Cyanobacterial harmful algal blooms (CyanoHABs) are a growing ecological and public health concern in inland and coastal water bodies across the United States. Satellite remote sensing, particularly Sentinel-3 Ocean Land and Colour Instrument (OLCI) products, is commonly used to monitor these blooms in large lakes. Sentinel-2 (S2), with a 20-meter spatial resolution, could enhance monitoring of smaller lakes. However, the performance and consistency of S2 compared to Sentinel-3 (300 meters) have not been thoroughly evaluated across regions. Limited in-situ validation underscores the need for a data-driven, satellite-to-satellite cross-validation framework to assess sensor consistency, evaluate algorithm robustness, and address uncertainties.
This summer intern project will apply data science and statistical evaluation techniques to assess the reliability of existing S2-derived cyanobacteria bloom indicators and to explore the development of new algorithms applicable to both S2 and S3 sensors. The objectives are 1) Develop a reproducible, scalable workflow for cross-sensor comparison of Sentinel-2 and Sentinel-3 cyanobacterial products, 2) Quantify inter-sensor agreement and uncertainty, and 3) Evaluate spatial scale effects using data aggregation strategies.