Remote sensing could facilitate efficient characterization of river systems for research and management purposes, provided that suitable image data are available and that the information derived therefrom is reliable. This study evaluated the utility of public domain multispectral images for estimating flow depths in a small stream and a larger gravel-bed river, using data acquired through a task-oriented consortium and the National Agricultural Imagery Program (NAIP). Field measurements were used to calibrate image-derived quantities to observed depths and to assess depth retrieval accuracy. A band ratio-based algorithm yielded coherent, hydraulically reasonable bathymetric maps for both field sites and three different types of image data. Applying a spatial filter reduced image noise and improved depth retrieval performance, with a strong calibration relationship (R2 = 0.68) and an observed (field-surveyed) versus predicted (image-derived) R2 of 0.6 for tasked images of the smaller stream. The NAIP data were less useful in this environment because of geo-referencing errors and a coarser spatial resolution. On the larger river, NAIP-derived bathymetry was more accurate, with an observed versus predicted R2 value of 0.64 for a compressed county mosaic easily accessible via the Internet. Comparison of remotely sensed bathymetric maps with field surveys indicated that although the locations of pools were determined accurately, their full depth could not be detected because of limited sensor radiometric resolution. Although a number of other constraints also must be considered, such as the need for local calibration data, depth retrieval from publicly available image data is feasible under appropriate conditions. Copyright © 2012 John Wiley & Sons, Ltd.