Defining the limits of spectrally based bathymetric mapping on a large river

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