Evaluation of a satellite-based cyanobacteria bloom detection algorithm using field-measured microcystin data.

Area of Science:

• Environmental Science
• Remote Sensing
• Ecology

Background:

• Cyanobacterial harmful algal blooms (CyanoHABs) are increasing globally, posing risks to recreational and drinking water sources due to potential cyanotoxin exposure.
• Effective monitoring strategies are crucial for public health and water resource management.
• Remote sensing offers a valuable tool for large-scale, frequent assessment of water quality.

Purpose of the Study:

• To validate the effectiveness of the Cyanobacteria Index CIcyano algorithm for identifying toxin-producing CyanoHABs in lakes across the United States.
• To assess the algorithm's performance using satellite data and in-situ measurements of Microcystins (MCs).

Main Methods:

• A matchup dataset was created using satellite data (MERIS, OLCI) and field-measured Microcystins (MCs) data from 11 states over 11 bloom seasons (2005-2011, 2016-2019).
• The CIcyano algorithm's ability to detect CyanoHAB presence or absence was evaluated, with MCs serving as a proxy for bloom confirmation and health risk.
• Algorithm performance was assessed using metrics such as overall accuracy, precision, and recall.

Main Results:

• The CIcyano algorithm demonstrated an overall accuracy of 84% for CyanoHAB detection using same-day satellite and in-situ data matchups.
• Precision and recall for bloom detection were high, at 87% and 90%, respectively.
• Bootstrapping simulations indicated an expected overall accuracy between 77% and 87% (95% confidence).

Conclusions:

• The CIcyano algorithm is a validated and effective tool for the synoptic and routine monitoring of potentially toxic CyanoHABs in lakes nationwide.
• The findings support the utility of remote sensing for early detection and management of harmful algal blooms.
• This approach aids in safeguarding public health and water resources from cyanotoxin exposure.