Since the 1970s, remotely acquired optical imagery has been used to quantify surface sediment concentrations in sufficiently large water bodies [see reviews by Mertes et al., 2004; Ritchie et al., 2003a]. Such methods show promise for obtaining sediment concentrations and fluxes in rivers lacking direct measurements of suspended load, potentially providing valuable information on the role of floods in transporting sediment. With the present suite of satellites carrying optical instruments, image analysis could be used to regularly monitor suspended sediment transport conditions during large floods when high sediment concentrations and hazardous conditions otherwise inhibit sampling. Furthermore, imagery provides a synoptic assessment of the river system—a unique benefit that allows for both the setting of initial conditions and/or validation of suspended sediment transport predicted by multidimensional numerical models [Dekker et al., 2001; Ouillon et al., 2004].
 Regularly quantifying suspended sediment concentrations in large rivers with consistent methods would highlight seasonal and interannual variations, and mapping the spatial distribution of sediment loading could provide information on sediment sources. Both temporal and spatial trends are relevant for understanding large-scale biogeochemical cycling—especially of carbon as river discharge provides the primary pathway for carbon burial in oceans, contributing an estimated 6 × 1012 kg of sediment to the world's oceans each year [Milliman and Meade, 1983]. Furthermore, suspended sediment delivery to floodplains counteracts lateral erosion due to channel migration, contributing to floodplain maintenance and intrabasin storage of associated nutrients and contaminants.
 Although remote sensing is used widely to monitor oceans and lakes, its application to river systems is hampered by two crucial difficulties. First, image-derived measurements represent the concentration of suspended sediment within a surface layer—thus necessitating conversion to a depth-integrated load before comparison to numerical models or field measurements. Second, most methods have relied upon empirical relationships between sediment concentrations and the water-leaving reflectance captured by the remote sensing instrument [Ritchie et al., 2003a; Ruhl et al., 2001; Whitlock et al., 1981]. Empirical calibrations using remotely sensed or field-derived reflectance data provide site-specific predictions of water quality parameters with reasonable accuracy [e.g., Ritchie et al., 2003b; Ruhl et al., 2001; Whitlock et al., 1981], but are limited in their universal application and may not extend to the full range of conditions present in inland waters [Schiebe et al., 1992]. Although several workers have identified a linear relationship between suspended sediment concentration (SSC) and reflectance within a concentration range of 0 to 50 mg L−1 [Munday and Alfoldi, 1979; Ritchie et al., 2003b], an exponential relationship clearly exists at higher concentrations [Curran and Novo, 1988; Holyer, 1978; Schiebe et al., 1992]. Thus calibrations established during lower flows do not apply to more turbid conditions because of the nonlinear response of reflectance to increasing sediment levels [Jonasz and Fournier, 2007; Pavelsky and Smith, 2009; Ritchie et al., 2003a; Schiebe et al., 1992; Witte, 1982]. Consequently, establishing a physical basis for modeling the water-leaving reflectance for a given concentration could prove to more reliably assess SSC. Furthermore, this would present a general approach which could be applied across a range of rivers, a range of conditions, and over several decades of imagery.
 In this paper we seek to establish a framework for a universal approach for deriving SSC in rivers from remote sensing imagery. We emphasize that our primary contribution is to synthesize the work of others into a unifying framework, fundamentally based on physical theory, and specific to turbid rivers. Because we hope that this framework will be tested by others, we purposely designed this study to rely upon the minimum amount of easily obtainable in situ data and model code. More specifically, we:
 1. Present model coefficients describing the inherent optical properties of a specific type (color, density) and size distribution of inorganic and organic particles suspended in water.
 2. Generate end members (sets of water-leaving reflectance spectra) for each of several combinations of SSC and other relevant water constituents impacting the remote-sensing signal.
 3. Invert the end members with field measurements of the water-leaving spectral reflectance for a given SSC to obtain reasonable values of the other water constituents.
 4. Decompose the signal in two Landsat images using a spectral mixture analysis approach with the site-specific model end members to arrive at surface values of SSC in mg L−1.
 A theoretical approach, which accounts for the dependency of the form and magnitude of the water-leaving spectra on sediment mineralogy and particle-size distribution, appears promising for meeting our objective of a physically based, universally applicable approach to inferring high SSC in rivers.