Quantifying riverine surface currents from time sequences of thermal infrared imagery

River surface currents are quantified from thermal and visible band imagery using two methods. One method utilizes time stacks of pixel intensity to estimate the streamwise velocity at multiple locations. The other method uses particle image velocimetry to solve for optimal two‐dimensional pixel displacements between successive frames. Field validation was carried out on the Wolf River, a small coastal plain river near Landon, Mississippi, United States, on 26–27 May 2010 by collecting imagery in association with in situ velocities sampled using electromagnetic current meters deployed 0.1 m below the river surface. Comparisons are made between mean in situ velocities and image‐derived velocities from 23 thermal and 6 visible‐band image sequences (5 min length) during daylight and darkness conditions. The thermal signal was a small apparent temperature contrast induced by turbulent mixing of a thin layer of cooler water near the river surface with underlying warmer water. The visible‐band signal was foam on the water surface. For thermal imagery, streamwise velocities derived from the pixel time stack and particle image velocimetry technique were generally highly correlated to mean streamwise current meter velocities during darkness (r2 typically greater than 0.9) and early morning daylight (r2 typically greater than 0.83). Streamwise velocities from the pixel time stack technique had high correlation for visible‐band imagery during early morning daylight hours with respect to mean current meter velocities (r2 > 0.86). Streamwise velocities for the particle image velocimetry technique for visible‐band imagery had weaker correlations with only three out of six correlations performed having an r2 exceeding 0.6. Key Points Thermal infrared images can be used to quantify surface velocity Optical current meter approach was more robust Re‐radiation and reflections into imagery degrades velocity techniques