Remote sensing-based prediction of cyanobacterial harmful algal blooms in riverine systems

Abstract

Cyanobacterial Harmful Algal Blooms (CyanoHABs) pose signifi cant environmental and public health challenges, particularly in inland water bodies affected by nutrient enrichment and climate variability. This study explores the application of Sentinel-3 Ocean and Land Colour Instrument (OLCI) Level-1b satellite data for detecting and analyzing CyanoHABs in the Yamuna River, a highly urbanized and ecologically stressed river system in northern India. Using chlorophyll a concentration and meteorological parameters such as temperature, rainfall, and relative humidity, this research investigates the spatial and temporal dynamics of algal blooms. A systematic preprocessing workflow involving spatial filtering, flag masking, and outlier detec tion was employed to ensure data accuracy. Correlation analysis and visualization techniques were applied to examine the relationships be tween chlorophyll-a concentrations and environmental factors. Re sults indicate a strong positive correlation with temperature, a weak positive relationship with rainfall, and a moderate negative correla tion with humidity. Seasonal patterns reveal peak chlorophyll-a levels during pre-monsoon months, declining during post-monsoon and win ter periods. These findings underscore the utility of remote sensing combined with machine learning approaches for monitoring and pre dicting CyanoHABs, offering insights for early warning systems and water quality management strategies.