Coastal resources are prone to intertwined effects of climate variability and anthropogenic stressors. With their massive societal and economic benefits through fisheries, aquaculture, and recreation, it is imperative for decision-making entities to integrate the highest-quality data and observations into decision support systems, thereby enhancing coastal management and monitoring . To further enrich existing observational capabilities, we have developed an expedited data processing system that ingests, processes, and displays water quality (WQ) maps ( i.e., chlorophyll-a, Secchi, total suspended solids) from high- resolution imagery (10 – 30 m) of Landsat and Sentinel-2 missions. This web-based platform, STREAM (a s atellite-based analysis t ool for r apid e valuation of a quatic environments ), offers globally validated WQ products developed using a processing engine that relies on a machine-learning model. For its interface, we harness various tools and capabilities that have already been developed as part of NASA’s near-real-time data processing systems (e.g., Fire Information for Resource Management System) . It allows end-users to visualize WQ maps, identify pixel values, and view time-series plots for a given pixel or a region. STREAM will enable low-latency (< 6 hours) detection of anomalous WQ conditions for robust and timely decision-making. The system is currently live and supports processing at select regions.