Remote sensing has emerged as a powerful method of characterizing river systems but is subject to several important limitations. This study focused on defining the limits of spectrally based mapping in a large river. We used multibeam echosounder (MBES) surveys and hyperspectral images from a deep, clear-flowing channel to develop techniques for inferring the maximum detectable depth, dmax , directly from an image and identifying optically deep areas that exceed dmax . Optimal Band Ratio Analysis (OBRA) of progressively truncated subsets of the calibration data provided an estimate of dmax by indicating when depth retrieval performance began to deteriorate due to the presence of depths greater than the sensor could detect. We then partitioned the calibration data into shallow and optically deep ( d>dmax ) classes and fit a logistic regression model to estimate the probability of optically deep water, Pr(OD) . Applying a Pr(OD) threshold value allowed us to delineate optically deep areas and thus only attempt depth retrieval in relatively shallow locations. For the Kootenai River, dmax reached as high as 9.5 m at one site, with accurate depth retrieval ( R2=0.94 ) in areas with d<dmax . As a first step toward scaling up from short reaches to long river segments, we evaluated the portability of depth-reflectance relations calibrated at one site to other sites along the river. This analysis highlighted the importance of calibration data spanning a broad range of depths. Due to the inherent limitations of passive optical depth retrieval in large rivers, a hybrid field- and remote sensing-based approach would be required to obtain complete bathymetric coverage.