A review of remote sensing-based water quality monitoring in turbid coastal waters

Abstract Monitoring water quality in turbid coastal waters is critical for ecological security, yet traditional sampling methods are often inefficient and spatially limited. Remote sensing, with its multiscale coverage and real-time capabilities, has emerged as a pivotal tool. However, the complex optical properties of turbid coastal waters, such as the strong absorption-scattering coupling effects of suspended particulate matter (SPM), chlorophyll- a (Chl -a C h l - a), and colored dissolved organic matter (CDOM), pose significant challenges, including spectral signal saturation and poor algorithm generalizability. Current inversion approaches include: (1) physics-based models, such as the quasi-analytical algorithm (QAA) grounded in radiative transfer theory, which perform well in moderately turbid waters but are error-prone in highly turbid environments due to multiple scattering; (2) semi-empirical models, which leverage spectral band ratios for computational efficiency but suffer from limited regional adaptability; and (3) data-driven methods, including machine learning and deep learning, which improve accuracy via nonlinear mapping but face limitations in interpretability and robustness, particularly under extreme environmental conditions. Future efforts should prioritize multisource data fusion, integrating satellite, unmanned aerial vehicle (UAV), and in situ observations; physics-guided deep learning; hyperspectral technology optimization; and the development of standardized algorithm libraries. These advancements will enhance the accuracy, transferability, and operational feasibility of remote sensing-based water quality monitoring and contribute to more effective and sustainable marine governance.