Phytoplankton are fundamental to sustaining marine ecosystems and significantly in-fluence the global carbon cycle. However, to indetify their types accurately from sat-ellite imagery remains a challenge. This study presents machine learning approaches for classifying phytoplankton types, including coccolithophores, diatoms, and dino-flagellates, using Second-generation Global Imager (SGLI) imagery aboard the GCOM-C satellite. Several algorithms were evaluated, with Random Forest (RF) and Gradient Tree Boosting (GTB) achieving the highest classification performance. To assess model transferability, the developed machine learning models were applied in another sub-region and on a different date of acquisition. The validation confirmed the ability of the model to generalize across sub-region and temporal variations in SGLI imagery. As a result, the potential of combined machine learning and SGLI im-agery can improve phytoplankton detection, enabling large-scale monitoring at both regional and global levels. This paper highlights the importance of combining artifi-cial intelligence with satellite-derived ocean color data to improve the monitoring of marine ecosystems.

Phytoplankton are fundamental to sustaining marine ecosystems and significantly influence the global carbon cycle. However, identifying their types accurately from satellite imagery remains a challenge. This study presents machine learning approaches for classifying phytoplankton types, including coccolithophores, diatoms, and dinoflagellates, using Second-generation Global Imager (SGLI) imagery aboard the GCOM-C satellite. Several algorithms were evaluated, with Random Forest (RF) and Gradient Tree Boosting (GTB) achieving the highest classification performance in classifying coccolitophores and diatoms. On the other hand, both RF and Classification and Regression Trees (CARTs) are effective for distinguishing dinoflagellates from surrounding water types. To assess model transferability, the developed machine learning models were applied in another sub-regions and on a different date of acquisition. The validation confirmed the ability of the model to generalize across sub-region and temporal variations in SGLI imagery. As a result, the potential of combined machine learning and SGLI imagery can improve phytoplankton detection, enabling large-scale monitoring at both regional and global levels. This paper highlights the importance of combining artificial intelligence with satellite-derived ocean color data to improve the monitoring of marine ecosystems.

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