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The impact of measurement incidence angle (θ) on the accuracy of radar-based surface soil-moisture (Θs) retrievals is largely unknown due to discrepancies in theoretical backscatter models as well as limitations in the availability of sufficiently extensive ground-based Θs observations for validation. Here, we apply a data-assimilation-based evaluation technique for remotely sensed Θs retrievals that does not require ground-based soil-moisture observations to examine the sensitivity of skill in surface Θs retrievals to variations in θ. Past results with the evaluation approach have shown that it is capable of detecting relative variations in the anomaly correlation coefficient between remotely sensed Θs retrievals and ground-truth soil-moisture measurements. Application of the evaluation approach to the Vienna University of Technology (TU Wien) European Remote Sensing (ERS) scatterometer Θs data set over regional-scale ( ~ 10002 km2) domains in the Southern Great Plains and southeastern (SE) regions of the U.S. indicate a relative reduction in correlation-based skill of 23% to 30% for Θs retrievals obtained from far-field (θ>50°) ERS observations relative to Θs estimates obtained at θ <; 26°. Such relatively modest sensitivity to θ is consistent with Θs retrieval noise predictions made using the TU-Wien ERS Water Retrieval Package 5 backscatter model. However, over moderate vegetation cover in the SE domain, the coupling of a bare soil backscatter model with a “vegetation water cloud” canopy model is shown to overestimate the impact of θ on Θs retrieval skill.