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Harmful Algal Blooms

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By Maryland-Delaware-D.C. Water Science Center July 20, 2026

Studying Harmful Algal Blooms

The MD-DE-DC Water Science Center has developed extensive capabilities for monitoring and predicting harmful algal blooms (HABs).

The MD-DE-DC Water Science Center has developed extensive capabilities for monitoring and predicting harmful algal blooms (HABs).

Our team has designed a carefully characterized SI-traceable hyperspectral microscope system to identify cyanobacteria and other algal taxa, supporting both satellite and field-based remote sensing research.

Data generated from this system have been published as a publicly available data release, with the SMASH paper serving as one example of its application.

Our predictive modeling leverages state-of-the-art machine learning techniques to estimate algal pigment concentrations directly from satellite data at regional, national, and global scales. These data products can be used as powerful monitoring tools, revealing spatial and temporal patterns in historical algal bloom occurrence. Such models are critical for filling gaps in historical pigment concentration measurements and for identifying key drivers of algal activity.

The MD-DE-DC Water Science Center has developed extensive capabilities for monitoring and predicting harmful algal blooms (HABs).

Our team has designed a carefully characterized SI-traceable hyperspectral microscope system to identify cyanobacteria and other algal taxa, supporting both satellite and field-based remote sensing research.

Data generated from this system have been published as a publicly available data release, with the SMASH paper serving as one example of its application.

Our predictive modeling leverages state-of-the-art machine learning techniques to estimate algal pigment concentrations directly from satellite data at regional, national, and global scales. These data products can be used as powerful monitoring tools, revealing spatial and temporal patterns in historical algal bloom occurrence. Such models are critical for filling gaps in historical pigment concentration measurements and for identifying key drivers of algal activity.

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