A comprehensive review of various environmental factors' roles in remote sensing techniques for assessing surface water quality.

The aim of this research was to evaluate the existing remote sensing (RS) products, various tools and techniques, and their limitations in retrieving the optically active (OA) Chlorophyll-a (CHL) concentration from transitional, coastal and inland waters. In recent decades, satellite RS technique has emerged as a vital tool for assessing surface water quality (WQ) in a cost-effective and timely manner. Initially used in the 1970s to study ocean color (OC), RS techniques have advanced significantly, enabling the retrieval of key WQ indicators like CHL, colored dissolved organic matter (CDOM), total suspended matter (TSM), turbidity (TURB), and more from satellite images. Among these indicators, CHL is particularly important as it directly signifies eutrophication. While RS technique has been reliable in estimating CHL concentrations in open waterbodies (case1 water) such as oceans, it's application in shallow, turbid waters (case2 water) like transitional, coastal and inland areas faces challenges. Interference from other OA-WQ indicators like CDOM and TSM, coupled with environmental factors such as atmospheric components, sun-glint, and adjacency effects (AE), complicate the accurate CHL estimation. To address these challenges, researchers have developed four categories of CHL retrieval algorithms: empirical, semi-empirical, hybrid and data-driven models. Empirical and data-driven methods are straightforward but require regional calibration for accuracy, whereas semi-empirical approaches, rooted in solid theoretical foundations, demand extensive ancillary optical measurements. To harness the potential of RS in WQ assessment fully, it is essential to optimize these algorithms regionally, tailoring them to the specific optical characteristics of diverse waterbodies. This optimization process is vital for integrating RS technique as a complementary data source alongside traditional monitoring approach. By addressing the impact of environmental factors and fine-tuning of CHL retrieval methods according to regional nuances, satellite RS technique can significantly enhance the reliability and effectiveness of surface WQ evaluation, thereby contributing to more informed and efficient water resource management strategies. This review emphasizes the impact of these factors, categorizes CHL retrieval algorithms into empirical, semi-empirical, hybrid and data-driven methods and applicability in terms of tools/models' reliability and challenges for the further advancement of this approaches for monitoring transitional, coastal and inland waters. To optimize the reliability of remotely sensed CHL data, regional configuration(s) of retrieving algorithms is vital. By addressing these challenges and tailoring methods to specific regions, integrating satellite RS into traditional monitoring approaches can significantly enhance surface WQ assessment.