Remote sensing of cyanobacterial harmful algal blooms: Current trends and future directions

Abstract

Cyanobacterial harmful algal blooms (CyanoHABs) pose significant threats to aquatic ecosystems, public health, and economic sustainability worldwide. This progress report explores recent advancements in CyanoHAB detection, quantification, and monitoring using multi-sensor remote sensing approaches, artificial intelligence (AI) applications, and their integration with health impact studies. We presented the capabilities of various satellite sensors in monitoring CyanoHABs across different spatial and temporal scales, discussing the integration of multiple data sources to overcome individual sensor limitations. The report highlights the promise of AI, particularly machine learning (ML) techniques, in improving CyanoHAB detection and forecasting, demonstrating how ML methods consistently outperformed traditional algorithms in estimating phycocyanin concentrations, a key indicator of CyanoHABs.We examined the development of cloud-based applications for real-time monitoring and public awareness. Furthermore, we explored the health impacts of CyanoHABs on humans and animals, emphasizing the role of remote sensing and ML in mitigating these effects. The economic implications of CyanoHAB-related health issues are discussed, along with the potential for integrating remote sensing data with epidemiological studies. Overall, this report underscores the importance of cross-disciplinary, integrated approaches that combine cutting-edge remote sensing technologies, advanced ML techniques, and health impact assessments to address the complex challenges posed by CyanoHABs in inland waters.