Understanding the effects of climate on glaciers requires precise estimates of ice volume change over several decades. This is achieved by the geodetic mass balance computed by two means: (1) the digital elevation model (DEM) comparison (SeqDEM) allows measurements over the entire glacier, however the low contrast over glacierized areas is an issue for the DEM generation through the photogrammetric techniques and (2) the profiling method (SePM) is a faster alternative but fails to capture the spatial variability of elevation changes. We present a new framework (SSD) that relies upon the spatial variability of the elevation change to densify a sampling network to optimize the surface-elevation change quantification. Our method was tested in two small glaciers over different periods. We conclude that the SePM overestimates the elevation change by similar to 20% with a mean difference of similar to 1.00 m (root mean square error (RMSE) = similar to 3.00 m) compared with results from the SeqDEM method. A variogram analysis of the elevation changes showed a mean difference of <0.10 m (RMSE = similar to 2.40 m) with SSD approach. A final assessment on the largest glacier in the French Alps confirms the high potential of our method to compute the geodetic mass balance, without going through the generation of a full-density DEM, but with a similar accuracy than the SeqDEM approach.