Water color remote sensing models: Inversion mechanisms, optimization, and challenges

Water color remote sensing plays a vital role in monitoring aquatic environments, yet significant challenges remain in model applicability and data integration. This review presents a comprehensive bibliometric analysis of 577 WCRS-related articles indexed in the Web of Science Core Collection, identifying research hotspots, international collaborations, and model evolution trends. The analysis reveals that China leads in publication count, while the United States holds a dominant position in citation impact and international collaboration, highlighting a gap between research output and influence. Keyword co-occurrence mapping shows that bio-optical modeling, eutrophication monitoring, and high-resolution sensing in optically complex waters are key research frontiers. The main types of models such as empirical, analytical, semi-analytical, and machine learning. Are systematically categorized and assessed based on their application scope, limitations, and optimization strategies through representative case studies. Persistent challenges remain, particularly in data quality, algorithm generalization, and the fusion of multi-source data. To address these issues, five future research directions are proposed, including the development of physics-informed AI models and creation of global data-sharing platforms. This work provides both a knowledge map and practical guidance to advance WCRS theory and applications under increasingly complex water and aquatic conditions. • Analyzed 577 water color remote sensing-related publications to uncover research trends and emerging hotspots. • Compared analytical, empirical, and semi-analytical models. • Analyzed the current problems of water color remote sensing and proposed solutions.