Sea state estimation for ship operations: A systematic review of methods, datasets, and challenges

Accurate sea state estimation is essential for ensuring maritime safety, supporting ship operations, and mitigating environmental risks. Despite the progress from traditional physics-based models to advanced data-driven and hybrid approaches, a structured synthesis and comparative analysis of their underlying assumptions, reported performance, and domain applicability across the existing literature remains limited. To address this issue, this study conducts a systematic review following the PRISMA guidelines, analyzing 77 peer-reviewed papers recently published. A novel taxonomy is proposed, categorizing sea state estimation methods into four classes: wind-based, in-situ measurement-based, ship motion-based, and radar-based approaches. A comprehensive multi-dimensional analysis was further conducted to identify research patterns across regions, funding source, sea areas, wave characteristics, sea state classification, datasets, modeling methods, and evaluation metrics. The results reveal strong geographical bias (nearly 70% of studies from Europe), reliance on non-standardized datasets, and limited focus on complex wave dynamics. Ship motion-based and in-situ measurement-based methods dominate current research. Accurate models for coastal and harbor regions remain lacking. Based on these results, a forward-looking research roadmap is developed. It emphasizes the need to establish open data infrastructures, develop physics-informed learning frameworks, and design lightweight deployable models for real-time applications.