Spatiotemporal Modelling and Monitoring of Harmful Algal Blooms Using IoT in Lake Victoria Basin: Integrating & Remote Sensing for Near Real-Time HAB Water Quality

Harmful algal blooms (HABs) are an increasing global threat to water security, aquatic ecosystems, public health and UN's sustainable development. As freshwater ecosystems continue to face growing environmental pressure, this research presents an innovative geospatial framework that integrates satellite remote sensing with low-cost Internet of Things (IoT) technologies for near real-time monitoring of HAB dynamics in Kenya's Lake Victoria Basin. By integrating geospatial remote sensing analytics, thermal modelling, Landsat 8 ocean colour algorithms and smart in-situ IoT sensor networks, the study successfully mapped cyanobacteria blooms, monitored chlorophyll-a concentrations and detected abnormal lake surface air temperature (LSAT) variations associated with bloom events. The framework was further validated using Sentinel-3 OLCI and NASA MODIS datasets, demonstrating strong reliability and scalability for environmental intelligence and aquatic ecosystem monitoring. The findings consequently highlight the potential of emerging technologies to transform HAB surveillance through timely reporting, early warning systems and data-driven environmental management strategies. This work therefore contributes toward advancing sustainable water resource management and supporting broader Sustainable Development Goals (SDGs) through innovative, cost-effective and scalable monitoring solutions.