Case II waters, including inland and inshore waters, are affected by many factors, such as phytoplankton, suspended particles, and colored dissolved organic matter, leading to complex and changeable optical characteristics of the water body. Hence, establishing a unified remote sensing quantitative estimation model for retrieving water environmental parameters is difficult. According to the optical characteristics of water, the method of water optical classification and water environmental parameter inversion can not only improve the accuracy of parameter estimation but also facilitate the model to be popularized in similar waters. This study aims to review the state-of-the-art concepts and methods of water optical classification on remote sensing technology for case II water monitoring. The classification-based applications on retrieving environmental parameters as well as the limitations and prospects are discussed.The criteria for considering studies for this review are based on the general development of water optical classification technology and ongoing studies from authors and their collaborators. The selection of studies is classified by different methods and applications for parameter retrieval. The main concept and advantages of water optical classification are illustrated with several examples presented in this study.According to the optical characteristics of water, the method of water optical classification and water environmental parameter inversion can not only improve the accuracy of parameter estimation but also facilitate the model to be popularized in similar waters. Water optical classification methods mainly include optical classification based on inherent optical characteristics, remote sensing reflectance waveform characteristics, and parameter inversion. The classification inversion strategy includes the fusion of classification and model algorithm, the optimization algorithm based on water optical type, and the hybrid calculation based on optimization of multi-model.Water optical classification is an effective tool for remotely recording the water quality and improving the estimation of the parameters especially in optically complex case II waters. The water retrieval of one predominated optical type should be based on its optimal model. However, accurate estimation of water composed of various types with spatiotemporal dynamics requires the determination of optimal models for each type and the blending strategy. The fuzzy-logic-based blending supports the production of seamless contiguities by considering weight factors. However, different classification methods and parameter estimation strategies should be reconsidered according to the complexity of water optical characteristics and research purposes.