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Chlorophyll-a concentration (Chl-a) is a crucial parameter for monitoring the water quality in coastal waters. The principal aim of this study is to evaluate the performance of existing Chl-a band ratio inversion models for estimating Chl-a from Sentinel2-MSI and Sentinel3-OLCI observation. This was performed using an extensive in situ Rrs-Chl-a dataset covering contrasted coastal waters (N = 1244, Chl-a (0.03–555.99) µg/L), which has been clustered into five optical water types (OWTs). Our results show that the blue/green inversion models are suitable to derive Chl-a over clear to medium turbid waters (OWTs 1, 2, and 3) while red/NIR models are adapted to retrieve Chl-a in turbid/high-Chl-a environments. As they exhibited the optimal performance considering these two groups of OWTs, MuBR (multiple band ratio) and NDCI (Normalized Difference Chlorophyll-a Index)-based models were merged using the probability values of the defined OWTs as the blending coefficients. Such a combination provides a reliable Chl-a prediction over the vast majority of the global coastal turbid waters (94%), as evidenced by a good performance on the validation dataset (e.g., MAPD = 21.64%). However, our study further illustrated that none of the evaluated algorithms yield satisfying Chl-a estimates in ultra-turbid waters, which are mainly associated with turbid river plumes (OWT 5). This finding highlights the limitation of multispectral ocean color observation in such optically extreme environments and also implies the interest to better explore hyperspectral Rrs information to predict Chl-a.
Tran et al. (Sat,) studied this question.