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Because of the high logistical and financial costs of direct measurements of riverine suspended sediment, remote sensing is increasingly used to supplement the direct‐observation record. The accuracy of this method is poorly constrained, and its potential as a tool for understanding river sediment transport is thus limited. We introduce and apply global‐scale methods for estimating depth‐integrated suspended‐sediment concentrations (SSCs) using Landsat 5 and 7 satellite imagery calibrated with 134,697 in situ SSC measurements. We account for river‐to‐river variability in the relationship between water optical properties and SSC by (a) categorizing rivers using unsupervised K‐means clustering and/or (b) correcting calibration estimates for individual rivers using local in situ measurements of SSC, suspended‐sediment grain size, and percent organic carbon (POC). In the absence of site‐specific in situ SSC measurements, clustering rivers reduces the average relative error of SSC estimates from 97% to 73%. We show that as few as five site‐specific in situ measurements combined with our algorithm further reduces average relative error to 49% and average relative at‐a‐station bias in SSC to 7%. Little additional improvement in accuracy or bias is gained by including measurements of percent sand or POC of the suspended sediment. Since only modest additional accuracy is gained after ~5–10 paired in situ SSC measurements and satellite observations, sampling campaigns should prioritize limited sampling at diverse locations rather than intensive sampling at a limited number of sites. In addition, we publish standalone calibrations for 151 rivers made solely with in situ SSC measurements local to those sites.