, cyanobacteria, and chlorophyll a. By leveraging the large spatial variation among lakes using two national-scale data sources, rather than focusing on temporal variability, this approach avoids many of the previous challenges in relating satellite imagery to cyanotoxins. For every satellite-derived lake-level Cyanobacteria Index (CI_cyano) increase of 0.01 CI_cyano/km2, the odds of exceeding six bloom thresholds increased by 23-54 %. When the models were applied to the 2192 satellite monitored lakes in the US, the number of lakes identified with ≥75 % probability of exceeding the thresholds included as many as 335 lakes for the lower thresholds and 70 lakes for the higher thresholds, respectively. For microcystin, the models identified 162 and 70 lakes with ≥75 % probability of exceeding the lower (0.2 μg/L) and higher (1.0 μg/L) thresholds, respectively. This approach represents a critical advancement in using satellite imagery and field data to identify lakes at risk for developing toxic cyanobacteria blooms. Such models can help translate satellite data to aid water quality monitoring and management.

Cyanobacterial Algal Bloom Monitoring: Molecular Methods and Technologies for Freshwater Ecosystems

Cyanobacteria (blue-green algae) can accumulate to form harmful algal blooms (HABs) on the surface of freshwater ecosystems under eutrophic conditions. Extensive HAB events can threaten local wildlife, public health, and the utilization of recreational waters. For the detection/quantification of cyanobacteria and cyanotoxins, both the United States Environmental Protection Agency (USEPA) and Health Canada increasingly indicate that molecular methods can be useful. However, each molecular detection method has specific advantages and limitations for monitoring HABs in recreational water ecosystems. Rapidly developing modern technologies, including satellite imaging, biosensors, and machine learning/artificial intelligence, can be integrated with standard/conventional methods to overcome the limitations associated with traditional cyanobacterial detection methodology. We examine advances in cyanobacterial cell lysis methodology and conventional/modern molecular detection methods, including imaging techniques, polymerase chain reaction (PCR)/DNA sequencing, enzyme-linked immunosorbent assays (ELISA), mass spectrometry, remote sensing, and machine learning/AI-based prediction models. This review focuses specifically on methodologies likely to be employed for recreational water ecosystems, especially in the Great Lakes region of North America.

Remote sensing for mapping algal blooms in freshwater lakes: a review

A large number of freshwater lakes around the world show recurring harmful algal blooms, particularly cyanobacterial blooms, that affect public health and ecosystem integrity. Prediction, early detection, and monitoring of algal blooms are inevitable for the mitigation and management of their negative impacts on the environment and human beings. Remote sensing provides an effective tool for detecting and spatiotemporal monitoring of these events. Various remote sensing platforms, such as ground-based, spaceborne, airborne, and UAV-based, have been used for mounting sensors for data acquisition and real-time monitoring of algal blooms in a cost-effective manner. This paper presents an updated review of various remote sensing platforms, data types, and algorithms for detecting and monitoring algal blooms in freshwater lakes. Recent studies on remote sensing using sophisticated sensors mounted on UAV platforms have revolutionized the detection and monitoring of water quality. Image processing algorithms based on Artificial Intelligence (AI) have been improved recently and predicting algal blooms based on such methods will have a key role in mitigating the negative impacts of eutrophication in the future.

Increased chlorophyll-a concentration in Barra Bonita reservoir during extreme drought periods

Climate projections models indicate that longer periods of droughts are expected within the next 100 years in various parts of South America. To understand the effects of longer periods of droughts on aquatic environments, we investigated the response of chlorophyll-a (Chl-a) concentration to recent severe drought events in the Barra Bonita Hydroelectric Reservoir (BBHR) in São Paulo State, Brazil. We used satellite imagery to estimate the Chl-a concentration from 2014 to 2020 using the Slope Index (NRMSE of 18.92% and bias of -0.20 mg m^-3). Ancillary data such as precipitation, water level and air temperature from the same period were also used. Drought events were identified using the standardized precipitation index (SPI). In addition, we computed the probability of future drought events. Two periods showed extremely dry conditions: 1) January-February (2014) and 2) April-May (2020). Both periods were characterized by a recurrence probability of 1in every 50 years. The highest correlation was observed between Chl-a concentration and SPI (-0.97) in 2014, while Chl-a had had the highest correlation with water level (-0.59) in 2020. These results provide new insights into the influence of extreme drought events on the Chl-a concentration in the BBHR and their relationship with other climate variables and reservoir water levels. Drought events imply less rainfall, higher temperatures, and atmospheric dryness, and these factors affect evaporation and the water levels in the reservoir.

Remote sensing of the cyanobacteria life cycle: A mesocosm temporal assessment of a Microcystis sp. bloom using coincident unmanned aircraft system (UAS) hyperspectral imagery and ground sampling efforts

Remote sensing technologies offer a consistent, spatiotemporal approach to assess water quality, which includes the detection, monitoring, and forecasting of cyanobacteria harmful algal blooms. In this study, a series of ex-situ mesoscale experiments were conducted to first develop and then monitor a Microcystis sp. bloom using a hyperspectral sensor mounted on an unmanned aircraft system (UAS) along with coincident ground sampling efforts including laboratory analyses and in-situ field probes. This approach allowed for the simultaneous evaluation of both bloom physiology (algal growth stages/life cycle) and data collection method on the performance of a suite of 41 spectrally-derived water quality algorithms across three water quality indicators (chlorophyll a, phycocyanin and turbidity) in a controlled environment. Results indicated a strong agreement between Lab and Field-based methods for all water quality indicators independent of growth phase, with regression R²-values above 0.73 for mean absolute percentage error (MAPE) and 0.87 for algorithm R² values. Three of the 41 algorithms evaluated met predetermined performance criteria (MAPE and algorithm R² values); however, in general, algal growth phase had a substantial impact on algorithm performance, especially those with blue and violet wave bands. This study highlights the importance of co-validating sensor technologies with appropriate ground monitoring methods to gain foundational knowledge before deploying new technologies in large-scale field efforts.

Quantification of microcystin production and biodegradation rates in the western basin of Lake Erie

Cyanobacterial biomass forecasts currently cannot predict the concentrations of microcystin, one of the most ubiquitous cyanotoxins that threaten human and wildlife health globally. Mechanistic insights into how microcystin production and biodegradation by heterotrophic bacteria change spatially and throughout the bloom season can aid in toxin concentration forecasts. We quantified microcystin production and biodegradation during two growth seasons in two western Lake Erie sites with different physicochemical properties commonly plagued by summer Microcystis blooms. Microcystin production rates were greater with elevated nutrients than under ambient conditions and were highest nearshore during the initial phases of the bloom, and production rates were lower in later bloom phases. We examined biodegradation rates of the most common and toxic microcystin by adding extracellular stable isotope-labeled microcystin-LR (1 μg L-1), which remained stable in the abiotic treatment (without bacteria) with minimal adsorption onto sediment, but strongly decreased in all unaltered biotic treatments, suggesting biodegradation. Greatest biodegradation rates (highest of -8.76 d-1, equivalent to the removal of 99.98% in 18 h) were observed during peak bloom conditions, while lower rates were observed with lower cyanobacteria biomass. Cell-specific nitrogen incorporation from microcystin-LR by nanoscale imaging mass spectrometry showed that a small percentage of the heterotrophic bacterial community actively degraded microcystin-LR. Microcystin production and biodegradation rates, combined with the microcystin incorporation by single cells, suggest that microcystin predictive models could be improved by incorporating toxin production and biodegradation rates, which are influenced by cyanobacterial bloom stage (early vs. late bloom), nutrient availability, and bacterial community composition.

A validation of satellite derived cyanobacteria detections with state reported events and recreation advisories across U.S. lakes

Cyanobacteria harmful algal blooms (cyanoHABs) negatively affect ecological, human, and animal health. Traditional methods of validating satellite algorithms with data from water samples are often inhibited by the expense of quantifying cyanobacteria indicators in the field and the lack of public data. However, state recreation advisories and other recorded events of cyanoHAB occurrence reported by local authorities can serve as an independent and publicly available dataset for validation. State recreation advisories were defined as a period delimited by a start and end date where a warning was issued due to detections of cyanoHABs over a state's risk threshold. State reported events were defined as any event that was documented with a single date related to cyanoHABs. This study examined the presence-absence agreement between 160 state reported cyanoHAB advisories and 1,343 events and cyanobacteria biomass estimated by a satellite algorithm called the Cyanobacteria Index (CIcyano). The true positive rate of agreement with state recreation advisories was 69% and 60% with state reported events. CIcyano detected a reduction or absence in cyanobacteria after 76% of the recreation advisories ended. CIcyano was used to quantify the magnitude, spatial extent, and temporal frequency of cyanoHABs; each of these three metrics were greater (r > 0.2) during state recreation advisories compared to non-advisory times with effect sizes ranging from small to large. This is the first study to quantitatively evaluate satellite algorithm performance for detecting cyanoHABs with state reported events and advisories and supports informed management decisions with satellite technologies that complement traditional field observations.

Water Resources in Africa under Global Change: Monitoring Surface Waters from Space

Abstract

The African continent hosts some of the largest freshwater systems worldwide, characterized by a large distribution and variability of surface waters that play a key role in the water, energy and carbon cycles and are of major importance to the global climate and water resources. Freshwater availability in Africa has now become of major concern under the combined effect of climate change, environmental alterations and anthropogenic pressure. However, the hydrology of the African river basins remains one of the least studied worldwide and a better monitoring and understanding of the hydrological processes across the continent become fundamental. Earth Observation, that offers a cost-effective means for monitoring the terrestrial water cycle, plays a major role in supporting surface hydrology investigations. Remote sensing advances are therefore a game changer to develop comprehensive observing systems to monitor Africa's land water and manage its water resources. Here, we review the achievements of more than three decades of advances using remote sensing to study surface waters in Africa, highlighting the current benefits and difficulties. We show how the availability of a large number of sensors and observations, coupled with models, offers new possibilities to monitor a continent with scarce gauged stations. In the context of upcoming satellite missions dedicated to surface hydrology, such as the Surface Water and Ocean Topography (SWOT), we discuss future opportunities and how the use of remote sensing could benefit scientific and societal applications, such as water resource management, flood risk prevention and environment monitoring under current global change.

Article Highlights

The hydrology of African surface water is of global importance, yet it remains poorly monitored and understoodComprehensive review of remote sensing and modeling advances to monitor Africa's surface water and water resourcesFuture opportunities with upcoming satellite missions and to translate scientific advances into societal applications.

Development of an App and Teaching Concept for Implementation of Hyperspectral Remote Sensing Data into School Lessons Using Augmented Reality

For the purpose of expanding STEM (science, technology, engineering, mathematics) education with remote sensing (RS) data and methods, an augmented reality (AR) app was developed in combination with a worksheet and lesson plan. Data from the Hyperspectral Imager for the Coastal Ocean (HICO) was searched for topics applicable to STEM curricula, which was found in the example of a harmful algal bloom in Lake Erie, USA, in 2011. Spectral shape algorithms were applied to differentiate between less harmful green and more harmful blue algae in the lake. The data was pre-processed to reduce its size significantly without losing too much information and then integrated into an app that was developed in Unity with the Vuforia extension. It was designed to let students browse and understand the raw data in RGB and a tangible hyperspectral cube, as well as to analyze algae maps derived from it. The app runs on Android smartphones with minimized data usage to make it less dependent on school funding and the socioeconomic background of students. Using educational concepts, such as active and collaborative learning, moderate constructivism, and scientific inquiry, the data was integrated into a lesson about environmental problems that was enhanced by the AR app. The app and worksheet were evaluated in two advanced geography courses (n = 36) and found to be complex, but doable and understandable, for the target group of German high school students in their final two school years. Thus, hyperspectral data can be used for STEM lessons using AR technology on students’ smartphones with several limitations both in the technology used and gainable knowledge.

Cyanobacterial pigment concentrations in inland waters: Novel semi-analytical algorithms for multi- and hyperspectral remote sensing data

Cyanobacteria are notorious for producing harmful algal blooms that present an ever-increasing serious threat to aquatic ecosystems worldwide, impacting the quality of drinking water and disrupting the recreational use of many water bodies. Remote sensing techniques for the detection and quantification of cyanobacterial blooms are required to monitor their initiation and spatiotemporal variability. In this study, we developed a novel semi-analytical approach to estimate the concentration of cyanobacteria-specific pigment phycocyanin (PC) and common phytoplankton pigment chlorophyll a (Chl a) from hyperspectral remote sensing data. The PC algorithm was derived from absorbance-concentration relationship, and the Chl a algorithm was devised based on a conceptual three-band structure model. The developed algorithms were applied to satellite imageries obtained by the Hyperspectral Imager for the Coastal Ocean (HICO™) sensor and tested in Lake Kinneret (Israel) during strong cyanobacterium Microcystis sp. bloom and out-of-bloom times. The sensitivity of the algorithms to errors was evaluated. The Chl a and PC concentrations were estimated with a mean absolute percentage difference (MAPD) of 16% and 28%, respectively. Sensitivity analysis shows that the influences of backscattering and other water constituents do not affect the estimation accuracy of PC (~2% MAPD). The reliable PC/Chl a ratios can be obtained at PC concentrations above 10 mg m^-3. The computed PC/Chl a ratio depicts the contribution of cyanobacteria to the total phytoplankton biomass and permits investigating the role of ambient factors in the formation of a complex planktonic community. The novel algorithms have extensive practical applicability and should be suitable for the quantification of PC and Chl a in aquatic ecosystems using hyperspectral remote sensing data as well as data from future multispectral remote sensing satellites, if the respective bands are featured in the sensor.

Quantifying Karenia brevis bloom severity and respiratory irritation impact along the shoreline of Southwest Florida

Nearly all annual blooms of the toxic dinoflagellate Karenia brevis (K. brevis) pose a serious threat to coastal Southwest Florida. These blooms discolor water, kill fish and marine mammals, contaminate shellfish, cause mild to severe respiratory irritation, and discourage tourism and recreational activities, leading to significant health and economic impacts in affected communities. Despite these issues, we still lack standard measures suitable for assessing bloom severity or for evaluating the efficacy of modeling efforts simulating bloom initiation and intensity. In this study, historical cell count observations along the southwest Florida shoreline from 1953 to 2019 were used to develop monthly and annual bloom severity indices (BSI). Similarly, respiratory irritation observations routinely reported in Sarasota and Manatee Counties from 2006 to 2019 were used to construct a respiratory irritation index (RI). Both BSI and RI consider spatial extent and temporal evolution of the bloom, and can be updated routinely and used as objective criteria to aid future socioeconomic and scientific studies of K. brevis. These indices can also be used to help managers and decision makers both evaluate the risks along the coast during events and design systems to better respond to and mitigate bloom impacts. Before 1995, sampling was done largely in response to reports of discolored water, fish kills, or respiratory irritation. During this timeframe, lack of sampling during the fall, when blooms typically occur, generally coincided with periods of more frequent-than-usual offshore winds. Consequently, some blooms may have been undetected or under-sampled. As a result, the BSIs before 1995 were likely underestimated and cannot be viewed as accurately as those after 1995. Anomalies in the frequency of onshore wind can also largely account for the discrepancies between BSI and RI during the period from 2006 to 2019. These findings highlighted the importance of onshore wind anomalies when predicting respiratory irritation impacts along beaches.

Continuous and Synoptic Assessment of Indian Inland Waters for Harmful Algae Blooms

Cyanobacterial Harmful Algal Blooms (CyanoHABs) are progressively becoming a major water quality, socioeconomic, and health hazard worldwide. In India, there are frequent episodes of severe CyanoHABs, which are left untreated due to a lack of awareness and monitoring infrastructure, affecting the economy of the country gravely. In this study, for the first time, we present a country-wide analysis of CyanoHABs in India by developing a novel interactive cloud-based dashboard called "CyanoKhoj" in Google Earth Engine (GEE) which uses Sentinel-3 Ocean and Land Colour Instrument (OLCI) remotely sensed datasets. The main goal of this study was to showcase the utility of CyanoKhoj for rapid monitoring and discuss the widespread CyanoHABs problems across India. We demonstrate the utility of Cyanokhoj by including select case studies of lakes and reservoirs geographically spread across five states: Bargi and Gandhisagar Dams in Madhya Pradesh, Hirakud Reservoir in Odisha, Ukai Dam in Gujarat, Linganamakki Reservoir in Karnataka, and Pulicat Lake in Tamil Nadu. These sites were studied from September to November 2018 using CyanoKhoj, which is capable of near-real-time monitoring and country-wide assessment of CyanoHABs. We used CyanoKhoj to prepare spatiotemporal maps of Chlorophyll-a (Chl-a) content and Cyanobacterial Cell Density (CCD) to study the local spread of the CyanoHABs and their phenology in these waterbodies. A first-ever all-India CCD map is also presented for the year 2018, which highlights the spatial spread of CyanoHABs throughout the country (32 large waterbodies across India with severe bloom: CCD>2,500,000). Results indicate that CyanoHABs are most prevalent in nutrient-rich waterbodies prone to industrial and other nutrient-rich discharges. A clear temporal evolution of the blooms showed that they are dominant during the post-monsoon season (September-October) when the nutrient concentrations in the waterbodies are at their peak, and they begin to decline towards winter (November-December). CyanoKhoj is an open-source tool that can have a significant broader impact in mapping CyanoHABs not only throughout cyanobacteria data-scarce India, but on a global level using archived and future Sentinel-3A/B OLCI data.

A Meta-Analysis on Harmful Algal Bloom (HAB) Detection and Monitoring: A Remote Sensing Perspective

Algae serves as a food source for a wide range of aquatic species; however, a high concentration of inorganic nutrients under favorable conditions can result in the development of harmful algal blooms (HABs). Many studies have addressed HAB detection and monitoring; however, no global scale meta-analysis has specifically explored remote sensing-based HAB monitoring. Therefore, this manuscript elucidates and visualizes spatiotemporal trends in HAB detection and monitoring using remote sensing methods and discusses future insights through a meta-analysis of 420 journal articles. The results indicate an increase in the quantity of published articles which have facilitated the analysis of sensors, software, and HAB proxy estimation methods. The comparison across multiple studies highlighted the need for a standardized reporting method for HAB proxy estimation. Research gaps include: (1) atmospheric correction methods, particularly for turbid waters, (2) the use of analytical-based models, (3) the application of machine learning algorithms, (4) the generation of harmonized virtual constellation and data fusion for increased spatial and temporal resolutions, and (5) the use of cloud-computing platforms for large scale HAB detection and monitoring. The planned hyperspectral satellites will aid in filling these gaps to some extent. Overall, this review provides a snapshot of spatiotemporal trends in HAB monitoring to assist in decision making for future studies.

A new approach to monitor water quality in the Menor sea (Spain) using satellite data and machine learning methods

The Menor sea is a coastal lagoon declared by the European Union as a sensitive area to eutrophication due to human activities. To control the deterioration of its water quality, it is necessary to monitor some parameters such as chlorophyll-a (chl-a), which indicates phytoplankton biomass in the water. In the study area, current efforts focus on in-situ measurements to estimate chl-a by means of a few permanent stations and seasonal oceanographic campaigns, however they are expensive and time consuming. In this work, we proposed a machine learning approach based on Sentinel-2 data to estimate chl-a content on the upper part of the water column. Random forest (rf), support vector machine (svmRadial), Artificial Neural Network (ANN) and Deep Neural Network (DNN) algorithms were utilized under three feature selection scenarios, and several spectral indices were used in combination with Sentinel 2 bands. Rf, svmRadial and DNN performed better when all the available predictors were included in the models (RMSE = 0.82, 0.82 and 1.76 mg/m³ respectively), whereas ANN achieved better results under scenario c (principal components). Our results demonstrate the possibility to estimate chl-a concentration in a cost-effective manner and thereby provide near-real time information to monitor the water quality of the Menor sea, what can be of great interest for local authorities, tourism and fishing industry.

Satellite remote sensing to assess cyanobacterial bloom frequency across the United States at multiple spatial scales

Cyanobacterial blooms can have negative effects on human health and local ecosystems. Field monitoring of cyanobacterial blooms can be costly, but satellite remote sensing has shown utility for more efficient spatial and temporal monitoring across the United States. Here, satellite imagery was used to assess the annual frequency of surface cyanobacterial blooms, defined for each satellite pixel as the percentage of images for that pixel throughout the year exhibiting detectable cyanobacteria. Cyanobacterial frequency was assessed across 2,196 large lakes in 46 states across the continental United States (CONUS) using imagery from the European Space Agency's Ocean and Land Colour Instrument for the years 2017 through 2019. In 2019, across all satellite pixels considered, annual bloom frequency had a median value of 4% and a maximum value of 100%, the latter indicating that for those satellite pixels, a cyanobacterial bloom was detected by the satellite sensor for every satellite image considered. In addition to annual pixel-scale cyanobacterial frequency, results were summarized at the lake- and state-scales by averaging annual pixel-scale results across each lake and state. For 2019, average annual lake-scale frequencies also had a maximum value of 100%, and Oregon and Ohio had the highest average annual state-scale frequencies at 65% and 52%. Pixel-scale frequency results can assist in identifying portions of a lake that are more prone to cyanobacterial blooms, while lake- and state-scale frequency results can assist in the prioritization of sampling resources and mitigation efforts. Satellite imagery is limited by the presence of snow and ice, as imagery collected in these conditions are quality flagged and discarded. Thus, annual bloom frequencies within nine climate regions were investigated to determine whether missing data biased results in climate regions more prone to snow and ice, given that their annual summaries would be weighted toward the summer months when cyanobacterial blooms tend to occur. Results were unbiased by the time period selected in most climate regions, but a large bias was observed for the Northwest Rockies and Plains climate region. Moderate biases were observed for the Ohio Valley and the Southeast climate regions. Finally, a clustering analysis was used to identify areas of high and low cyanobacterial frequency across CONUS based on average annual lake-scale cyanobacterial frequencies for 2019. Several clusters were identified that transcended state, watershed, and eco-regional boundaries. Combined with additional data, results from the clustering analysis may offer insight regarding large-scale drivers of cyanobacterial blooms.

The Lake Erie HABs Grab: A binational collaboration to characterize the western basin cyanobacterial harmful algal blooms at an unprecedented high-resolution spatial scale

Monitoring of cyanobacterial bloom biomass in large lakes at high resolution is made possible by remote sensing. However, monitoring cyanobacterial toxins is only feasible with grab samples, which, with only sporadic sampling, results in uncertainties in the spatial distribution of toxins. To address this issue, we conducted two intensive "HABs Grabs" of microcystin (MC)-producing Microcystis blooms in the western basin of Lake Erie. These were one-day sampling events during August of 2018 and 2019 in which 100 and 172 grab samples were collected, respectively, within a six-hour window covering up to 2,270 km² and analyzed using consistent methods to estimate the total mass of MC. The samples were analyzed for 57 parameters, including toxins, nutrients, chlorophyll, and genomics. There were an estimated 11,513 kg and 30,691 kg of MCs in the western basin during the 2018 and 2019 HABs Grabs, respectively. The bloom boundary poses substantial issues for spatial assessments because MC concentration varied by nearly two orders of magnitude over very short distances. The MC to chlorophyll ratio (MC:chl) varied by a factor up to 5.3 throughout the basin, which creates challenges for using MC:chl to predict MC concentrations. Many of the biomass metrics strongly correlated (r > 0.70) with each other except chlorophyll fluorescence and phycocyanin concentration. While MC and chlorophyll correlated well with total phosphorus and nitrogen concentrations, MC:chl correlated with dissolved inorganic nitrogen. More frequent MC data collection can overcome these issues, and models need to account for the MC:chl spatial heterogeneity when forecasting MCs.

Assessing cyanobacterial frequency and abundance at surface waters near drinking water intakes across the United States

This study presents the first large-scale assessment of cyanobacterial frequency and abundance of surface water near drinking water intakes across the United States. Public water systems serve drinking water to nearly 90% of the United States population. Cyanobacteria and their toxins may degrade the quality of finished drinking water and can lead to negative health consequences. Satellite imagery can serve as a cost-effective and consistent monitoring technique for surface cyanobacterial blooms in source waters and can provide drinking water treatment operators information for managing their systems. This study uses satellite imagery from the European Space Agency's Ocean and Land Colour Instrument (OLCI) spanning June 2016 through April 2020. At 300-m spatial resolution, OLCI imagery can be used to monitor cyanobacteria in 685 drinking water sources across 285 lakes in 44 states, referred to here as resolvable drinking water sources. First, a subset of satellite data was compared to a subset of responses (n = 84) submitted as part of the U.S. Environmental Protection Agency's fourth Unregulated Contaminant Monitoring Rule (UCMR 4). These UCMR 4 qualitative responses included visual observations of algal bloom presence and absence near drinking water intakes from March 2018 through November 2019. Overall agreement between satellite imagery and UCMR 4 qualitative responses was 94% with a Kappa coefficient of 0.70. Next, temporal frequency of cyanobacterial blooms at all resolvable drinking water sources was assessed. In 2019, bloom frequency averaged 2% and peaked at 100%, where 100% indicated a bloom was always present at the source waters when satellite imagery was available. Monthly cyanobacterial abundances were used to assess short-term trends across all resolvable drinking water sources and effect size was computed to provide insight on the number of years of data that must be obtained to increase confidence in an observed change. Generally, 2016 through 2020 was an insufficient time period for confidently observing changes at these source waters; on average, a decade of satellite imagery would be required for observed environmental trends to outweigh variability in the data. However, five source waters did demonstrate a sustained short-term trend, with one increasing in cyanobacterial abundance from June 2016 to April 2020 and four decreasing.

Acute health effects associated with satellite-determined cyanobacterial blooms in a drinking water source in Massachusetts

BACKGROUND

The occurrence of cyanobacterial blooms in freshwater presents a threat to human health. However, epidemiological studies on the association between cyanobacterial blooms in drinking water sources and human health outcomes are scarce. The objective of this study was to evaluate if cyanobacterial blooms were associated with increased emergency room visits for gastrointestinal (GI), respiratory and dermal illnesses.

METHODS

Satellite-derived cyanobacteria cell concentrations were estimated in the source of drinking water for the Greater Boston area, during 2008-2011. Daily counts of hospital emergency room visits for GI, respiratory and dermal illnesses among drinking water recipients were obtained from an administrative record database. A two-stage model was used to analyze time-series data for an association between cyanobacterial blooms and the occurrence of illnesses. At the first stage, predictive autoregressive generalized additive models for Poisson-distributed outcomes were fitted to daily illness count data and daily predictive variables. At the second stage, residuals from the first stage models were regressed against lagged categorized cyanobacteria concentration estimates.

RESULTS

The highest cyanobacteria concentration (above the 75th percentile) was associated with an additional 4.3 cases of respiratory illness (95% confidence interval: 0.7, 8.0, p = 0.02, n = 268) compared to cyanobacteria concentrations below the 50th percentile in a two-day lag. There were no significant associations between satellite derived cyanobacterial concentrations and lagged data on GI or dermal illnesses.

CONCLUSION

The study demonstrated a significant positive association between satellite-derived cyanobacteria concentrations in source water and respiratory illness occurring 2 days later. Future studies will require direct measures of cyanotoxins and health effects associated with exposure to cyanobacteria-impacted drinking water sources.

Evaluation of a satellite-based cyanobacteria bloom detection algorithm using field-measured microcystin data

Widespread occurrence of cyanobacterial harmful algal blooms (CyanoHABs) and the associated health effects from potential cyanotoxin exposure has led to a need for systematic and frequent screening and monitoring of lakes that are used as recreational and drinking water sources. Remote sensing-based methods are often used for synoptic and frequent monitoring of CyanoHABs. In this study, one such algorithm - a sub-component of the Cyanobacteria Index called the CIcyano, was validated for effectiveness in identifying lakes with toxin-producing blooms in 11 states across the contiguous United States over 11 bloom seasons (2005-2011, 2016-2019). A matchup data set was created using satellite data from MEdium Resolution Imaging Spectrometer (MERIS) and Ocean Land Colour Imager (OLCI), and nearshore, field-measured Microcystins (MCs) data as a proxy of CyanoHAB presence. While the satellite sensors cannot detect toxins, MCs are used as the indicator of health risk, and as a confirmation of cyanoHAB presence. MCs are also the most common laboratory measurement made by managers during CyanoHABs. Algorithm performance was evaluated by its ability to detect CyanoHAB 'Presence' or 'Absence', where the bloom is confirmed by the presence of the MCs. With same-day matchups, the overall accuracy of CyanoHAB detection was found to be 84% with precision and recall of 87 and 90% for bloom detection. Overall accuracy was expected to be between 77% and 87% (95% confidence) based on a bootstrapping simulation. These findings demonstrate that CIcyano has utility for synoptic and routine monitoring of potentially toxic cyanoHABs in lakes across the United States.

Risk prediction of microcystins based on water quality surrogates: A case study in a eutrophicated urban river network

Microcystins (MCs), the toxic by-products from harmful algal bloom (HAB), have caused world-wide concern due to their acute toxicity in freshwater ecosystems. Most studies on HAB have been conducted for shallow freshwater lakes, such as Taihu Lake in China. However, algal blooms in urban rivers located downstream of eutrophicated lakes are also a serious problem for local administrators. It is important for them to know the current and potential risk level of MCs. This environmental issue is rarely reported or discussed. Within this context, we monitored MC concentrations in the Binhu River Network (BRN) in the algal bloom season (Aug, Sep, and Oct) in 2019. To note if the MC concentrations were dangerous, we used 1.0 μg/L suggested by the World Health Organization as the standard value. The proportions of MC samples violating the standard value were 31.78% (Aug), 21.14% (Sep) and 30.77% (Oct). We also designed two statistical models to predict MC concentrations and the possibility to exceed the standard level based on 10 water quality surrogates: Artificial Neural Network (ANN) and Logistic Regression (LR) models. These two models were trained and validated by the monitoring dataset (n = 224). Both models had good performances during training and testing. Although the water quality varied diversely both in spatial and temporal scale, Cluster Analysis (CA) could detect similarities among the samples and separated them into 3 classes, with each class denoting different types of rivers based on the 10 water quality surrogates. Then the ANN and LR were applied as a function of chl-a in each class; by gradually increasing chl-a concentration, we detected chl-a thresholds in class 1, 2, 3 were 25.5, 224, and 109.5 μg/L, respectively, when MCs have a 50% possibility to exceed standard level. The threshold values provided important implications for MC management in the BRN.

Water Quality Retrieval from PRISMA Hyperspectral Images: First Experience in a Turbid Lake and Comparison with Sentinel-2

A new era of spaceborne hyperspectral imaging has just begun with the recent availability of data from PRISMA (PRecursore IperSpettrale della Missione Applicativa) launched by the Italian space agency (ASI). There has been pre-launch optimism that the wealth of spectral information offered by PRISMA can contribute to a variety of aquatic science and management applications. Here, we examine the potential of PRISMA level 2D images in retrieving standard water quality parameters, including total suspended matter (TSM), chlorophyll-a (Chl-a), and colored dissolved organic matter (CDOM) in a turbid lake (Lake Trasimeno, Italy). We perform consistency analyses among the aquatic products (remote sensing reflectance (Rrs) and constituents) derived from PRISMA and those from Sentinel-2. The consistency analyses are expanded to synthesized Sentinel-2 data as well. By spectral downsampling of the PRISMA images, we better isolate the impact of spectral resolution in retrieving the constituents. The retrieval of constituents from both PRISMA and Sentinel-2 images is built upon inverting the radiative transfer model implemented in the Water Color Simulator (WASI) processor. The inversion involves a parameter (gdd) to compensate for atmospheric and sun-glint artifacts. A strong agreement is indicated for the cross-sensor comparison of Rrs products at different wavelengths (average R ≈ 0.87). However, the Rrs of PRISMA at shorter wavelengths (<500 nm) is slightly overestimated with respect to Sentinel-2. This is in line with the estimates of gdd through the inversion that suggests an underestimated atmospheric path radiance of PRISMA level 2D products compared to the atmospherically corrected Sentinel-2 data. The results indicate the high potential of PRISMA level 2D imagery in mapping water quality parameters in Lake Trasimeno. The PRISMA-based retrievals agree well with those of Sentinel-2, particularly for TSM.

Exploring the potential value of satellite remote sensing to monitor chlorophyll-a for US lakes and reservoirs

Assessment of chlorophyll-a, an algal pigment, typically measured by field and laboratory in situ analyses, is used to estimate algal abundance and trophic status in lakes and reservoirs. In situ-based monitoring programs can be expensive, may not be spatially, and temporally comprehensive and results may not be available in the timeframe needed to make some management decisions, but can be more accurate, precise, and specific than remotely sensed measures. Satellite remotely sensed chlorophyll-a offers the potential for more geographically and temporally dense data collection to support estimates when used to augment or substitute for in situ measures. In this study, we compare available chlorophyll-a data from in situ and satellite imagery measures at the national scale and perform a cost analysis of these different monitoring approaches. The annual potential avoided costs associated with increasing the availability of remotely sensed chlorophyll-a values were estimated to range between $5.7 and $316 million depending upon the satellite program used and the timeframe considered. We also compared sociodemographic characteristics of the regions (both public and private lands) covered by both remote sensing and in situ data to check for any systematic differences across areas that have monitoring data. This analysis underscores the importance of continued support for both field-based in situ monitoring and satellite sensor programs that provide complementary information to water quality managers, given increased challenges associated with eutrophication, nuisance, and harmful algal bloom events.

Environment Changes, Aflatoxins, and Health Issues, a Review

Crops contaminated by aflatoxins (AFs), the toxic and carcinogenic mycotoxins produced namely by Aspergillus flavus and Aspergillus parasiticus, have severe impacts on human health. Changes in temperature and water availability related to actual climate changes (increased temperature, heavy rainfalls, and droughts) are modulating factors of mould growth and production of mycotoxins. To protect human and animal health from the harmful effects caused by AFs, the development of a safe and effective multifaceted approach in combating food and feed contamination with AFs is necessary. This review aims to collect and analyze the available information regarding AF presence in food and feed to reinforce AF management and to prevent health issues related to the AF exposure in the light of actual climate changes.

Recent Advancements in the Removal of Cyanotoxins from Water Using Conventional and Modified Adsorbents—A Contemporary Review

The prevalence of cyanobacteria is increasing in freshwaters due to climate change, eutrophication, and their ability to adapt and thrive in changing environmental conditions. In response to various environmental pressures, they produce toxins known as cyanotoxins, which impair water quality significantly. Prolonged human exposure to cyanotoxins, such as microcystins, cylindrospermopsin, saxitoxins, and anatoxin through drinking water can cause severe health effects. Conventional water treatment processes are not effective in removing these cyanotoxins in water and advanced water treatment processes are often used instead. Among the advanced water treatment methods, adsorption is advantageous compared to other methods because of its affordability and design simplicity for cyanotoxins removal. This article provides a current review of recent developments in cyanotoxin removal using both conventional and modified adsorbents. Given the different cyanotoxins removal capacities and cost of conventional and modified adsorbents, a future outlook, as well as suggestions are provided to achieve optimal cyanotoxin removal through adsorption.

Quantifying the Human Health Benefits of Using Satellite Information to Detect Cyanobacterial Harmful Algal Blooms and Manage Recreational Advisories in U.S. Lakes

Significant recent advances in satellite remote sensing allow environmental managers to detect and monitor cyanobacterial harmful algal blooms (cyanoHAB), and these capabilities are being used more frequently in water quality management. A quantitative estimate of the socioeconomic benefits generated from these new capabilities, known as an impact assessment, was missing from the growing literature on cyanoHABs and remote sensing. In this paper, we present an impact assessment framework to characterize the socioeconomic benefits of satellite remote sensing for detecting cyanoHABs and managing recreational advisories at freshwater lakes. We then apply this framework to estimate the socioeconomic benefits of satellite data that were used to manage a 2017 cyanoHAB event in Utah Lake. CyanoHAB events on Utah Lake can pose health risks to people who interact with the blooms through recreation. We find that the availability of satellite data yielded socioeconomic benefits by improving human health outcomes valued at approximately $370,000, though a sensitivity analysis reveals that this central estimate can vary significantly ($55,000-$1,057,000 in benefits) as a result of different assumptions regarding the time delay in posting a recreational advisory, the number of people exposed to the cyanoHAB, the number of people who experience gastrointestinal symptoms, and the cost per case of illness.

Assessing the origin of a massive cyanobacterial bloom in the Río de la Plata (2019): Towards an early warning system

The Río de la Plata estuary drains the second largest river basin of South America. The occurrence of frequent cyanobacterial blooms of the Microcystis and Dolichospermum complex in the Uruguayan coast are associated with high flows of Uruguay River due to rainy years. In summer 2019, a massive cyanobacterial bloom reached up to the Uruguayan Atlantic coast. This study seeks to unveil the origin and the environmental conditions that favored the occurrence of the last cyanobacterial bloom in the Río de la Plata, and to contribute with the development of an early warning system of cyanobacterial scum on Montevideo beaches. A complementary approach was applied with Sentinel-2 imagery, environmental data of monitoring programs of Salto Grande Reservoir and Montevideo beaches, hydro-meteorological information, and hydroelectric dam operation. Images were analyzed with the Normalized Difference Chlorophyll Index (NDCI), which allowed evaluating several water bodies within the same ranges. Positive anomalous rainfall increased river flows, particularly that of Uruguay and Negro rivers, which caused the opening of the dam spillways. NDCI maps showed that areas with high values (NDCI_>0.06) in Salto Grande reservoir kept a similar surface area before and after the prolonged overflow period (8.7-7.8 km², before and after). In the Río Negro reservoirs, however, NDCI_>0.06 coverage remarkably changed (62.5 km², Palmar reservoir), with a subsequent 56-fold reduction in the post-discharge of surface water. Twenty days after opening the spillways, Montevideo beaches were closed to swimming and the NDCI_>0.06 surface reached 51.7 km² in the Río de la Plata coast. The dynamics of NDCI areas, the downstream bloom discharge, and the predicted Río de la Plata residual currents, suggest that the cyanobacterial bloom originated in the Negro River (Palmar reservoir). This bloom event was one of the worst that occurred in the Río de la Plata in last 20 years, circulated along the Uruguayan sub-corridor to the Atlantic coast along 690 km from its origin, and lasted three months on Montevideo coast. This is the first study that addresses the impact of cyanobacterial blooms from the Negro River reservoirs on the Río de la Plata estuary. Therefore, the Negro River basin is where the main efforts should be directed to mitigate massive cyanobacterial blooms.

Predicting microcystin concentration action-level exceedances resulting from cyanobacterial blooms in selected lake sites in Ohio

Cyanobacterial harmful algal blooms and the toxins they produce are a global water-quality problem. Monitoring and prediction tools are needed to quickly predict cyanotoxin action-level exceedances in recreational and drinking waters used by the public. To address this need, data were collected at eight locations in Ohio, USA, to identify factors significantly related to observed concentrations of microcystins (a freshwater cyanotoxin) that could be used in two types of site-specific regression models. Real-time models include easily or continuously-measured factors that do not require that a sample be collected; comprehensive models use a combination of discrete sample-based measurements and real-time factors. The study sites included two recreational sites and six water treatment plant sites. Real-time models commonly included variables such as phycocyanin, pH, specific conductance, and streamflow or gage height. Many real-time factors were averages over time periods antecedent to the time the microcystin sample was collected, including water-quality data compiled from continuous monitors. Comprehensive models were useful at some sites with lagged variables for cyanobacterial toxin genes, dissolved nutrients, and (or) nitrogen to phosphorus ratios. Because models can be used for management decisions, important measures of model performance were sensitivity, specificity, and accuracy of estimates above or below the microcystin concentration threshold standard or action level. Sensitivity is how well the predictive tool correctly predicts exceedance of a threshold, an important measure for water-resource managers. Sensitivities > 90% at four Lake Erie water treatment plants indicated that models with continuous monitor data were especially promising. The planned next steps are to collect more data to build larger site-specific datasets and validate models before they can be used for management decisions.

CyanoTRACKER: A cloud-based integrated multi-platform architecture for global observation of cyanobacterial harmful algal blooms

Over the past decade, the global proliferation of cyanobacterial harmful algal blooms (CyanoHABs) have presented a major risk to the public and wildlife, and ecosystem and economic services provided by inland water resources. As a consequence, water resources, environmental, and healthcare agencies are in need of early information about the development of these blooms to mitigate or minimize their impact. Results from various components of a novel multi-cloud cyber-infrastructure referred to as "CyanoTRACKER" for initial detection and continuous monitoring of spatio-temporal growth of CyanoHABs is highlighted in this study. The novelty of the CyanoTRACKER framework is the collection and integration of combined community reports (social cloud), remote sensing data (sensor cloud) and digital image analytics (computation cloud) to detect and differentiate between regular algal blooms and CyanoHABs. Individual components of CyanoTRACKER include a reporting website, mobile application (App), remotely deployable solar powered automated hyperspectral sensor (CyanoSense), and a cloud-based satellite data processing and integration tool. All components of CyanoTRACKER provided important data related to CyanoHABs assessments for regional and global water bodies. Reports and data received via social cloud including the mobile App, Twitter, Facebook, and CyanoTRACKER website, helped in identifying the geographic locations of CyanoHABs affected water bodies. A significant increase (124.92%) in tweet numbers related to CyanoHABs was observed between 2011 (total relevant tweets = 2925) and 2015 (total relevant tweets = 6579) that reflected an increasing trend of the harmful phenomena across the globe as well as an increased awareness about CyanoHABs among Twitter users. The CyanoHABs affected water bodies extracted via the social cloud were categorized, and smaller water bodies were selected for the deployment of CyanoSense, and satellite data analysis was performed for larger water bodies. CyanoSense was able to differentiate between ordinary algae and CyanoHABs through the use of their characteristic absorption feature at 620 nm. The results and products from this infrastructure can be rapidly disseminated via the CyanoTRACKER website, social media, and direct communication with appropriate management agencies for issuing warnings and alerting lake managers, stakeholders and ordinary citizens to the dangers posed by these environmentally harmful phenomena.

Quantifying national and regional cyanobacterial occurrence in US lakes using satellite remote sensing

Cyanobacterial harmful algal blooms are the most common form of harmful algal blooms in freshwater systems throughout the world. However, in situ sampling of cyanobacteria in inland lakes is limited both spatially and temporally. Satellite data has proven to be an effective tool to monitor cyanobacteria in freshwater lakes across the United States. This study uses data from the European Space Agency Envisat MEdium Resolution Imaging Spectrometer and the Sentinel-3 Ocean and Land Color Instrument to provide a national overview of the percentage of lakes experiencing a cyanobacterial bloom on a weekly basis for 2008-2011, 2017, and 2018. A total of 2321 lakes across the contiguous United States were included in the analysis. We examined four different thresholds to define when a waterbody is classified as experiencing a bloom. Across these four thresholds, we explored variability in bloom percentage with changes in seasonality and lake size. As a validation of algorithm performance, we analyzed the agreement between satellite observations and previously established ecological patterns, although data availability in the wintertime limited these comparisons on a year-round basis. Changes in cyanobacterial bloom percentage at the national scale followed the well-known temporal pattern of freshwater blooms. The percentage of lakes experiencing a bloom increased throughout the year, reached a maximum in fall, and decreased through the winter. Wintertime data, particularly in northern regions, were consistently limited due to snow and ice cover. With the exception of the Southeast and South, regional patterns mimicked patterns found at the national scale. The Southeast and South exhibited an unexpected pattern as cyanobacterial bloom percentage reached a maximum in the winter rather than the summer. Lake Jesup in Florida was used as a case study to validate this observed pattern against field observations of chlorophyll a. Results from this research establish a baseline of annual occurrence of cyanobacterial blooms in inland lakes across the United States. In addition, methods presented in this study can be tailored to fit the specific requirements of an individual system or region.

Determination of Phycocyanin from Space—A Bibliometric Analysis

Over the past few decades, there has been an increase in the number of studies about the estimation of phycocyanin derived from remote sensing techniques. Since phycocyanin is a unique pigment of inland water cyanobacteria, the quantification of its concentration from earth observation data is important for water quality monitoring - once some species can produce toxins. Because of the growth of this field in the past decade, several reviews and studies comparing algorithms have been published. Thus, instead of focusing on algorithms comparison or description, the goal of the present study is to systematically analyze and visualize the evolution of publications. Using the Web of Science database this study analyzed the existing publications on remote sensing of phycocyanin decade-by-decade for the period 1991–2020. The bibliometric analysis showed how research topics evolved from measuring pigments to the quantification of optical properties and from laboratory experiments to measuring entire temperate and tropical aquatic systems. This study provides the status quo and development trend of the field and points out what could be the direction for future research.

Implementation of a Satellite Based Inland Water Algal Bloom Alerting System Using Analysis Ready Data

Water managers need tools to assist in the management of ever increasing algal bloom problems over wide spatial areas to complement sparse and declining in situ monitoring networks. Optical methods employing satellite data offer rapid and widespread coverage for early detection of bloom events. The advent of the Analysis Ready Data (ARD) and Open Data Cube concepts offer the means to lower the technical challenges confronting managers, allowing them to adopt satellite tools. Exploiting Landsat ARD integrated into the Digital Earth Australia data cube, we developed a prototype algal bloom alerting tool for the state of New South Wales, Australia. A visualization portal allows managers to gain insights into bloom status across the state as a whole and to further investigate spatial patterns in bloom alerts at an individual water body basis. To complement this we also proposed an algal alert system for trial based on chlorophyll and TSM levels which requires further testing. The system was able to retrieve the status of 444 water bodies across the state and outputs from the visualization system are presented. Time series of image acquisitions during an intense bloom in one lake are used to demonstrate the potential of the system. We discuss the implications for further development and operationalisation including the potential for augmentation with alternative algorithms and incorporation of other sensor ARD data to improve both temporal and spectral resolutions.

A space-time geostatistical model for probabilistic estimation of harmful algal bloom biomass and areal extent

Harmful algal blooms (HABs) have been increasing in intensity worldwide, including the western basin of Lake Erie. Substantial efforts have been made to track these blooms using in situ sampling and remote sensing. However, such measurements do not fully capture HAB spatial and temporal dynamics due to the limitations of discrete shipboard sampling over large areas and the effects of clouds and winds on remote sensing estimates. To address these limitations, we develop a space-time geostatistical modeling framework for estimating HAB intensity and extent using chlorophyll a data sampled during the HAB season (June-October) from 2008 to 2017 by five independent monitoring programs. Based on the Bayesian information criterion for model selection, trend variables explain bloom northerly and easterly expansion from Maumee Bay, wind effects over depth, and variability among sampling methods. Cross validation results demonstrate that space-time kriging explains over half of the variability in daily, location-specific chlorophyll observations, on average. Conditional simulations provide, for the first time, comprehensive estimates of overall bloom biomass (based on depth-integrated concentrations) and surface areal extent with quantified uncertainties. These new estimates are contrasted with previous Lake Erie HAB monitoring studies, and deviations among estimates are explored and discussed. Overall, results highlight the importance of maintaining sufficient monitoring coverage to capture bloom dynamics, as well as the benefits of the proposed approach for synthesizing data from multiple monitoring programs to improve estimation accuracy while reducing uncertainty.

Measurement of Cyanobacterial Bloom Magnitude using Satellite Remote Sensing

Cyanobacterial harmful algal blooms (cyanoHABs) are a serious environmental, water quality and public health issue worldwide because of their ability to form dense biomass and produce toxins. Models and algorithms have been developed to detect and quantify cyanoHABs biomass using remotely sensed data but not for quantifying bloom magnitude, information that would guide water quality management decisions. We propose a method to quantify seasonal and annual cyanoHAB magnitude in lakes and reservoirs. The magnitude is the spatiotemporal mean of weekly or biweekly maximum cyanobacteria biomass for the season or year. CyanoHAB biomass is quantified using a standard reflectance spectral shape-based algorithm that uses data from Medium Resolution Imaging Spectrometer (MERIS). We demonstrate the method to quantify annual and seasonal cyanoHAB magnitude in Florida and Ohio (USA) respectively during 2003-2011 and rank the lakes based on median magnitude over the study period. The new method can be applied to Sentinel-3 Ocean Land Color Imager (OLCI) data for assessment of cyanoHABs and the change over time, even with issues such as variable data acquisition frequency or sensor calibration uncertainties between satellites. CyanoHAB magnitude can support monitoring and management decision-making for recreational and drinking water sources.

Remote sensing of cyanobacterial blooms in inland waters: present knowledge and future challenges

Timely monitoring, detection and quantification of cyanobacterial blooms are especially important for controlling public health risks and understanding aquatic ecosystem dynamics. Due to the advantages of simultaneous data acquisition over large geographical areas and high temporal coverage, remote sensing strongly facilitates cyanobacterial bloom monitoring in inland waters. We provide a comprehensive review regarding cyanobacterial bloom remote sensing in inland waters including cyanobacterial optical characteristics, operational remote sensing algorithms of chlorophyll, phycocyanin and cyanobacterial bloom areas, and satellite imaging applications. We conclude that there have many significant progresses in the remote sensing algorithm of cyanobacterial pigments over the past 30 years. The band ratio algorithms in the red and near-infrared (NIR) spectral regions have great potential for the remote estimation of chlorophyll a in eutrophic and hypereutrophic inland waters, and the floating algae index (FAI) is the most widely used spectral index for detecting dense cyanobacterial blooms. Landsat, MODIS (Moderate Resolution Imaging Spectroradiometer) and MERIS (MEdium Resolution Imaging Spectrometer) are the most widely used products for monitoring the spatial and temporal dynamics of cyanobacteria in inland waters due to the appropriate temporal, spatial and spectral resolutions. Future work should primarily focus on the development of universal algorithms, remote retrievals of cyanobacterial blooms in oligotrophic waters, and the algorithm applicability to mapping phycocyanin at a large spatial-temporal scale. The applications of satellite images will greatly improve our understanding of the driving mechanism of cyanobacterial blooms by combining numerical and ecosystem dynamics models.

Real Time HABs Mapping Using NASA Glenn Hyperspectral Imager

The hyperspectral imaging system (HSI) developed by the NASA Glenn Research Center was used from 2015-2017 to collect high spatial resolution data over Lake Erie and the Ohio River. Paired with a vicarious correction approach implemented by the Michigan Tech Research Institute, radiance data collected by the HSI system can be converted to high quality reflectance data which can be used to generate near-real time (within 24 hours) products for the monitoring of harmful algal blooms using existing algorithms. The vicarious correction method relies on imaging a spectrally constant target to normalize HSI data for atmospheric and instrument calibration signals. A large asphalt parking lot near the Western Basin of Lake Erie was spectrally characterized and was determined to be a suitable correction target. Due to the HSI deployment aboard an aircraft, it is able to provide unique insights into water quality conditions not offered by space-based solutions. Aircraft can operate under cloud cover and flight paths can be chosen and changed on-demand, allowing for far more flexibility than space-based platforms. The HSI is also able to collect data at a high spatial resolution (~1 m), allowing for the monitoring of small water bodies, the ability to detect small patches of surface scum, and the capability to monitor the proximity of blooms to targets of interest such as water intakes. With this new rapid turnaround time, airborne data can serve as a complementary monitoring tool to existing satellite platforms, targeting critical areas and responding to bloom events on-demand.

Comparison of satellite reflectance algorithms for estimating turbidity and cyanobacterial concentrations in productive freshwaters using hyperspectral aircraft imagery and dense coincident surface observations

We analyzed 37 satellite reflectance algorithms and 321 variants for five satellites for estimating turbidity in a freshwater inland lake in Ohio using coincident real hyperspectral aircraft imagery converted to relative reflectance and dense coincident surface observations. This study is part of an effort to develop simple proxies for turbidity and algal blooms and to evaluate their performance and portability between satellite imagers for regional operational turbidity and algal bloom monitoring. Turbidity algorithms were then applied to synthetic satellite images and compared to in situ measurements of turbidity, chlorophyll-a (Chl-a), total suspended solids (TSS) and phycocyanin as an indicator of cyanobacterial/blue green algal (BGA) abundance. Several turbidity algorithms worked well with real Compact Airborne Spectrographic Imager (CASI) and synthetic WorldView-2, Sentinel-2 and Sentinel-3/MERIS/OLCI imagery. A simple red band algorithm for MODIS imagery and a new fluorescence line height algorithm for Landsat-8 imagery had limited performance with regard to turbidity estimation. Blue-Green Algae/Phycocyanin (BGA/PC) and Chl-a algorithms were the most widely applicable algorithms for turbidity estimation because strong co-variance of turbidity, TSS, Chl-a, and BGA made them mutual proxies in this experiment.