Performance of the Ocean Color Algorithms: QAA, GSM, and GIOP in Inland and Coastal Waters

The degradation of coastal waters and lakes is of a great concern due to their important roles in providing fresh water and food to the neighboring communities. Therefore, a robust monitoring plan is needed to assess the emerging water quality issues and identify the possible sources of pollution. Remote sensing could be an alternative invaluable approach for water quality monitoring compared to the traditional monitoring methods which are limited in terms of spatial coverage and temporal variability. The remote sensing–based water quality data can be estimated by modelling the apparent optical properties (AOPs) and/or the inherent optical properties (IOPs). Retrieving IOPs enables the estimation of aquatic biomass, primary production, and carbon pools. Therefore, several studies have invested significantly in improving the performance of the IOPs models to better estimate the water quality parameters. To assess uncertainty and improve IOP models in estimating water quality parameters, we review here studies of IOP modelling with a focus on coastal and inland waters. The review includes the most common IOP models: the GSM Semi-Analytical Bio-Optical Model, the Quasi-Analytical Algorithm (QAA), and the Generalized IOP Algorithm (GIOP). We review performance of these models and the regions in which they are applied in. Additionally, the limitations of the models are discussed and thus recommendations are proposed to overcome the uncertainties and incorporate better results from the models. This review could directly influence the science of future missions with the ability to monitor coastal and inland waters.