Glacier thickness distribution is a prerequisite to simulate the future of glaciers. Inaccurate thicknesses may lead to significant uncertainties in the timing of future changes to glaciers and their consequences for water resources or sea level rise. Unfortunately, glacier thickness distribution is rarely measured and consequently has to be estimated. In this study, we present an approach developed on the well documented Argentière Glacier (French Alps) that uses surface mass balance (SMB) together with surface flow velocity data to quantify glacier thickness distribution over the entire surface of the glacier. We compare the results of our approach to those obtained applying Farinotti et al. (2009) approach. Our results show that glacier thickness distribution are significantly biased when the glacier SMB profile used to quantify the ice fluxes is not constrained with in situ measurements. We also show that even with SMB measurements available on the studied glacier, ice flux estimates can be inaccurate. This inability to correctly estimate ice fluxes from the apparent SMB may be due to the steady state assumption that is not respected from the available glacier surface topography data. Therefore, ice thickness measurements on few cross sections (four are used in this study) are required to constrain the ice flux estimates and lead to an overall agreement between the ice thickness estimations and measurements. Using our approach, the ice thicknesses only differ by 10% from observations in average, but can differ by up to 150 m (or 30%) locally. We also show that approaches that use the glacier surface slope can lead to large uncertainties given that the quantification of the slope is highly uncertain. The approach presented here does not pretend to be applied globally but rather as a tool to quantify ice thickness distribution over the entire surface of glaciers for which a few in situ surface mass balance and thickness data are available together with surface flow velocities that can be obtained for example from remote sensing.