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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 (ρt), Rayleigh-corrected reflectances (ρs), and remote sensing reflectances (Rrs). MCI slightly outperformed NDCI across all reflectance products. MCI using ρt 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.

Abstract 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.

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 Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) will provide unique high temporal frequency observations of the United States coastal waters to quantify processes that vary on short temporal and spatial scales. The frequency and coverage of observations from geostationary orbit will improve quantification and reduce uncertainty in tracking water quality events such as harmful algal blooms and oil spills. This study looks at the potential for GLIMR to complement existing satellite platforms from its unique geostationary viewpoint for water quality and oil spill monitoring with a focus on temporal and spatial resolution aspects. Water quality measures derived from satellite imagery, such as harmful algal blooms, thick oil, and oil emulsions are observable with glint <0.005 sr -1 , while oil films require glint >10 -5  sr -1 . Daily imaging hours range from 6 to 12 h for water quality measures, and 0 to 6 h for oil film applications throughout the year as defined by sun glint strength. Spatial pixel resolution is 300 m at nadir and median pixel resolution was 391 m across the entire field of regard, with higher spatial resolution across all spectral bands in the Gulf of Mexico than existing satellites, such as MODIS and VIIRS, used for oil spill surveillance reports. The potential for beneficial glint use in oil film detection and quality flagging for other water quality parameters was greatest at lower latitudes and changed location throughout the day from the West and East Coasts of the United States. GLIMR scan times can change from the planned ocean color default of 0.763 s depending on the signal-to-noise ratio application requirement and can match existing and future satellite mission regions of interest to leverage multi-mission observations., Competing Interests: Declaration of competing interest Antonio Mannino reports financial support was provided by NASA. Joseph Salisbury reports financial support was provided by NASA., (Published by Elsevier Ltd.)

Mapping the seagrass distribution and density in the underwater landscape can improve global Blue Carbon estimates. However, atmospheric absorption and scattering introduce errors in space-based sensors' retrieval of sea surface reflectance, affecting seagrass presence, density, and above-ground carbon ( AGC seagrass ) estimates. This study assessed atmospheric correction's impact on mapping seagrass using WorldView-2 satellite imagery from Saint Joseph Bay, Saint George Sound, and Keaton Beach in Florida, USA. Coincident in situ measurements of water-leaving radiance ( L W ), optical properties, and seagrass leaf area index (LAI) were collected. Seagrass classification and the retrieval of LAI were compared after empirical line height (ELH) and dark-object subtraction (DOS) methods were used for atmospheric correction. DOS left residual brightness in the blue and green bands but had minimal impact on the seagrass classification accuracy. However, the brighter reflectance values reduced LAI retrievals by up to 50% compared to ELH-corrected images and ground-based observations. This study offers a potential correction for LAI underestimation due to incomplete atmospheric correction, enhancing the retrieval of seagrass density and above-ground Blue Carbon from WorldView-2 imagery without in situ observations for accurate atmospheric interference correction., Competing Interests: Conflicts of Interest: The authors declare no conflict of interest.

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Ltd.)

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 tau (τ)| = 0.56; gamma ( γ ) = 2.87 years), but had negligible change (| τ | = 0.03) from 2008 to 2011. Two of the nine regions had moderate (0.3 ≤ | τ | < 0.5) increases in spatial extent from 2017 to 2020, and eight of nine regions had negligible (| τ | < 0.2) change from 2008 to 2011. Twelve states had a strong or moderate increase from 2017 to 2020 (| τ | ≥ 0.3), while only one state had a moderate increase and two states had a moderate decrease from 2008 to 2011. A decrease, or no change, in cyanoHAB spatial extent did not indicate a lack of issues related to cyanoHABs. Sensitivity results of randomly omitted daily CONUS scenes confirm that even with reduced data availability during a short four-year temporal assessment, the direction and strength of the changes in spatial extent remained consistent. We present the first set of national maps of lake cyanoHAB spatial extent across CONUS and demonstrate an approach for quantifying past and future changes at multiple spatial scales. Results presented here provide water quality managers information regarding current cyanoHAB spatial extent and quantify rates of change., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Erin Urquhart, Megan Coffer reports financial support was provided by NASA.

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 (CI cyano ). The true positive rate of agreement with state recreation advisories was 69% and 60% with state reported events. CI cyano detected a reduction or absence in cyanobacteria after 76% of the recreation advisories ended. CI cyano 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., (Copyright © 2022. Published by Elsevier B.V.)

Water quality monitoring is relevant for protecting the designated, or beneficial uses, of water such as drinking, aquatic life, recreation, irrigation, and food supply that support the economy, human well-being, and aquatic ecosystem health. Managing finite water resources to support these designated uses requires information on water quality so that managers can make sustainable decisions. Chlorophyll-a (chl-a, µg L -1 ) concentration can serve as a proxy for phytoplankton biomass and may be used as an indicator of increased anthropogenic nutrient stress. Satellite remote sensing may present a complement to in situ measures for assessments of water quality through the retrieval of chl-a with in-water algorithms. Validation of chl-a algorithms across US lakes improves algorithm maturity relevant for monitoring applications. This study compares performance of the Case 2 Regional Coast Colour (C2RCC) chl-a retrieval algorithm, a revised version of the Maximum-Peak Height (MPH (P) ) algorithm, and three scenarios merging these two approaches. Satellite data were retrieved from the MEdium Resolution Imaging Spectrometer (MERIS) and the Ocean and Land Colour Instrument (OLCI), while field observations were obtained from 181 lakes matched with U.S. Water Quality Portal chl-a data. The best performance based on mean absolute multiplicative error (MAE mult ) was demonstrated by the merged algorithm referred to as C 15 -M 10 (MAE mult  = 1.8, bias mult  = 0.97, n = 836). In the C 15 -M 10 algorithm, the MPH (P) chl-a value was retained if it was > 10 µg L -1 ; if the MPH (P) value was ≤ 10 µg L -1 , the C2RCC value was selected, as long as that value was < 15 µg L -1 . Time-series and lake-wide gradients compared against independent assessments from Lake Champlain and long-term ecological research stations in Wisconsin were used as complementary examples supporting water quality reporting requirements. Trophic state assessments for Wisconsin lakes provided examples in support of inland water quality monitoring applications. This study presents and assesses merged adaptations of chl-a algorithms previously reported independently. Additionally, it contributes to the transition of chl-a algorithm maturity by quantifying error statistics for a number of locations and times., (© 2022. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)