Background:Cyanobacterial harmful algal blooms (cyanoHABs) pose risks to human and animal health.Methods:We investigated the relationship between cyanoHABs and asthma or wheeze-related emergency department (ED) visits near three Wisconsin cities (Green Bay, Madison, and Oshkosh) during 2017-2019. CyanoHAB exposure was approximated using the Cyanobacterial Assessment Network remotely sensed satellite indicator of cyanobacterial biomass, a chlorophyl algorithm (ChlBS) aggregated by water-adjacent ZIP Code Tabulation Areas (ZCTA), and distance weighted from the nearest waterbody. Weekly counts of ED visits for asthma or wheeze were aggregated by ZCTA. Poisson generalized linear models estimated the association between the weekly number of ED visits and weekly ChlBS, adjusting for maximum temperature, dewpoint, fine particulate matter (PM2.5), month, and correlation within ZCTA.Results:During 2017-2019, 7,057 ED visits for asthma or wheeze occurred in the study area (42 ZCTAs). Peaks in ChlBS occurred between June and October, with higher values in Lake Winnebago and Lake Mendota compared to Green Bay. ChlBS was not associated with ED visits for asthma or wheeze (adjusted rate ratio = 1.00, 95% confidence interval = 0.99, 1.00), and the presence of onshore winds did not change this result. Monthly aggregations of ED visits and ChlBS showed a monotonic trend between increasing ChlBS and ED visits during July-September.Conclusion:This study demonstrates the utility of remote sensing data in environmental health research. Future studies could explore individual-level exposure and outcomes to refine health risks associated with cyanoHABs.

Tidal marshes offer numerous ecological and economic benefits but are threatened by human development, sea-level rise, and invasive species. Routine mapping of their species composition is crucial for coastal management, yet current efforts are spatially and temporally limited and lack species identification. To address this, we used 3-m PlanetScope satellite imagery to classify four common marsh plants across Virginia's Middle Peninsula in Chesapeake Bay, a region targeted for restoration given its ecological and economic significance. We developed a random forest classifier using May 2021 reference data from the Virginia Institute of Marine Science, delineating Spartina alterniflora, Phragmites australis, Spartina patens, and Juncus roemerianus at eight marshes. Balanced agreement was strong at 93%, ranging from 63% to 98% across individual marshes. We then expanded to the broader Middle Peninsula, using the Mann-Whitney U test to compare satellite- and reference-derived coverage of P. australis, finding moderate agreement despite a large temporal offset (rrb = 0.45; N = 152). Following suitable model performance, we generated annual assessments for May 2021-2024. S. alterniflora was most extensively distributed, covering half of our study area. We also analyzed random forest class probabilities to inform data collection and model interpretation. Probabilities were generally above 0.6, although those accompanying J. roemerianus were notably lower. We present methods for large-scale species mapping to inform resource prioritization and coastal management, including a framework for communicating classification certainty, which is adaptable to stakeholder needs. Our framework can be used to retrain our classification model for application elsewhere, where local field data is available.

We developed a model to predict surface water temperature across U.S. lakes using satellite remote sensing and in situ observations to enhance cyanobacterial harmful algal bloom (cyanoHAB) forecasting. The study focused on Sentinel-3 Ocean and Land Colour Instrument (OLCI) sensor resolved lakes. We developed random forest models using both Landsat-derived and in-situ-measured surface water temperature. Landsat models offered broad spatial and temporal coverage of all OLCI resolved lakes, but they were sensitive to cloud cover and required filtering to minimize error. In contrast, the in situ model represented fewer OLCI resolved lakes, but yielded lower mean absolute error and bias. The models predicted lake surface temperature across the entire calendar year, with best performance (RMSEapplied = 1.11; biasapplied = 0.01; MAEapplied = 0.77) from the in situ model. This approach allowed for the continuous prediction of lake surface temperatures from 1.1 to 31.6 °C for unfrozen, open‑water conditions critical for improving the accuracy of cyanoHAB forecasting. A key strength of this study was the use of an extensive dataset and model validation against in situ observations, which improved predictive accuracy throughout the year across all seasons. The predictive model offers a water resource tool for management, ecosystem protection, and public health.

Satellite constellations often launch platforms over several years, increasing observational frequency and capturing additional, potentially more extreme, events. Consequently, reported changes in satellite-derived data may inadvertently capture variations in observational frequency rather than true environmental trends. This study used the Sentinel-3 Cyanobacteria Index (CI-cyano) to assess impacts of varying observational frequency on data distributions and trends. Daily CI-cyano was temporally aggregated into weekly composites using maximum, mean, and median values as both continuous and ordinal observations. Sentinel-3A, Sentinel-3B, and combined Sentinel-3A & -3B were compared using the Wilcoxon signed-rank test. For continuous observations, temporally aggregating via the maximum value showed a large 9% increase for combined Sentinel-3A & -3B versus Sentinel-3A or Sentinel-3B individually, compared to a small 1% decrease for temporal aggregation via the mean and negligible differences via the median. For ordinal observations, temporal aggregation via the maximum and mean showed large increases of up to 25% for combined Sentinel-3A & -3B, while the median showed small decreases of up to 5%. The seasonal Mann-Kendall trend test was then applied to Sentinel-3 imagery from 2016 to 2023, with and without observations from Sentinel-3B. Temporal aggregation via the maximum showed a moderate 20% increase with Sentinel-3B compared to a small 8% increase without Sentinel-3B; mean and median showed negligible trends. An abbreviated assessment using Sentinel-2 had similar results, with large increases for combined Sentinel-2A & -2B via the maximum, but small and moderate decreases via mean and median. Results suggest that temporal aggregation impacts multi-platform datasets. For more consistent summaries, continuous datasets should be temporally aggregated using the mean or median, and ordinal datasets using the median. Results are applicable to any satellite-derived water quality datasets with varied observational frequency. This study addresses a critical gap in the remote sensing community, ensuring relevant statistical concepts are appropriately applied in multi-platform analyses.

The lateral transport of dissolved organic carbon (DOC) is crucial in tidal marsh carbon budgets, but estimating DOC remains a challenge due to the dynamic nature of terrestrial-aquatic interfaces and limitations of in situ observations. This study used a linear model to correlate DOC and chromophoric dissolved organic matter (CDOM) absorption coefficient at 440 nm ( a CDOM ( 440 ) ; r 2 = 0.75 ) in Bald Head Creek, North Carolina, USA. Modeled DOC concentrations were merged with current velocity derived from the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM) to estimate daily DOC fluxes from January to April 2021, representing winter and spring in the mid-latitude northern hemisphere. DOC flux depended on season and tide, where negative values represent DOC import into the marsh and positive values represent export to the estuary. Results showed that DOC was exported from the marsh in February and March after peak rainfall. While tidal inundation and increased river flow from precipitation influenced DOC flux, the average daily DOC import of -13.6 ± 6.0 g C m-2 d-1 was greater than the average daily DOC export of 9.4 ± 8.3 g C m-2 d-1 during the study period, showing that the marsh was a net sink for DOC during the early spring. This study demonstrated that field measurements with models like SCHISM provided a synoptic representation of DOC flux, where rainfall-induced river discharge impacts DOC mobilization and export to coastal systems like Bald Head Creek.

Mountain lakes provide cultural, aesthetic, and recreational services across the globe. Despite their recognized importance, there is no consistent definition of a mountain lake, which hampers describing them individually as well as in aggregate. Additionally, it makes it difficult to study and manage this unique population. We develop a rules-based approach for classifying mountain lakes according to the topography of the area draining directly to the lake. We apply the approach to the data from the United States National Lakes Assessment, for which the population is defined as lakes that are at least 1 ha in surface area and at least 1 m deep in the conterminous US (CONUS). Leveraging this national assessment allows for evaluating the condition of mountain lakes relative to all lakes in the CONUS. There are an estimated 12,353 (95 % C.I. 10,529-14,177) mountain lakes that account for 6.4 % of the lake population in the CONUS. Mountain lakes are in better condition than non-mountain lakes for 11 of 12 physical, chemical, and biological indicators (acid neutralizing capacity was the one exception). Approximately 25 % of mountain lakes are classified as eutrophic or hypereutrophic, and nearly 50 % are in fair or poor condition with respect to riparian vegetation and lakeshore disturbance. Mountain lake watersheds have lower proportions of developed land cover (mean ± 95 % CI: 0.8 ± 0.1 %) compared to non-mountain lakes (6.7 ± 0.3 %); however, developed land cover is more concentrated closer to the lakeshore for mountain lakes compared to non-mountain lakes. Coupled with characteristics such as high runoff, low hydraulic conductivity, and shallow bedrock depths, mountain lakes may be more susceptible to the adverse effects of human development and climate change compared to non-mountain lakes. These findings underscore the need for targeted monitoring, conservation, and management strategies to protect these valuable and sensitive lake environments.

Chemical dispersant formulations typically provide maximum oil dispersion in waters between 30 and 40 ppt (parts per thousand) salt content, which encompasses typical 35 ppt ocean salinity. Ocean salinity can vary locally, from very low values due to freshwater river inflows or ice melt, to extremely high values during freeze up periods or within natural brine pools. In this study, the influence of salinity (0.2-125 ppt) on dispersion effectiveness (DE) was evaluated for three oils and four dispersants using the baffled flask test (BFT) and oil droplet size distribution (DSD) measures. Tank-scale dispersions support the laboratory-scale results of DSD. Light and medium crude oils were effectively dispersed over a wide salinity range using the BFT. However, dispersions exhibiting similar DE values possessed a wide range in DSD metrics, suggesting that the latter offers useful information for spill planning and understanding the transport and fate of spilled oil.

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 mu g 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.

The Chowan River flows from southern Virginia through northeastern North Carolina and into the Albemarle Sound, a part of the second largest U.S. estuary. The Chowan, which serves as an important recreational area and provides critical nursery habitat for multiple vulnerable species, has garnered much attention in recent years due to recurrent cyanobacterial harmful algal blooms (cHABs) associated with microcystins (MCs). Here we document unprecedented toxic blooms of Microcystis spp. during summer and fall of 2019 with MC concentrations two to three orders above the recreational guidelines of the Environmental Protection Agency (EPA, 2019). Based on 16S sequencing results in this study and previously published reports, the genus Microcystis emerged as a primary concern within the region. Shifts in assemblage composition, including relative abundance of Microcystis spp. and contributions from potential MC-degraders, linked to overall toxin concentrations and bloom stage. Congeners of varying toxicity, mainly MC-RR and MC-LR, were the most prevalent, corroborating that congeners other than MC-LR should be considered as health risk guidelines are developed. Downstream toxin transport was indicated based on changes in accumulated dissolved MC within the western Albemarle Sound which matched toxin dynamics in the Chowan River. This study provides important novel data on bacterial community composition, MC dynamics, and spatial connectivity for the Chowan River region that can aid monitoring approaches and management strategies for the protection of public health along the Chowan River and within the western Albemarle Sound.

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.

Seagrasses have been widely recognized for their ecosystem services, but traditional seagrass monitoring approaches emphasizing ground and aerial observations are costly, time-consuming, and lack standardization across datasets. This study leveraged satellite imagery from Maxar's WorldView-2 and WorldView-3 high spatial resolution, commercial satellite platforms to provide a consistent classification approach for monitoring seagrass at eleven study areas across the continental United States, representing geographically, ecologically, and climatically diverse regions. A single satellite image was selected at each of the eleven study areas to correspond temporally to reference data representing seagrass coverage and was classified into four general classes: land, seagrass, no seagrass, and no data. Satellite-derived seagrass coverage was then compared to reference data using either balanced agreement, the Mann-Whitney U test, or the Kruskal-Wallis test, depending on the format of the reference data used for comparison. Balanced agreement ranged from 58% to 86%, with better agreement between reference- and satellite-indicated seagrass absence (specificity ranged from 88% to 100%) than between reference- and satellite-indicated seagrass presence (sensitivity ranged from 17% to 73%). Results of the Mann-Whitney U and Kruskal-Wallis tests demonstrated that satellite-indicated seagrass percentage cover had moderate to large correlations with reference-indicated seagrass percentage cover, indicative of moderate to strong agreement between datasets. Satellite classification performed best in areas of dense, continuous seagrass compared to areas of sparse, discontinuous seagrass and provided a suitable spatial representation of seagrass distribution within each study area. This study demonstrates that the same methods can be applied across scenes spanning varying seagrass bioregions, atmospheric conditions, and optical water types, which is a significant step toward developing a consistent, operational approach for mapping seagrass coverage at the national and global scales. Accompanying this manuscript are instructional videos describing the processing workflow, including data acquisition, data processing, and satellite image classification. These instructional videos may serve as a management tool to complement field- and aerial-based mapping efforts for monitoring seagrass ecosystems.

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.

Potential acute and chronic human health effects associated with exposure to cyanobacteria and cyanotoxins, including respiratory symptoms, are an understudied public health concern. We examined the relationship between estimated cyanobacteria biomass and the frequency of respiratory-related hospital visits for residents living near Green Bay, Lake Michigan, Wisconsin during 2017-2019. Remote sensing data from the Cyanobacteria Assessment Network was used to approximate cyanobacteria exposure through creation of a metric for cyanobacteria chlorophyll-a (ChlBS). We obtained counts of hospital visits for asthma, wheezing, and allergic rhinitis from the Wisconsin Hospital Association for ZIP codes within a 3-mile radius of Green Bay. We analyzed weekly counts of hospital visits versus cyanobacteria, which was modelled as a continuous measure (ChlBS) or categorized according to World Health Organization's (WHO) alert levels using Poisson generalized linear models. Our data included 2743 individual hospital visits and 114 weeks of satellite derived cyanobacteria biomass indicator data. Peak values of ChlBS were observed between the months of June and October. Using the WHO alert levels, 60% of weeks were categorized as no risk, 19% as Vigilance Level, 15% as Alert Level 1, and 6% as Alert Level 2. In Poisson regression models adjusted for temperature, dewpoint, season, and year, there was no association between ChlBS and hospital visits (rate ratio [RR] [95% Confidence Interval (CI)] = 0.98 [0.77, 1.24]). There was also no consistent association between WHO alert level and hospital visits when adjusting for covariates (Vigilance Level: RR [95% CI] 0.88 [0.74, 1.05], Alert Level 1: 0.82 [0.67, 0.99], Alert Level 2: 0.98 [0.77, 1.24], compared to the reference no risk category). Our methodology and model provide a template for future studies that assess the association between cyanobacterial blooms and respiratory health.

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.

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 (AGCseagrass) 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 (Lw), 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.

The management and governance of our surface waters is core to life and prosperity on our planet. However, monitoring data are not available to many potential users and the disparate nature of water bodies makes consistent monitoring across so many systems difficult. While satellite Earth observation (EO) offers solutions, there are numerous challenges that limit the use of satellite EO for water monitoring. To understand the perceptions of using satellite EO for water quality monitoring, a survey was conducted within academia and the water quality management sector. Study objectives were to assess community understanding of satellite EO water quality data, identify barriers in the adoption of satellite EO data, and analyse trust in satellite EO data. Most (40 %) participants were beginners with little understanding of satellite EO. Participants indicated problems with satellite EO data accessibility (31 %) and interpretability (26 %). Results showed a high level of trust with satellite EO data and higher trust with in-situ EO data. This study highlighted the gap between water science, applied social science, and policy. A transdisciplinary approach to managing water resources is needed to bridge water disciplines and take a key role in areas such as social issues, knowledge brokering, and translation.

Water clarity has long been used as a visual indicator of the condition of water quality. The clarity of waters is generally valued for esthetic 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 the 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 reservoirs 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 water bodies. 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 54% of the water bodies in our dataset were classified as "marginally suitable to suitable" with approximately 31% classed as "eminently suitable" and approximately 15% classed as "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. Furthermore, 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.

Cyanobacterial harmful algal blooms (cyanoHABs) in reservoirs can be transported to downstream waters via scheduled discharges. Transport dynamics are difficult to capture in traditional cyanoHAB monitoring, which can be spatially disparate and temporally discontinuous. The introduction of satellite remote sensing for cyanoHAB monitoring provides opportunities to detect where cyanoHABs occur in relation to reservoir release locations, like canal inlets. The study objectives were to assess (1) differences in reservoir cyanoHAB frequencies as determined by in situ and remotely sensed data and (2) the feasibility of using satellite imagery to identify conditions associated with release-driven cyanoHAB export. As a representative case, Lake Okeechobee and the St. Lucie Estuary (Florida, USA), which receives controlled releases from Lake Okeechobee, were examined. Both systems are impacted by cyanoHABs, and the St. Lucie Estuary experienced states of emergency for extreme cyanoHABs in 2016 and 2018. Using the European Space Agency's Sentinel-3 OLCI imagery processed with the Cyanobacteria Index (CIcyano), cyanoHAB frequencies across Lake Okeechobee from May 2016-April 2021 were compared to frequencies from in situ data. Strong agreement was observed in frequency rankings between the in situ and remotely sensed data in capturing intra-annual variability in bloom frequencies across Lake Okeechobee (Kendall's tau = 0.85, p-value = 0.0002), whereas no alignment was observed when evaluating inter-annual variation (Kendall's tau = 0, p-value = 1). Further, remotely sensed observations revealed that cyanoHABs were highly frequent near the inlet to the canal connecting Lake Okeechobee to the St. Lucie Estuary in state-ofemergency years, a pattern not evident from in situ data alone. This study demonstrates how remote sensing can complement traditional cyanoHAB monitoring to inform reservoir release decision making.

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.

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.