Global submesoscale eddy identification and characteristic analysis based on multi-source remote sensing data

Abstract. Research on oceanic submesoscale eddies has long been limited by the spatiotemporal resolution constraints of satellite altimeters. Relevant studies remain insufficient and have mostly focused on regional waters. Global investigations on submesoscale eddy detection, dataset construction, and distribution characteristic analysis based on multi-source remote sensing data and multiple methods are still lacking. In this study, we first develop a submesoscale eddy detection method by integrating high spatiotemporal resolution ocean color data, deep learning algorithms, and digital image processing techniques and construct a global submesoscale eddy dataset based on chlorophyll-a observations. Furthermore, we design a multi-scale eddy detection framework using altimeter data and establish two global datasets: a submesoscale eddy dataset derived from SWOT satellite altimeter measurements and a multi-scale eddy dataset generated from merged altimeter data. Finally, we statistically compare the scale, seasonal, and geographical distribution characteristics of eddies from the three constructed datasets and systematically analyze the strengths and limitations of different data sources and algorithms for submesoscale eddy detection. This study effectively compensates for the scarcity of existing submesoscale eddy datasets, provides a valid verification approach for conventional eddy products, and offers a feasible guideline for data selection in diverse submesoscale eddy research scenarios.