Leveraging Machine Learning and Artificial Intelligence Approaches to Improve Harmful Algal Bloom Monitoring and Forecasts

Distinguishing diatom and dinoflagellate blooms in Chesapeake Bay is difficult because its waters are optically complex and field observations rarely coincide with satellite measurements. This project combines historical phytoplankton observations, NOAA satellite bloom products, geospatial processing and ecological information in a reproducible workflow. Quality control addresses mixed pixels, land contamination, cloud, missing observations, saturation and inconsistent coordinates. Of 31,531 field-to-satellite records, 860 initially pass validation; neighbouring pixels offer further recoverable data. A preliminary Mini U-Net illustrates segmentation feasibility but exposes weak automatic labels, motivating interactive expert validation and masking. Seasonal phenology and environmental variables are evaluated as features for future multimodal models. The NOAA NERTO project was mentored by Michelle Tomlinson and Sachidananda Mishra at NCCOS.