Scientometric Trends in Harmful Algal Bloom Prediction: Integrating Optical Remote Sensing and Mathematical Models (2000-2024)

Harmful Algal Blooms (HABs) pose a significant threat to the environment, economy, and public health, producing toxins, reducing biodiversity, causing decomposition, and, in some cases, generating vector-borne diseases. The early detection of HABs using predictive models remains a challenge. This study fills a gap in the literature by performing a scientometric analysis of 155 unique scientific articles published between 2000 and 2024, focusing on the prediction of HABs using mathematical models in conjunction with optical detection methods. The articles were obtained from Scopus and Web of Science and unified using a semi-automated process that included text mining and DOI-based web scraping with CrossRef API. The search strategy was structured into three thematic axes: Harmful Algal Blooms (HABs), mathematical modelling, and optical detection methods. Using tools such as Bibliometrix and ScientoPy, the analysis revealed a 29.20% increase in publications between 2018 and 2023, indicating growing scientific interest. The United States led research productivity (36.69%) and citations (51.1%). Three main research trends were identified: authorship patterns, national-level contributions, and emerging issues in the field. These results underscore the increasing integration of remote sensing and predictive modelling in HAB monitoring, highlighting areas for future research.