Grain size data are important for studying and managing rivers, but they are difficult to obtain in the field. Therefore, methods have been developed that use images from small and remotely piloted aircraft. However, uncertainty in grain size data from such image-based products is understudied. Here we present a new way of uncertainty estimation that includes fully modeled errors. We use this technique to assess the effect of several image acquisition aspects on grain size uncertainty.