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.

Increased tourism in Belize over the last decade and the growth of the local population have led to coastal development and infrastructure expansion. Land use alteration and anthropogenic activity may change the sediment and nutrient loads in coastal systems, which can negatively affect ecosystems via mechanisms such as reducing photosynthetically active radiation fields, smothering sessile habitats, and stimulating eutrophication events. Accurate monitoring and prediction of water quality parameters such as Total Suspended Sediments (TSS), are essential in order to understand the influence of land-based changes, climate, and human activities on the coastal systems and devise strategies to mitigate negative impacts. This study implements machine learning algorithms such as Random Forests (RF), Extreme Gradient Boosting (XGB), and Deep Neural Networks (DNN) to estimate TSS using Sentinel-2 reflectance data in the Belize Coastal Lagoon (BCL) and validates the results using TSS data collected in situ. DNN performed the best and estimated TSS with a testing R² of 0.89. Time-series analysis was also performed on the BCL’s TSS trends using Bayesian Changepoint Detection (BCD) methods to flag anomalously high TSS spatio-temporally, which may be caused by dredging events. Having such a framework can ease the near-real-time monitoring of water quality in Belize, help track the TSS dynamics for anomalies, and aid in meeting and maintaining the sustainable goals for Belize.

Environmental remote sensing (ERS) has been a cornerstone technology for decades, allowing for comprehensive monitoring and management of ecosystems. Recent advancements in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have transformed the field of ERS by offering robust, data-driven solutions for analyzing complex, large-scale remote sensing datasets and answering important scientific questions. This intervention explores the diverse applications of AI in ERS, focusing on key domains such as water, agriculture, urban, and wetland monitoring. This work also includes discussions on crucial aspects such as the impact of foundation models in ERS and outlines key points on the ethical and responsible use of AI in ERS. The intervention is concluded by identifying promising next steps for future research.

Cyanobacterial harmful algal blooms (CyanoHABs) pose global risks to public health, ecosystems, and economies. Despite advancements in satellite remote sensing, monitoring gaps persist, particularly in smaller, remote, and resource-limited regions, where CyanoHABs often remain undetected. Satellite-based methods, though effective for large-scale monitoring, suffer from a low spatial resolution, cloud cover, and reliance on in situ validation data. Traditional in situ monitoring equipment, including high-precision spectroradiometers, is costly and logistically challenging, further exacerbating global monitoring inequities. Cyanosense 2.0 (CS2.0) is a low-cost system for real-time in situ CyanoHAB detection and satellite validation designed to address these monitoring voids. CS2.0 integrates two hyperspectral Hamamatsu spectrometers and a microcontroller system, recording Remote Sensing Reflectance (R_rs) with high agreement to industry-grade instruments (R ² = 0.86, Normalized Root Mean Squared Error (NRMSE) = 9.82%), at a fraction of the cost (∼$1300). During field validation in multiple CyanoHAB-prone U.S. lakes, CS2.0 showed a strong performance when tested for widely used satellite-based CyanoHAB models and indices (R ² = 0.74–0.83; NRMSE = 13%–18%). The system’s weatherproof design supports long-term autonomous deployments, making it functional in remote environments. As a scalable and accessible solution, CS2.0 holds the potential to democratize CyanoHAB monitoring and improve global water quality assessments, especially in under-represented regions.