A preliminary assessment of Sentinel-3 ocean and land color instrument data for the estimation of chlorophyll-a concentration using bio-optical methods in Annaba Bay and El Kala’s coast (Algerian Basin)

The use of bio-optical methods and high spatial and spectral resolution satellite sensors has enabled the estimation of chlorophyll-a (Chl-a) on a large spatiotemporal scale. However, their application on a regional scale necessitates validation and, in certain cases, calibration, especially when it comes to coastal waters. This study was carried out in the eastern Algerian coastal waters to evaluate the relevance of Sentinel-3 satellite data in Annaba Bay and El Kala’s coast. Sentinel-3 OLCI (Ocean and Land Color Instrument) Level-2 products (OC4Me and Chl-a NN) and the Chl-a generated by applying the C2RCC processor on Level-1B (Chl-a C2RCC) were compared to the measured Chl-a at fixed locations. In addition, nine other Chl-a algorithms have been tested. Finally, six of those later were locally calibrated. The results demonstrate the good performance of the initially available products (Chl-a C2RCC and Chl-a NN), followed by the blue–green algorithms (OC4, OC5, OC6, MedOC4, and ADOC4), whereas the near-infrared (NIR) algorithms (2B-OLCI, 3B-OLCI, and G2B) show underwhelming performance and very poor prediction of the Chl-a concentration. Even though the OC4Me has the best correlation (r = 0.9820), it also has the highest MAE, RMSD, and BIAS (0.628, 1.264, and 0.5696, respectively). The fit of the NIR methods has been significantly refined. Nevertheless, their performance is still inadequate for the estimation of the Chl-a in our region. The performance of the calibrated OCx was improved, with the OC6-AW at the top of the list (r = 0.9881, MAE = 0.08). The latter outperforms the existing ones, which improves the estimation of Chl-a based on the Sentinel-3 OLCI data in our study area.