Assessment of chlorophyll-a retrieval algorithms over Kakinada and Yanam turbid coastal waters along east coast of India using Sentinel-3A OLCI and Sentinel-2A MSI sensors

Phytoplankton Chlorophyll concentration (Chl-a) is a prominent indicator to understand the health and productivity of marine ecosystems that depend on the spatial distribution of Phytoplankton in the ocean waters. The spatial and temporal variability of Chl-a makes it difficult to monitor the coastal waters using in-situ data alone. This study aims to retrieve the Chl-a distribution in Yanam and Kakinada turbid coastal waters using Fluorescence Line Height (FLH) and Maximum Chlorophyll Index (MCI) algorithms from Sentinel-3 OLCI and Sentinel-2 MSI data. The FLH products show moderate correlation with retrieved and in-situ Chl-a data, whereas the MCI products show exceptionally good correlation with both the data. Assessment results show a better correlation between in-situ and retrieved Chl-a derived from Sentinel-3 and Sentinel-2 satellite data (R2 = 0.858; R2 = 0.883). The final results show that the S2MCI product gives the best results compared to S3MCI to retrieve Chl-a in the study area. Analysis results demonstrate that FLH algorithm and models are limited to the coastal area with Chl-a < 4 mg/m3, whereas MCI algorithm is recommended for Chl-a > 4 mg/m3. The S2MCI algorithm shows better performance with root mean square error (RMSE = 0.76) and mean absolute relative error (MARE = 1.14) for Chlorophyll retrieval compared with other algorithms. The MCI algorithm is more effectively used to retrieve the Chl-a in the study area. Moreover, this study also demonstrates the potential of Sentinel-3 OLCI and Sentinel-2 MSI satellite data for better observation and retrieval of Chl-a distribution over the turbid coastal waters.