Desertification processes pose a global environmental threat, impacting 61 × 106 km2 of the terrestrial land area. Changes in overland flow patterns and consequent rainwater redistribution in drylands present a potential pathway to desertification, because vegetation often relies on water inputs from runoff to sustain growth under insufficient rainfall conditions. Of particular importance are the very shallow overland flows that redistribute water, nutrients, and biological matter within arid landscapes. However, characterizing overland flow patterns remains challenging, due to their very shallow depths, their distributed nature, and the poor understanding of how these flows interact with the underlying rough soil surface. This paper describes how coupling thermal images of shallow overland flows with light detection and ranging (LiDAR) scanning of the underlying soil surface in 1 m2 experimental trays allows spatial patterns of shallow overland flow to be quantified. Laboratory experiments were used to explore the behaviors of shallow overland flow as mean slope gradients and soil roughness were varied. The results show that these imaging techniques are able to capture differences in flow patterns arising across soil surfaces with varying slope, roughness, and spatial variation in infiltration properties. Several spatial indices characterizing overland flow patterns were found to correlate with runoff volume. The presence of high permeability soil patches substantially regulated overland flow. A next logical step would be to apply the thermal and LiDAR measurement techniques to the hillslope scale.