Abstract
Based on centimeter-level ultra-high-resolution UAV images, the drifting velocity of floating macroalgae are quantified. In this study, a Large Scale Particle Image Velocimetry (LSPIV) method is used for analyzing the drift of floating macroalgae in high-resolution Red-Green-Blue (RGB) images and videos collected from an unmanned aerial vehicles (UAVs) of hovering mode. The method employs Maximum Cross-Correlation (MCC) and Iterative Multigrid Approach (IMA) to achieve high spatial resolution and wide range of velocity gradient. Utilizing floating macroalgae as natural tracers, we enhance tracer signals using the Red-Green band virtual baseline Floating green Algae Height (RGFAH) index. Subsequently, LSPIV and a deep learning U-Net model are employed to acquire high spatiotemporal resolution information regarding the distribution and drift velocity of floating macroalgae. We then establish comprehensive instantaneous and time-averaged flow fields of floating macroalgae. Utilizing input data derived from alterations in water surface grayscale due to natural tracers such as water surface bubbles, suspended sediment, waves, and sun glint, we analyze the periodic variations in the sea surface velocity and wave intensity correlation based on statistical and Fourier analysis in instantaneous and time-averaged flow fields. 
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