Spectrally based remote sensing of river bathymetry

Abstract This paper evaluates the potential for remote mapping of river bathymetry by (1) examining the theoretical basis of a simple, ratio‐based technique for retrieving depth information from passive optical image data; (2) performing radiative transfer simulations to quantify the effects of suspended sediment concentration, bottom reflectance, and water surface state; (3) assessing the accuracy of spectrally based depth retrieval under field conditions via ground‐based reflectance measurements; and (4) producing bathymetric maps for a pair of gravel‐bed rivers from hyperspectral image data. Consideration of the relative magnitudes of various radiance components allowed us to define the range of conditions under which spectrally based depth retrieval is appropriate: the remotely sensed signal must be dominated by bottom‐reflected radiance. We developed a simple algorithm, called optimal band ratio analysis (OBRA), for identifying pairs of wavelengths for which this critical assumption is valid and which yield strong, linear relationships between an image‐derived quantity X and flow depth d . OBRA of simulated spectra indicated that water column optical properties were accounted for by a shorter‐wavelength numerator band sensitive to scattering by suspended sediment while depth information was provided by a longer‐wavelength denominator band subject to strong absorption by pure water. Field spectra suggested that bottom reflectance was fairly homogeneous, isolating the effect of depth, and that radiance measured above the water surface was primarily reflected from the bottom, not the water column. OBRA of these data, 28% of which were collected during a period of high turbidity, yielded strong X versus d relations ( R 2 from 0·792 to 0·976), demonstrating that accurate depth retrieval is feasible under field conditions. Moreover, application of OBRA to hyperspectral image data resulted in spatially coherent, hydraulically reasonable bathymetric maps, though negative depth estimates occurred along channel margins where pixels were mixed. This study indicates that passive optical remote sensing could become a viable tool for measuring river bathymetry. Copyright © 2009 John Wiley & Sons, Ltd.