Remote Sensing of River Bathymetry: Evaluating a Range of Sensors, Platforms, and Algorithms on the Upper Sacramento River, California, USA Water Resources Research

Remote sensing has become an increasingly viable tool for characterizing fluvial systems. In this study, we used field measurements with a 1.6-km reach of the upper Sacramento River, CA, to evaluate the potential of mapping water depths with a range of platforms, sensors, and depth retrieval methods.Field measurements of water column optical properties also were compared to similar data sets from other rivers to provide context for our results. We considered field spectra, a multispectral satellite image, hyperspectral data collected from conventional and unmanned aircraft, and a bathymetric LiDAR andapplied a generalized version of Optimal Band Ratio Analysis and the Knearest neighbors regression machine learning algorithm. Linear, quadratic, exponential, power, and lowess Optimal Band Ratio Analysis models enabled flexible curve-fitting in calibrating spectrally based quantities to depth; anexponential formulation avoided artifacts associated with other model types. Knearest neighbors