Observing platforms of greenhouse gas column mole fractions using remote sensing instruments have enhanced the capability of carbon data assimilation systems at large scales and helped improve our understanding of the underlying processes involved in the exchange of carbon at the surface of the globe. In this study, we quantify the additional information carried by these measurements at finer scales and consider the impact of vertical transport errors in current modeling systems, one of the main sources of uncertainties in regional inverse flux estimates. Surface-based column-integrated sensors are shown to be a significant source of information to constrain local surface fluxes at fine scales. Gain and error reduction are only about 20% lower than for in situ instruments. Column measurements show less dependence on near-field surface fluxes compared to in situ, with the error reduction being more homogeneously distributed. However, vertical transport errors still impact the flux retrievals, as with in situ measurements but to a lesser extent. Inverse fluxes from both types of measurements were affected by errors in vertical mixing and mean horizontal winds, with a larger impact on the inverse carbon balance using in situ measurements. The use of remote sensing measurements also appeared to constrain significantly the boundary concentrations, a critical limitation in current regional inversions. We finally performed a pseudo-data experiment combining both types of instruments, creating an optimal observing network with a lower impact of planetary boundary layer transport errors on the surface fluxes and the boundary concentrations, and a more widely distributed reduction of the errors over the boundaries.