Remote sensing of suspended sediment concentration during turbid flood conditions on the Feather River, California—A modeling approach

Abstract

Direct measurements of suspended sediment concentration (SSC) in rivers are surprisingly sparse. We present an approach for measuring these concentrations from space, tailored to fit rivers with limited records of flood‐level SSC. Our approach requires knowledge of a typical particle‐size distribution of sediment suspended during floods, the dominant mineralogy, and a calibration consisting of above‐water reflectance field spectra with known SSC. Surface SSC values were derived for two Landsat images covering 70 km of the Feather and portions of the Sacramento, Yuba, and Bear Rivers in California in order to capture conditions during a large flood event. Using optical theory and radiative transfer modeling we modeled remote‐sensing reflectance (Rrs) for a number of three‐component mixtures composed of color dissolved organic matter (CDOM), water, and montmorillonite particles. We then iteratively estimated CDOM by fitting modeled spectra for a range of absorption coefficients to field‐measured spectra collected from the Sacramento River and matched to measured SSC values. Spectral mixture analysis with a two‐end‐member model yielded end‐member fractions and SSC via a look‐up table specific to the Landsat sensor. Model closure was within the error of measured SSC values, suggesting that this approach is promising for deriving SSC on rivers during flood conditions when empirical relationships established between low SSC values and Rrs are no longer valid.