Remotely sensed (RS) data are becoming increasingly important as sources of auxiliary information in forest resource assessments. Data from several satellites providing moderate image resolution are freely available (e.g. Sentinel-2). In addition, very-high-resolution three-dimensional data are available due to the advent of unmanned aerial vehicles (UAV). The increasing availability of auxiliary data offers new opportunities for large-scale forest surveys using UAVs. A recently developed hierarchical model-based mode of inference makes it possible to use hierarchically nested auxiliary data in estimating population properties, such as total or mean biomass or volume, and their corresponding uncertainties in a statistically appropriate manner. In this study, hierarchical model-based inference was used to estimate growing stock volume (GSV; m3ha−1) and its variance using a small sample of field data, a larger sample of UAV data, and wall-to-wall Sentinel-2 data in a study area in SE Norway. The main objective of the study was to compare the performance, in terms of precision, of hierarchical model-based inference (denoted Case C) against two alternative cases. These were (1) model-based inference based on field data and wall-to-wall data, collected either with airborne laser scanning (Case A.1) or Sentinel-2 data (Case A.2), and (2) hybrid inference using a small sample of field data and a larger sample of UAV data (Case B). A second objective was to assess the possibility of reducing the UAV sampling intensity when adopting Case C rather than B, without decreasing the precision of the GSV estimates. The results, calculated as standard error as percentage of the mean (SÊ%), indicated that in case C the precision was of similar magnitude (SÊ%=3.44%) as for Case A.1 (SÊ%=3.69%) and for Case B (SÊ%=3.58%). The standard error of Case A.2 was nearly twice as large (SÊ%=5.81%) as the rest of the cases. The results also indicated possibilities of reducing the UAV sampling intensity without losing precision in cases where wall-to-wall Sentinel-2 data are available (Case C). The same precision for Case C with only five UAV samples was achieved as for Case B with 55 UAV samples. Thus, the study highlights the cost-efficiency of applications of UAV as in Case C and also provides first insights in the use of Sentinel-2 data for GSV estimation in boreal conditions. •Forest growing stock volume was estimated using field, UAV, and Sentinel-2 data.•Hierarchical model-based inference was adopted.•Adding Sentinel-2 data in UAV based estimation was cost-efficient.•Similar results for the hierarchical inference and model-based estimation with ALS.•First insights in using Sentinel-2 data for forest growing stock volume estimation.