With the decline of operational river gauges monitoring sediments, a viable means of quantifying sediment transport is needed. In this study, we address this issue by applying relationships between hydraulic geometry of river channels, water discharge, water-leaving surface reflectance (SR), and suspended sediment concentration (SSC) to quantify sediment discharge with the aid of space-based observations. We examined 5490 Landsat scenes to estimate water discharge, SSC, and sediment discharge

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... r the period from 1984 to 2017 at nine gauging sites along the Upper Mississippi River. We used recent advances in remote sensing of fluvial systems, such as automated river width extraction, Bayesian discharge inference with at-many-stations hydraulic geometry (AMHG), and SSC-SR regression models. With 621 Landsat scenes available from all the gauging sites, the results showed that the water discharge and SSC retrieval from Landsat imagery can yield reasonable sediment discharge estimates along the Upper Mississippi River. An overall relative bias of −25.4, mean absolute error (MAE) of 6.24 × 104 tonne/day, relative root mean square error (RRMSE) of 1.21, and Nash–Sutcliffe Efficiency (NSE) of 0.49 were obtained for the sediment discharge estimation. Based on these statistical metrics, we identified three of the nine gauging sites (St. Louis, MO; Chester, IL; and Thebes, IL), which were in the downstream portion of the river, to be the best locations for estimating water and sediment discharge using Landsat imagery.