Remote sensing could enable high-resolution mapping of long river segments, but realizing this potential will require new methods for inferring channel bathymetry from passive optical image data without using field measurements for calibration. As an alternative to regression-based approaches, this study introduces a novel framework for Flow REsistance Equation-Based Imaging of River Depths (FREEBIRD). This technique allows for depth retrieval in the absence of field data by linking a linear relation between an image-derived quantity X and depth d to basic equations of open channel flow: continuity and flow resistance. One FREEBIRD algorithm takes as input an estimate of the channel aspect (width/depth) ratio A and a series of cross-sections extracted from the image and returns the coefficients of the X versus d relation. A second algorithm calibrates this relation so as to match a known discharge Q. As an initial test of FREEBIRD, these procedures were applied to panchromatic satellite imagery and publicly available aerial photography of a clear-flowing gravel-bed river. Accuracy assessment based on independent field surveys indicated that depth retrieval performance was comparable to that achieved by direct, field-based calibration methods. Sensitivity analyses suggested that FREEBIRD output was not heavily influenced by misspecification of A or Q, or by selection of other input parameters. By eliminating the need for simultaneous field data collection, these methods create new possibilities for large-scale river monitoring and analysis of channel change, subject to the important caveat that the underlying relationship between X and d must be reasonably strong.