Extended empirical orthogonal function analysis of spatiotemporal chlorophyll-a variability in Indonesian waters using remote sensing data

Chlorophyll-a (Chl-a) variability in Indonesian waters is influenced by a combination of oceanographic and climatic processes operating across multiple spatial and temporal scales, making it an important indicator of marine ecosystem productivity and environmental change. This study investigates the multiscale spatiotemporal variability of Chl-a and its relationships with major ocean–climate drivers. Monthly satellite-derived Chl-a data from 2015 to 2024 were analyzed using Extended Empirical Orthogonal Function (EEOF) analysis to identify dominant modes and their temporal evolution. The total variance explained by the first four EEOF modes was 44.20%. EEOF1 and EEOF2 accounted for 33.76% of the variance and primarily represented the seasonal cycle associated with monsoon-driven upwelling, whereas EEOF3 and EEOF4 explained 10.44% and were associated with interannual variability related to the El Niño–Southern Oscillation (ENSO). The two mode pairs indicate that Chl-a variability results from seasonal monsoon forcing, basin-scale climate variability, and regional oceanographic conditions. The reconstructed anomaly fields and observed anomaly fields resemble each other closely, which means that the EEOF method effectively separates overlapping signals over a large range of time scales. It also mitigates edge effects. It, therefore, will be useful for ecosystem monitoring, climate-informed fisheries forecasting, and adaptive marine resource management.