In the bustling urban landscape of Long Island Sound, a new frontier in environmental monitoring is unfolding, one that could revolutionize how we understand and manage our estuaries. Alana Menendez, a researcher from The CUNY Graduate Center and The City College of New York, has developed groundbreaking algorithms that harness the power of high-resolution satellite imagery to monitor key indicators of estuarine health. This work, published in the International Journal of Applied Earth Observations and Geoinformation, promises to enhance our ability to track and manage the complex biogeochemical dynamics of urban estuaries, with significant implications for the energy sector.
Long Island Sound, one of the world’s most urbanized estuaries, is a critical biogeochemical reactor, influenced by both natural and human-induced disturbances. Traditional in situ sampling methods, while valuable, often fall short in capturing the full spatial and temporal dynamics of these ecosystems. Menendez’s research addresses this gap by leveraging satellite sensors to provide a comprehensive view of estuarine processes. “Satellite sensors offer a unique vantage point,” Menendez explains, “allowing us to capture estuarine processes at scales that are not feasible with in situ sampling alone.”
The research focuses on two key indicators of estuarine water quality: colored dissolved organic matter (CDOM) and dissolved organic carbon (DOC). CDOM, which absorbs light in the ultraviolet range, is a critical component of estuarine ecosystems, influencing water quality and biogeochemical processes. Menendez’s algorithms, optimized for Sentinel-2A/2B MSI and Landsat-8/9 OLI sensors, retrieve CDOM absorption at 300 nm (aCDOM(300)) and the absorption spectral slope in the 275–295 nm range (S275-295). These measurements serve as proxies for CDOM amount and quality, respectively.
The algorithms, developed using machine learning (ML) random forest regression, incorporate remote sensing reflectance bands, band ratios, and the month of acquisition. This approach allows for the retrieval of DOC concentrations, which are tightly linked to aCDOM(300) and S275-295. “The tight relationship between aCDOM(300) and S275-295 makes it possible to estimate DOC concentrations accurately,” Menendez notes, highlighting the robustness of the methodology.
The implications of this research extend beyond environmental monitoring. For the energy sector, understanding the biogeochemical dynamics of estuaries is crucial. Estuaries act as natural filters, processing nutrients and pollutants before they reach coastal waters. By providing detailed insights into these processes, Menendez’s algorithms can help energy companies and policymakers make informed decisions about wastewater management, tidal exchanges, and the impact of episodic events. This could lead to more sustainable practices, reducing the environmental footprint of energy production and infrastructure.
The study’s findings reveal strong CDOM spatiotemporal dynamics in Long Island Sound, particularly in the Western Narrows, an area known for its impaired water quality. These dynamics are influenced by seasonal freshwater discharge, tidal wetland carbon export, recurring wastewater pollution, and episodic extreme events. The ability to monitor these dynamics in real-time could significantly enhance estuarine management strategies, ensuring that these critical ecosystems remain resilient and functional.
As we look to the future, Menendez’s research paves the way for more advanced and integrated monitoring systems. The use of machine learning and high-resolution satellite imagery opens new avenues for environmental monitoring, not just in estuaries but in various ecosystems. This could lead to more precise and timely interventions, benefiting both the environment and the energy sector. With continued advancements in satellite technology and data analysis, the potential for transformative change in environmental management is immense.
