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.

Assessing the suitability of lakes and reservoirs for recreation using Landsat 8.

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.

Assessing potential of the Geostationary Littoral Imaging and Monitoring Radiometer (GLIMR) for water quality monitoring across the coastal United States.

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.

Providing a framework for seagrass mapping in United States coastal ecosystems using high spatial resolution satellite imagery.

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.

Satellites quantify the spatial extent of cyanobacterial blooms across the United States at multiple scales.

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.

Potential for commercial PlanetScope satellites in oil response monitoring.

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.

Simulated response of St. Joseph Bay, Florida, seagrass meadows and their belowground carbon to anthropogenic and climate impacts.

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 km2 to 39.2 km2, averaging 38.0 ± 0.8 km2 compared to an observed seagrass extent of 23.0 ± 3.0 km2 derived from Landsat (range = 17.9-27.4 km2). GrassLight predicted a mean seagrass LAI of 2.7 m2 leaf m-2 seabed, compared to a mean LAI of 1.9 m2 m-2 estimated from Landsat, indicating that seagrass density in St. Joseph Bay may have been below its light-limited ecological potential. Climate and anthropogenic change simulations using GrassLight predicted the impact of changes in temperature, pH, chlorophyll a, chromophoric dissolved organic matter and turbidity on seagrass meadows. Simulations predicted a 2-8% decline in seagrass extent with rising temperatures that was offset by a 3-11% expansion in seagrass extent in response to ocean acidification when compared to present conditions. Simulations of water quality impacts showed that a doubling of turbidity would reduce seagrass extent by 18% and total leaf area by 21%. Combining climate and water quality scenarios showed that ocean acidification may increase seagrass productivity to offset the negative effects of both thermal stress and declining water quality on the seagrasses growing in St. Joseph Bay. This research highlights the importance of considering multiple limiting factors in understanding the effects of environmental change on seagrass ecosystems.

Vertical artifacts in high-resolution WorldView-2 and WorldView-3 satellite imagery of aquatic systems.

Satellite image artefacts are features that appear in an image but not in the original imaged object and can negatively impact the interpretation of satellite data. Vertical artefacts are linear features oriented in the along-track direction of an image system and can present as either banding or striping; banding are features with a consistent width, and striping are features with inconsistent widths. This study used high-resolution data from DigitalGlobe's (now Maxar) WorldView-3 satellite collected at Lake Okeechobee, Florida (FL), on 30 August 2017. This study investigated the impact of vertical artefacts on both at-sensor radiance and a spectral index for an aquatic target as WorldView-3 was primarily designed as a land sensor. At-sensor radiance measured by six of WorldView-3's eight spectral bands exhibited banding, more specifically referred to as non-uniformity, at a width corresponding to the multispectral detector sub-arrays that comprise the WorldView-3 focal plane. At-sensor radiance measured by the remaining two spectral bands, red and near-infrared (NIR) #1, exhibited striping. Striping in these spectral bands can be attributed to their time delay integration (TDI) settings at the time of image acquisition, which were optimized for land. The impact of vertical striping on a spectral index leveraging the red, red edge, and NIR spectral bands-referred to here as the NIR maximum chlorophyll index (MCINIR)-was investigated. Temporally similar imagery from the European Space Agency's Sentinel-3 and Sentinel-2 satellites were used as baseline references of expected chlorophyll values across Lake Okeechobee as neither Sentinel-3 nor Sentinel-2 imagery showed striping. Striping was highly prominent in the MCINIR product generated using WorldView-3 imagery, as noise in the at-sensor radiance exceeded any signal of chlorophyll in the image. Adjusting the image acquisition parameters for future tasking of WorldView-3 or the functionally similar WorldView-2 satellite may alleviate these artefacts. To test this, an additional WorldView-3 image was acquired at Lake Okeechobee, FL, on 26 May 2021 in which the TDI settings and scan line rate were adjusted to improve the signal-to-noise ratio. While some evidence of non-uniformity remained, striping was no longer noticeable in the MCINIR product. Future image tasking over aquatic targets should employ these updated image acquisition parameters. Since the red and NIR #1 spectral bands are critical for inland and coastal water applications, archived images not collected using these updated settings may be limited in their potential for analysis of aquatic variables that require these two spectral bands to derive.

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.

Merging of the Case 2 Regional Coast Colour and Maximum-Peak Height chlorophyll-a algorithms: validation and demonstration of satellite-derived retrievals across US lakes.

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 (MAEmult) was demonstrated by the merged algorithm referred to as C15-M10 (MAEmult = 1.8, biasmult = 0.97, n = 836). In the C15-M10 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.

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.

Satellites for long-term monitoring of inland U.S. lakes: The MERIS time series and application for chlorophyll-a.

Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll algorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, ChlBS, which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS.

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.

An initial validation of Landsat 5 and 7 derived surface water temperature for U.S. lakes, reservoirs, and estuaries.

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. In situ data from the Water Quality Portal (WQP) were used for validation. The WQP serves data collected by state, federal, and tribal groups. Discrete in situ temperature data included measurements at 11,910 U.S. lakes and reservoirs from 1980 through 2015. Landsat temperature measurements could include 170,240 lakes and reservoirs once an operational product is achieved. The Landsat-derived temperature mean absolute error was 1.34°C in lake pixels >180 m from land, 4.89°C at the land-water boundary, and 1.11°C in estuaries based on comparison against discrete surface in situ measurements. This is the first study to quantify Landsat resolvable U.S. lakes and reservoirs, and large-scale validation of an operational satellite provisional temperature climate data record algorithm. Due to the high performance of open water pixels, Landsat satellite data may supplement traditional in situ sampling by providing data for most U.S. lakes, reservoirs, and estuaries over consistent seasonal intervals (even with cloud cover) for an extended period of record of more than 35 years.