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

• Published in: Water resources research 2019-03, Vol.55 (3), p.2142-2169
• Main Authors: Legleiter, Carl J., Harrison, Lee R.
• Format: Article
• Language: English
• Subjects: Algorithms; Bathymeters; bathymetric LiDAR; bathymetric mapping; Bathymetry; Curve fitting; Data; Depth; Detection; hyperspectral; Learning algorithms; Lidar; Machine learning; Mapping; Mathematical models; Measurement methods; Optical properties; Platforms; Regression analysis; Remote sensing; remote sensing of rivers; Remote sensors; Retrieval; Rivers; satellite image data; Satellite imagery; Satellites; Scaling; Sensors; Shallow water; Surveys; Unmanned aerial vehicles; Unmanned aircraft; unmanned aircraft system (UAS); Water circulation; Water column; Water depth
• ISSN: 0043-1397; 1944-7973
• DOI: 10.1029/2018WR023586

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 and applied a generalized version of Optimal Band Ratio Analysis and the K nearest 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; an exponential formulation avoided artifacts associated with other model types. K nearest neighbors regression increased observed versus predicted (OP) R2 values, particularly for the satellite image; we also found that preprocessing of satellite images was unnecessary and that a basic data product could be used for depth retrieval. Bathymetric LiDAR was highly accurate and precise in shallow water, but a lack of bottom returns from areas greater than 2 m deep resulted in large gaps in coverage. The maximum detectable depth imposes an important constraint on fluvial remote sensing and a hybrid approach combined with field surveys of deep areas might be a more realistic operational strategy for bathymetric mapping. Future work will focus on scaling up from short reaches to long river segments. Key Points Accurate depths were retrieved from satellite images, hyperspectral data acquired from manned and unmanned aircraft, and a bathymetric LiDAR Generalized Optimal Band Ratio Analysis allows flexible calibration of spectral quantities to depth; exponential model avoids artifacts The maximum detectable depth is a key constraint on fluvial remote sensing, particularly for LiDAR due to a lack of bottom returns >2 m