With the development of many earth-observing remote sensing (RS) platforms, spatially distributed remote sensing products are becoming critical inputs to many hydrologic and meteorological models. Remotely sensed soil moisture (SM) and evapotranspiration (ET) including ground-based data have the potential to be used for estimating pixel-scale soil hydraulic parameters. However, only a few studies have been conducted to better understand the impact of assimilating both SM and ET in estimating soil hydraulic properties of the root zone. In this study, we used inverse modeling based on the Noisy Monte Carlo Genetic Algorithm by linking RS SM and ET derived from the Surface Energy Balance Algorithm for Land for estimating pixel-scale effective soil hydraulic properties. Walnut Creek (Iowa), Brown (Illinois), and Lubbock (Texas) test sites were selected to assess the performance of this approach from point to satellite scales using synthetic and validation experiments. For comparison purposes, inverse modeling results were analyzed under three scenarios (ET only, SM only, and SM + ET in the optimization criteria). These results showed that considering both SM and ET components improved the estimations of effective soil hydraulic properties and reduced their uncertainties better than SM or ET only. Overall, although uncertainty exists, our proposed SM + ET based scheme performed well in estimating effective soil hydraulic properties at multiple spatial scales (point, airborne, and satellite footprints) under various hydroclimatic conditions.