A review on remote-sensing-based harmful cyanobacterial bloom monitoring services

Abstract

Optical satellite observations have been recently introduced as the backbone of several harmful algal bloom monitoring frameworks for regional or continental-scale decision-making. Documented in prior peer-reviewed publications, these satellite-based decision support systems are not directly comparable, making a synthesis effort inevitable for future improvements. This review highlights select, widely used harmul cyanobacteria bloom (cyanoHABs) monitoring services, including the Cyanobacteria Assessment Network (CyAN), Cyanobacterial Bloom Indicator (CyaBI), CyanoTRACKER, EOLakeWatch, and CyanoKhoj, by focusing on their effectiveness in freshwater and inland waters. We selected these systems for their widespread use, documented effectiveness, and diverse approaches to cyanoHABs monitoring. These services provide early warnings and actionable insights, enabling effective responses to protect water quality, ecosystem health, and public safety. It considers the broader remote-sensing-based monitoring landscape, noting the capabilities and impacts of these services. Our assessments underscore the transformative impact of services like CyAN, which provide robust early warnings using the Cyanobacteria Index (CI). CyanoTRACKER and EOLakeWatch improve community engagement and data collection, increasing monitoring effectiveness. CyanoKhoj leverages high-resolution monitoring through GEE, offering valuable insights. The quality of cyanoHABs products depends on satellite imagery and processing level, noting that most processors leverage Top of Atmosphere or Rayleigh-corrected reflectance products to arrive at cyanoHABs products. Challenges in cyanoHABs monitoring also include variability in ecosystems and accurate biomass estimations. Despite challenges, services like CyAN, CyanoTRACKER, EOLakeWatch, and CyanoKhoj have made significant strides in communicating and managing cyanoHABs risks. This review identifies key future research directions: (1) improving algorithmic approaches and accuracy, (2) defining a universal threshold for bloom formation, (3) utilizing emerging technologies and democratizing data and information, and (4) addressing satellite technique trade-offs in cyanoHABs analysis. By focusing on these areas and leveraging machine learning, future advancements promise more accurate and comprehensive monitoring to protect aquatic ecosystems and public health.