Kate Meyers

Water quality monitoring is integral to preserving the health of freshwater ecosystems, and satellite remote sensing has emerged as one monitoring method. Sentinel-2, in particular, has been valuable for water quality monitoring due to its 5-day global temporal revisit time and spatial resolution that ranges from 10 to 60 metres. Sentinel-2 can be used to measure and monitor chlorophyll-a, which historically has been used as an indicator of water quality, eutrophication and harmful algal blooms. Our goal was to review aquatic chlorophyll-a Sentinel-2 research to assess the types of validation evidence reported. Validation evidence is defined here as the set of information key to assessing algorithm performance, and include the spatial and temporal scales of satellite validation, reported in situ sampling method context information, demonstration of validation results through plots, and appropriate algorithm performance metrics. We highlight how the body of literature collectively contributes to advancing a national scale chlorophyll-a product that could support future resource management applications. Our review of 122 published studies indicated that much of the validation evidence corresponded to early stages, as defined by the NASA data maturity framework, due to a limited focus on individual lakes and limited detail on methodology for reproducibility. Prioritizing data accessibility for both in situ data and satellite workflows used in published studies; reporting methods with transparency and consistency; and using standard algorithm performance metrics could provide a consistent framework to support and enhance the utility of satellite inland water quality research. These three quality assurance mechanisms can promote effective evaluation of approaches for remote sensing of chlorophyll-a. Adopting these quality criteria could enable the integration of validation evidence from multiple studies, supporting more spatially and temporally representative products that would advance these approaches towards maturation for broader application.

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.

Publicly available cyanobacterial harmful algal blooms (cyanoHAB) forecasts are now available at www.epa.gov/habs/hab-forecasts, offering water quality and public health managers a tool for anticipating cyanoHAB conditions in the largest United States lakes and reservoirs. These near-term forecasts support proactive management actions, such as sampling and additional monitoring, aligned with the U.S. Harmful Algal Bloom and Hypoxia Research and Control Act call for advancements in cyanoHAB forecasting. Accurate forecasting technology could save communities health related costs, monitoring and response resources, and drinking water treatment costs through early detection of cyanoHABs. A hierarchical Bayesian spatiotemporal model predicts cyanoHABs by determining probabilities of weekly lake exceedance of 12 μg/L chlorophyll-a for 2,192 satellite resolved lakes across the contiguous United States. The 2021–2023 predictions from this model were evaluated to determine occurrence of false negative and false positive predictions to update model interpretation. In total, 331,049 events were analyzed, of which false negatives represented 1.34% and false positives 8.06% of forecasts. False negatives were predominantly events lasting 1–3 weeks in the late fall. False positives varied in temporal length but were typically associated with actual blooms, often incorrectly forecasting blooms before, after, or between actual bloom events. The three-year model run demonstrated performance consistency and operational readiness. A stoplight approach for probability reporting could improve data visualization. Insights into forecast limitations support transparent communication and stakeholder engagement. This evaluation helps managers interpret model limitations when integrating results into decision frameworks to mitigate cyanoHAB impacts, protecting public and environmental health.