Accurate measurement of river surface flow velocity is essential for water disaster prevention, water resources management, and hydrological research. While optical flow methods based on image velocimetry have garnered significant interest for their non-contact advantages, they remain prone to substantial estimation errors when applied to natural river environments characterized by complex surface motion patterns, pronounced non-rigid deformations, and multiscale nonlinear flow dynamics. To address these challenges, this study proposes a multi-scale local–global deep optical flow network (MSKFlow) for river surface velocity measurement via drone video imagery, aiming to improve measurement accuracy in complex water surface environments. The framework first introduces a joint feature extraction strategy that integrates multi-scale convolution with the Swin Transformer, which effectively enhances feature representation and matching capabilities under complex flow field conditions. Moreover, a superkernel updater is employed to replace the conventional convolutional gated recurrent unit, significantly improving optical flow refinement in complex flow field structures. Experimental results based on a synthetic river dataset demonstrate that MSKFlow can accurately reconstruct flow field structures under controlled conditions. Field validation using drone-recorded videos from two reaches of the Heihe River Basin shows that, when benchmarked against current meter measurements, the estimated surface flow velocities yield average relative errors of 14.4% and 15.2%, respectively. These results confirm the reliability and stability of the proposed method under natural river surface conditions. Overall, MSKFlow offers an efficient and promising solution for drone-based remote sensing monitoring of river surface flow velocity in complex environmental settings.
Zhang et al. (Wed,) studied this question.