A forward image model for passive optical remote sensing of river bathymetry

Legleiter, Carl; Roberts, Dar

By facilitating measurement of river channel morphology, remote sensing techniques could enable significantadvances in our understanding of fluvial systems. To realize this potential, researchers must first gainconfidence in image-derived river information, as well as an appreciation of its inherent limitations. Thispaper describes a forward image model (FIM) for examining the capabilities and constraints associated withpassive optical remote sensing of river bathymetry. Image data are simulated “from the streambed up” byfirst using information on depth and bottom reflectance to parameterize models of radiative transfer withinthe water column and atmosphere and then incorporating sensor technical specifications. This physics-basedframework provides a means of assessing the potential for spectrally-based depth retrieval from a particularriver of interest, given a sensor configuration. Forward image modeling of both a hypothetical meander bendand an actual gravel-bed river indicated that bathymetric accuracy and precision vary spatially as a functionof channel morphology, with less reliable depth estimates in pools. A simpler, more computationally efficientanalytical model highlighted additional controls on bathymetric uncertainty: optical depth and the ratio ofthe smallest detectable change in radiance to the bottom-reflected radiance. Application of the FIM to acomplex, natural channel illustrated how the model can be used to quantify the effects of various sensorcharacteristics. Bathymetric accuracy was determined primarily by spatial resolution, due to mixed pixelsalong the banks and sub-pixel scale variations in depth, whereas depth retrieval precision depended on thesensor's ability to resolve subtle changes in radiance. This flexible forward modeling approach thus allowsthe utility of image-derived river information to be evaluated in the context of specific investigations, leadingto more efficient, more informed use of remote sensing methods across a range of fluvial environments.

A forward image model for passive optical remote sensing of river bathymetry

Series