Machine Learning Analysis of CYGNSS Data for Chlorophyll Concentration Monitoring in Algal Blooms

This study explores the use of CYGNSS satellite data for detecting cyanobacterial harmful algal blooms (HABs). CYGNSS's unique capabilities in measuring surface reflectivity are harnessed to identify the telltale signs of algal blooms, offering a significant improvement over the traditional observation method. Preliminary results demonstrate that machine learning algorithms applied to CYGNSS data provide effective and timely detection of HABs. This method is promising for enhancing global water quality monitoring and management.