Optimized detection of submesoscale chlorophyll-a fronts from geostationary ocean color satellite GK-2B/GOCI-II imagery

Ocean fronts regulate ocean circulation, climate, and ecosystems, yet most satellite-based studies have concentrated on mesoscale thermal fronts from SST, leaving biological submesoscale fronts largely unexplored. Here we develop and apply an optimized algorithm to detect submesoscale chlorophyll-a (Chl-a) fronts using high-resolution data from the geostationary ocean color satellite GK-2B/GOCI-II (250 m, hourly). Our method combines a histogram-based detection framework with an optimal window size and a decorrelation length scale, ensuring spatial continuity while minimizing the discontinuities common in conventional approaches. This optimization enables robust identification of fine, complex, and short-lived frontal structures that SST-based methods cannot resolve. The detected fronts capture diurnal variability and reveal submesoscale features that are critical for linking physical dynamics with biological responses. We further present the occurrence probability map of Chl-a fronts, demonstrating their distinct spatial distributions. These results establish the potential of high-resolution geostationary ocean color data to advance the study of biological fronts and provide new opportunities for understanding ocean–ecosystem interactions.