Study of skin drag coefficients of green tide patches in the Yellow Sea using UAV images

Green tides caused by Ulva prolifera blooms have been a severe ecological issue in the Yellow Sea since 2008, with their drift trajectory prediction critical for disaster warning and mitigation. However, satellite monitoring faces limitations due to long revisit periods, cloud cover, and the inability to synchronously measure surface currents near algal patches, restricting accurate understanding of their drift mechanisms. This study employed unmanned aerial vehicles (UAVs) to conduct high-resolution monitoring of green tide patches off the coast of Haiyang, Shandong Province. By tracking 12 algal patches using UAV imagery, measuring surface currents with floating buoys, and recording wind speed/direction simultaneously, we derived the ratio of skin drag coefficients (β) between wind and water using momentum equations and numerical solutions (via Python’s SciPy library). Results showed that the drift directions of algal patches were highly consistent with surface currents (maximum deviation 10°), with β ranging from 0.0051 to 0.038 (mean 0.018). This indicates that surface currents play a significantly stronger role in driving green tide drift than wind. The study validated the high precision of UAV monitoring in measuring green tide drift speed, providing critical parameters for optimizing green tide trajectory prediction models.