Identifying algal bloom types and analyzing their diurnal variations using GOCI-Ⅱ data

Abstract

Frequent algal blooms pose a serious threat to the marine ecosystem of the East China Sea. The Geostationary Ocean Color Imager-Ⅱ (GOCI-Ⅱ), a second-generation geostationary satellite sensor, is crucial for monitoring marine environmental dynamics. To evaluate the potential of GOCI-II for identifying and monitoring the diurnal variation of algal blooms in the East China Sea, we combined a coupled ocean–atmosphere model with the eXtreme Gradient Boosting (XGBoost) method to develop an atmospheric correction algorithm for coastal waters (XGB-CW). Validation showed that this algorithm derived remote sensing reflectance (Rrs) from GOCI-Ⅱ with higher accuracy than those provided by the National Ocean Satellite Center of South Korea (NOSC). To further evaluate GOCI-Ⅱ’s potential for algal bloom types identification, we compared three identification algorithms’ (Bloom Index (BI), Diatom Index (DI), and Rslope) results with Rrs data derived by XGB-CW. And the BI algorithm performed best in distinguishing the diatoms and dinoflagellates blooms, while Rslope was effective under high biomass conditions. The DI algorithm was good for diatoms blooms but less effective for dinoflagellates. Using Photosynthetically Available Radiation (PAR) and Sea Surface Temperature (SST) data, we analyzed the influence of these factors on the daily variations and characteristics of Akashiwo sanguinea (Dinoflagellate) and Chaetoceros curvisetus (Diatom). The results showed more pronounced daily variations in A. sanguinea compared to C. curvisetus. GOCI-Ⅱ, combined with accurate atmospheric correction and identification algorithms, plays a crucial role in algal bloom monitoring.