  Nonlinear force-free (NLFF) modeling is regularly used in order to indirectly
infer the 3D geometry of the coronal magnetic field, not accessible on a
regular basis by means of direct measurements otherwise. We study the effect of
binning in time series NLFF modeling of individual active regions (ARs) in
order to quantify the effect of a different underlying spatial sampling on the
quality of modeling as well as on the derived physical parameters. We apply an
optimization method to sequences of SDO/HMI vector magnetogram data at three
different plate scales for three solar ARs to obtain nine NLFF model time
series. From the NLFF models, we deduce active-region magnetic fluxes, electric
currents, magnetic energies and relative helicities, and analyze those with
respect to the underlying spatial sampling. We calculate various metrics to
quantify the quality of the derived NLFF models and apply a Helmholtz
decomposition to characterize solenoidal errors. At a given spatial sampling,
the quality of NLFF modeling is different for different ARs, as well as varies
along of the individual model time series. For a given AR, modeling at a given
spatial sampling is not necessarily of superior quality compared to that
performed with a different plate scale. Generally, the NLFF model quality tends
to be higher for larger pixel sizes with the solenoidal quality being the
ultimate cause for systematic variations in model-deduced physical quantities.
Optimization-based modeling based on binned SDO/HMI vector data delivers
magnetic energies and helicity estimates different by $\lesssim$30\%, given
that concise checks ensure the physical plausibility and high solenoidal
quality of the tested model. Thus, spatial-sampling-induced differences are
relatively small compared to that arising from other sources of uncertainty.
