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Previous studies indicate that cyanobacterial harmful algal bloom (cyanoHAB) frequency, extent, and magnitude have increased globally over the past few decades. However, little quantitative capability is available to assess these metrics of cyanoHABs across broad geographic scales and at regular intervals. Here, the spatial extent was quantified from a cyanobacteria algorithm applied to two European Space Agency satellite platforms-the MEdium Resolution Imaging Spectrometer (MERIS) onboard Envisat and the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3. CyanoHAB spatial extent was defined for each geographic area as the percentage of valid satellite pixels that exhibited cyanobacteria above the detection limit of the satellite sensor. This study quantified cyanoHAB spatial extent for over 2,000 large lakes and reservoirs across the contiguous United States (CONUS) during two time periods: 2008-2011 via MERIS and 2017-2020 via OLCI when cloud-, ice-, and snow-free imagery was available. Approximately 56% of resolvable lakes were glaciated, 13% were headwater, isolated, or terminal lakes, and the rest were primarily drainage lakes. Results were summarized at national-, regional-, state-, and lake-scales, where regions were defined as nine climate regions which represent climatically consistent states. As measured by satellite, changes in national cyanoHAB extent did have a strong increase of 6.9% from 2017 to 2020 (|Kendall's

Seagrasses are globally recognized for their contribution to blue carbon sequestration. However, accurate quantification of their carbon storage capacity remains uncertain due, in part, to an incomplete inventory of global seagrass extent and assessment of its temporal variability. Furthermore, seagrasses are undergoing significant decline globally, which highlights the urgent need to develop change detection techniques applicable to both the scale of loss and the spatial complexity of coastal environments. This study applied a deep learning algorithm to a 30-year time series of Landsat 5 through 8 imagery to quantify seagrass extent, leaf area index (LAI), and belowground organic carbon (BGC) in St. Joseph Bay, Florida, between 1990 and 2020. Consistent with previous field-based observations regarding stability of seagrass extent throughout St. Joseph Bay, there was no temporal trend in seagrass extent (23 ± 3 km2, τ = 0.09, p = 0.59, n = 31), LAI (1.6 ± 0.2, τ = -0.13, p = 0.42, n = 31), or BGC (165 ± 19 g C m−2, τ = - 0.01, p = 0.1, n = 31) over the 30-year study period. There were, however, six brief declines in seagrass extent between the years 2004 and 2019 following tropical cyclones, from which seagrasses recovered rapidly. Fine-scale interannual variability in seagrass extent, LAI, and BGC was unrelated to sea surface temperature or to climate variability associated with the El Niño-Southern Oscillation or the North Atlantic Oscillation. Although our temporal assessment showed that seagrass and its belowground carbon were stable in St. Joseph Bay from 1990 to 2020, forecasts suggest that environmental and climate pressures are ongoing, which highlights the importance of the method and time series presented here as a valuable tool to quantify decadal-scale variability in seagrass dynamics. Perhaps more importantly, our results can serve as a baseline against which we can monitor future change in seagrass communities and their blue carbon.

Water clarity has long been used as a visual indicator of the condition of water quality. The clarity of waters is generally valued for aesthetic and recreational purposes. Water clarity is often assessed using a Secchi disk, attached to a measured line and lowered to a depth where it can be no longer seen. We have applied an approach which uses atmospherically corrected Landsat 8 data to estimate water clarity in freshwater bodies by using the Quasi-Analytical Algorithm (QAA) and Contrast Theory to predict Secchi depths for more than 270 lakes and ponds across the continental US. We found that incorporating Landsat 8 spectral data into methodologies created to retrieve the inherent optical properties (IOP) of coastal waters was effective at predicting in-situ measures of the clarity of inland lakes. The predicted Secchi depths were used to evaluate the recreational suitability for swimming and recreation using an assessment framework developed from public perception of water clarity. Results showed approximately 90 percent of the waterbodies in our dataset were classified as “marginally suitable to suitable” with less than 10 percent classed as “eminently suitable” to “totally unsuitable – unsuitable”. The implications are that satellites engineered for terrestrial applications can be successfully used with traditional ocean color algorithms and methods to measure the water quality of freshwater environments. Further, operational land-based satellite sensors have the temporal repeat cycles, spectral resolution, wavebands, and signal-to-noise ratios to be repurposed to monitor water quality for public use and trophic status of complex inland waters.

The United States Harmful Algal Bloom and Hypoxia Research Control Act of 2014 identified the need for forecasting and monitoring harmful algal blooms (HAB) in lakes, reservoirs, and estuaries across the nation. Temperature is a driver in HAB forecasting models that affects both HAB growth rates and toxin production. Therefore, temperature data derived from the U.S. Geological Survey Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus thermal band products were validated across 35 lakes and reservoirs, and 24 estuaries.

Phytoplankton blooms threaten aquatic ecosystems worldwide, with implications going beyond their apparent ecological aspects. Management solutions are needed to control the appearance of phytoplankton blooms and alleviate their impacts. Such solutions are supported by scientific results, many of which derive from modeling approaches.Data-driven models are now routinely deployed for the short-term (day to weeks) forecasting of phytoplankton dynamics. Nonetheless, such data-oriented efforts are often plagued by two issues, i.e., the lack of sufficient data and interpretability. On one hand, insufficient data result in overfitting, which produces poorly generalizable models that are unreliable under extrapolating conditions. On the other hand, the lack of interpretability hinders the contribution of such models in decision-making, since acting upon model predictions relies heavily on understanding of the model hypothesis.These two challenges motivated the present work, which aspired to investigate the suitability of multi-spectral satellite imagery as a source of phytoplankton-related data for the development of credible and accountable data-driven models. To this end, first, satellite-derived chlorophyll-a times series were created using Sentinel-2 and Landsat 8 imagery and a physics-based modular inversion and processing system. Then, two machine learning algorithms, i.e. a Random Forest (RF) and a Gaussian Process (GP) regression algorithm, were trained to map hydrometeorological drivers to the satellite-derived chlorophyll-a time series.The two algorithms were benchmarked against each other and against a naïve alternative, i.e., the persistence method, in terms of accuracy, uncertainty, and interpretability in three cases: (a) the mesotrophic Mulargia reservoir in Italy, (b) the hypereutrophic Harsha Lake in the USA, and (c) Lake Hume in Australia, a reservoir facing an increasing number of algal bloom events over the last 10 years.Results indicate that both machine learning models forecasted surface phytoplankton dynamics more accurately compared to their naïve alternative up to ten days ahead in the future. It should be noted though that forecasting accuracy deteriorated with increasing forecasting windows, mostly due to the uncertainty of meteorological forecasts.When the machine learning methods were compared to each other, the RF-based models were marginally better compared to their GP counterparts; they produced slightly more accurate and more certain chlorophyll-a predictions. RF-based models are also preferable in terms of interpretability. Their predictions unveiled specific patterns in hydrometeorological data that could explain phytoplankton dynamics in each case. On the contrary, it remained obscure how chlorophyll-a predictions were made by the GP regression models.More importantly this work offers evidence supporting that multi-spectral satellite data allow for the development of theory-guided, data-driven models for the forecasting of phytoplankton dynamics in lakes and reservoirs.

Seagrass meadows are degraded globally and continue to decline in areal extent due to human pressures and climate change. This study used the bio-optical model GrassLight to explore the impact of climate change and anthropogenic stressors on seagrass extent, leaf area index (LAI) and belowground organic carbon (BGC) in St. Joseph Bay, Florida, using water quality data and remotely-sensed sea surface temperature (SST) from 2002 to 2020. Model predictions were compared with satellite-derived measurements of seagrass extent and shoot density from the Landsat images for the same period. The GrassLight-derived area of potential seagrass habitat ranged from 36.2 km

Extraction of petroleum oil resources may result in oil spills in the aquatic environment. Active and passive satellites are generally limited in either spatial coverage, temporal revisit periods, or spatial resolution when tracking surface oil slicks. PlanetScope passive satellites are reported to have near daily global coverage at a resolution of 3.5 m at nadir. These satellites may complement monitoring and fill temporal gaps by leveraging sun glint caused by the nadir viewing angle. Here, we demonstrate potential for PlanetScope satellite usage by investigating overpass timing and sun glint intensity. The United States potential for use was greatest during summer solstice and at lower latitudes. When combined with other high-resolution active and passive satellites, PlanetScope coverage added an average of 86.3 days each year from January 2018 through December 2020, as demonstrated at the Mississippi Canyon Block 20 Saratoga Platform site in the Gulf of Mexico.

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.

Chemical dispersant formulations typically provide maximum oil dispersion in waters between 30–40 ppt (parts per thousand) salt content, which encompasses typical ocean salinity (~34 ppt). As a result, most laboratory studies of oil dispersion effectiveness (DE) are conducted at low to average ocean salinity. Ocean salinity can vary locally from below 20 ppt during ice and snow melt, to extremely high (over 100 ppt) during freeze up periods or within natural brine pools in deeper waters. In this study, the influence of salinity on DE was evaluated using the baffled flask test (BFT) at a dispersant-to-oil ratio (DOR) of 1:25. Benchtop experiments were conducted with Alaskan North Slope (ANS) crude oil in the presence or absence of chemical dispersant at 5 and 25°C and varying salinities (0.2 to 125 ppt). In addition to DE as determined by BFT, oil droplet size distribution (DSD) and fluorescence intensity was measured via a LISST-100X particle size analyzer (Sequoia Scientific, Inc., Bellevue, WA) and ECO fluorometer (Sea Bird - WET Labs, Inc.; Philomath, OR), respectively. Results indicate that in the presence of dispersant, maximum DE occurred at 25ppt, and decreases above and below this salinity. Concentration of small droplets (

When planning monitoring programmes, it is important that all participating institutions, companies and laboratories agree on the type and number of samples that eventually are to be analysed. This chapter focuses on cyanobacteria, cyanotoxins and parameters describing growth conditions favourable for cyanobacteria. Bertani et al. showed that monitoring strategies considerably affect the outcome of cyanobacterial monitoring. A basic understanding of limnology is a prerequisite for planning an appropriate monitoring programme for cyanobacterial occurrence in waterbodies; therefore, persons trained in limnology should be consulted in the planning phase, preferably with knowledge of the local waterbodies. Fieldwork, including site inspection, sample collection and, in most programmes, some on-site analyses, largely determines the quality of information obtained from the subsequent laboratory analyses. The frequency of site inspection and sampling also needs to be adapted to the objective of the programme. Training should include the handling of sampling and measuring devices as well as recognition of visible aspects of cyanobacterial blooms.

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 CI(cyano), 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 CI(cyano) has utility for synoptic and routine monitoring of potentially toxic cyanoHABs in lakes across the United States.

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., Key Points Cyanobacterial harmful algal blooms (cyanoHABs) are a human health risk that is typically mitigated by recreational advisories and warningsWe present a framework to quantify the socioeconomic benefits of using remote sensing technology to test for the presence of cyanoHABsSatellite technology yielded between $55,000 and $1,057,000 in socioeconomic benefits associated with improved human health outcomes

Seagrass form the basis for critically important marine ecosystems. Previously, we implemented a deep convolutional neural network (CNN) model to detect seagrass in multispectral satellite images of three coastal habitats in northern Florida. However, a deep CNN model trained at one location usually does not generalize to other locations due to data distribution shifts. In this paper, we developed a semi-supervised domain adaptation method to generalize a trained deep CNN model to other locations for seagrass detection. First, we utilized a generative adversarial network loss to align marginal data distribution between source domain and target domain using unlabeled data from both data domains. Second, we used a few labelled samples from the target domain to align class specific data distributions between the two domains, based on the contrastive semantic alignment loss. We achieved the best results in 28 out of 36 scenarios as compared to other state-of-the-art domain adaptation methods.

There are a wide range of satellite sensors used for water quality measures with various spatial resolutions. Here, we focus on spatial resolution because some estuaries are smaller than the satellite nadir pixel width, and multiple pixels within an estuary are required for quality assurance . We used the United States Environmental Protection Agency’s (US EPA) Estuarine Data Mapper polygon set to determine the spatial resolution required to reliably image every estuary and sub-estuary in the continental US. Kilometre resolution ocean colour sensors resolve 25% of estuaries and 36% of sub-estuaries with a 3 × 3-pixel grid. The 300 m Ocean and Land Colour Instrument (OLCI) on Sentinel-3 resolves 53% of continental US estuaries with a 3 × 3-pixel grid, and 85% of sub-estuaries. Currently orbiting 30 m resolution land imaging satellites are sufficient to resolve all US sub-estuaries and all estuaries in the Atlantic, 89% in the Gulf of Mexico, and 54% in the Pacific with a 3 × 3-pixel grid. However, 45% of Pacific estuaries cannot be resolved at 30 m resolution, because they have narrow riverine morphology. Our results indicate that a resolution of ≤15 m will be necessary to resolve these narrow riverine estuaries, which is only currently available with commercial satellites.

Cyanobacterial harmful algal blooms (cyanoHAB) cause human and ecological health problems in lakes worldwide. The timely distribution of satellite-derived cyanoHAB data is necessary for adaptive water quality management and for targeted deployment of water quality monitoring resources. Software platforms that permit timely, useful, and cost-effective delivery of information from satellites are required to help managers respond to cyanoHABs. The Cyanobacteria Assessment Network (CyAN) mobile device application (app) uses data from the European Space Agency Copernicus Sentinel-3 satellite Ocean and Land Colour Instrument (OLCI) in near real-time to make initial water quality assessments and quickly alert managers to potential problems and emerging threats related to cyanobacteria. App functionality and satellite data were validated with 25 state health advisories issued in 2017. The CyAN app provides water quality managers with a user-friendly platform that reduces the complexities associated with accessing satellite data to allow fast, efficient, initial assessments across lakes.

Coastal and estuarine ecosystems provide numerous economic and environmental benefits to society. However, increasing anthropogenic activities and developmental pressure may stress these areas and hamper their ecosystem services. Satellite remote sensing could be used as a tool for monitoring water quality parameters, including inherent optical properties (IOP) in coastal regions. Spatio-temporal information on IOP variability will help in understanding the dynamics of the water quality of estuaries. The objective of this research was to develop a novel hybrid model by combining and parameterizing existing quasi analytical and semi-analytical algorithms to estimate IOPs in four oligotrophic northern Gulf of Mexico Florida estuaries. The hybrid model was applied to above surface remote sensing reflectance data representing the Medium Resolution Imaging Spectrometer (MERIS) and Sentinel-3’s Ocean and Land Colour Instrument (OCLI) bands. The hybrid model produced a root means squared error (RMSE) of 0.32 m(−1) (13.95% NRMSE) for total absorption (a(t)), 0.21 m(−1) (7.61% NRMSE) for detritus-gelbstoff absorption (a(dg)), and 0.09 m(−1) (22.77% NRMSE) for phytoplankton pigment absorption (a(phi)). Results showed that absorption by detritus and gelbstoff (a(dg)) dominates the water in these estuaries. Monthly IOP variability in 2010 revealed that compared to other estuaries, magnitudes of IOPs was the highest in Pensacola Bay and therefore the highest attenuation. Findings also indicated that river discharge and precipitation predominantly govern the IOP variations in all four estuaries, showing an increase in IOP values following the high flow period. The hybrid model improved IOP retrieval in these low chlorophyll-a (Chl-a) estuaries where the existing spectral decomposition models did not perform satisfactorily.

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.

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

Cyanobacterial harmful algal blooms (CyanoHAB) are thought to be increasing globally over the past few decades, but relatively little quantitative information is available about the spatial extent of blooms. Satellite remote sensing provides a potential technology for identifying cyanoHABs in multiple water bodies and across geo-political boundaries. An assessment method was developed using MEdium Resolution Imaging Spectrometer (MERIS) imagery to quantify cyanoHAB surface area extent, transferable to different spatial areas, in Florida, Ohio, and California for the test period of 2008 to 2012. Temporal assessment was used to evaluate changes in satellite resolvable inland waterbodies for each state of interest. To further assess cyanoHAB risk within the states, the World Health Organization’s (WHO) recreational guidance level thresholds were used to categorize surface area of cyanoHABs into three risk categories: low, moderate, and high-risk bloom area. Results showed that in Florida, the area of cyanoHABs increased largely due to observed increases in high-risk bloom area. California exhibited a slight decrease in cyanoHAB extent, primarily attributed to decreases in Northern California. In Ohio (excluding Lake Erie), little change in cyanoHAB surface area was observed. This study uses satellite remote sensing to quantify changes in inland cyanoHAB surface area across numerous water bodies within an entire state. The temporal assessment method developed here will be relevant into the future as it is transferable to the Ocean Land Colour Instrument (OLCI) on Sentinel-3A/3B missions.

Water Quality (WQ) condition is based on ecosystem stressor indicators (e.g. water clarity) which are biogeochemically important and critical when considering the Deepwater Horizon oil spill restoration efforts under the 2012 RESTORE Act. Nearly all of the proposed RESTORE projects list restoring WC as a goal, but 90% neglect water clarity. Here, dynamics of optical constituents impacting clarity are presented from a 2009–2011 study within Pensacola, Choctawhatchee, St. Andrew and St. Joseph estuaries (targeted RESTORE sites) in Northwest Florida. Phytoplankton were the smallest contribution to total absorption (a(t-w)PAR) at 412 nm (5–11%), whereas colored dissolved organic matter was the largest (61–79%). Estuarine a(t-w)PAR was significantly related to light attenuation (K(d)PAR), where individual contributors to clarity and the influence of climatic events were discerned. Provided are conversion equations demonstrating interoperability of clarity indicators between traditional State-measured WQ measures (e.g. secchi disc), optical constituents, and even satellite remote sensing for obtaining baseline assessments.

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.

Seagrass form the basis for critically important marine ecosystems. Previously, we implemented a deep convolutional neural network (CNN) model to detect seagrass in multispectral satellite images of three coastal habitats in northern Florida. However, a deep CNN model trained at one location usually does not generalize to other locations due to data distribution shifts. In this paper, we developed a semi-supervised domain adaptation method to generalize a trained deep CNN model to other locations for seagrass detection. First, we utilized a generative adversarial network (GAN) loss to align marginal data distribution between source domain and target domain using unlabeled data from both data domains. Second, we used a few labelled samples from the target domain to align class specific data distributions between the two domains, based on the contrastive semantic alignment loss. We achieved the best results in 28 out of 36 scenarios as compared to other state-of-the-art domain adaptation methods.

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.

Monitoring lake biophysical water quality is a global challenge. Satellite remote sensing offers a technology for continuous water quality information in data poor regions throughout the United States. Quality assurance flag data are provided for the presence of snow/ice, land-adjacency, and unresolvable waterbodies supporting water quality derived measures from Envisat MEdium Resolution Imaging Spectrometer and Sentinel-3 Ocean and Land Colour Instrument for the continental United States. In addition, an updated Waterbody Data mask that contains valid waterbody and coastal ocean delineation is provided. The quality assurance flag datasets can benefit the scientific community in processing lake water quality throughout the contiguous United States by addressing errors from snow/ice, land adjacency, and land masking. The dataset presented here will be used in the development of national scale metrics for derived biophysical water quality in the US.

Observed bio-optical water quality data collected from 2009 to 2011 in Pensacola Bay, Florida were used to develop empirical remote sensing retrieval algorithms for chlorophyll a (Chla), colored dissolved organic matter (CDOM), and suspended particulate matter (SPM). Time-series of the three bio-optical water quality variables were generated from MEdium Resolution Imaging Spectrometer (MERIS) observations from 2003 to 2011. Bio-optical water quality in this estuary exhibited spatial and temporal variations that were correlated to river discharge and wind. Both annual mean and monthly mean bio-optical water quality variables were positively correlated to river discharge. Monthly mean bio-optical water quality variables were also positively correlated to wind speed and wind density (defined by the number of days with daily mean wind speed > 3 m s −1 in a month) over this estuary. These results indicate that bio-optical water quality dynamics in this estuary are vulnerable to changes in river discharge and river constituent loads and local weather conditions such as winter storms and hurricanes.

Satellite remote sensing offers an effective remedy to challenges in ground-based and aerial mapping that have previously impeded quantitative assessments of global seagrass extent. Commercial satellite platforms offer fine spatial resolution, an important consideration in patchy seagrass ecosystems. Currently, no consistent protocol exists for image processing of commercial data, limiting reproducibility and comparison across space and time. Additionally, the radiometric performance of commercial satellite sensors has not been assessed against the dark and variable targets characteristic of coastal waters. This study compared data products derived from two commercial satellites: DigitalGlobe's WorldView-2 and Planet's RapidEye. A single scene from each platform was obtained at St. Joseph Bay in Florida, USA, corresponding to a November 2010 field campaign. A reproducible processing regime was developed to transform imagery from basic products, as delivered from each company, into analysis-ready data usable for various scientific applications. Satellite-derived surface reflectances were compared against field measurements. WorldView-2 imagery exhibited high disagreement in the coastal blue and blue spectral bands, chronically overpredicting. RapidEye exhibited better agreement than WorldView-2, but overpredicted slightly across all spectral bands. A deep convolutional neural network was used to classify imagery into deep water, land, submerged sand, seagrass, and intertidal classes. Classification results were compared to seagrass maps derived from photointerpreted aerial imagery. This study offers the first radiometric assessment of WorldView-2 and RapidEye over a coastal system, revealing inherent calibration issues in shorter wavelengths of WorldView-2. Both platforms demonstrated as much as 97% agreement with aerial estimates, despite differing resolutions. Thus, calibration issues in WorldView-2 did not appear to interfere with classification accuracy, but could be problematic if estimating biomass. The image processing routine developed here offers a reproducible workflow for WorldView-2 and RapidEye imagery, which was tested in two additional coastal systems. This approach may become platform independent as more sensors become available.