Video-Based Measurement of River Surface Velocity: A Review of Methods, Measurement Workflows, and Validation

Unmanned aerial vehicles (UAVs), smartphones and computer vision are expanding the use of non-contact approaches for measuring river surface velocity and estimating discharge. This review combines bibliometric analysis with a qualitative synthesis of video-based river hydrometry, with emphasis on sensing platforms, image-processing methods, validation procedures and sources of uncertainty. It covers image velocimetry, particle tracking velocimetry (PTV), space–time image velocimetry (STIV), optical flow and emerging deep-learning-based approaches, and relates them to UAV, smartphone and fixed-camera workflows. Rather than considering these elements separately, the review follows the complete measurement chain from image acquisition, calibration, geometric correction and stabilization to velocity estimation, validation and discharge computation. Attention is given to environmental, hydraulic and imaging factors that affect accuracy, including surface texture, tracer availability, illumination, reflections, flow regime, camera geometry, frame rate, spatial resolution, stabilization and georeferencing. The review emphasizes that video-based methods primarily provide surface velocity, whereas discharge estimation requires additional information on channel geometry, water level, bathymetry and the conversion from surface to depth-averaged velocity. The review highlights the need for conventional hydrometric validation, transparent uncertainty reporting, standardized acquisition and reporting procedures, benchmark datasets, robust optical flow and deep-learning methods, and multisensor integration to support reliable operational river monitoring.