Observational surface data are used to reconstruct the ocean's interior through the “interior?+?surface quasigeostrophic” (isQG) method. The input data include the satellite-derived sea surface height, satellite-derived sea surface temperature, satellite-derived or Argo-based sea surface salinity, and an estimated stratification of the region. The results show that the isQG retrieval of subsurface density anomalies is quite promising compared to Argo profile data. At ?1000 m depth, the directions of retrieved velocity anomalies are comparable to those derived from Argo float trajectories. The reconstruction using surface density input field approximated only by SST (with constant SSS) performs less satisfactorily than that taking into account the contribution of SSS perturbations, suggesting that the observed SSS information is important for the application of the isQG method. Better reconstruction is obtained in the warm season than in the cold season, which is probably due to the stronger stratification in the warm season that confines the influence of the biases in the surface input data (especially SSS) in a shallow layer. The comparison between the performance of isQG with Argo-based SSS input and that with satellite-derived SSS input suggests that the biases in the SSS products could be a major factor that influences the isQG performance. With reduced biases in satellite-derived SSS in the future, the measurement-based isQG method is expected to achieve better reconstruction of ocean interior and thus is promising in practical application.