Digital elevation models (DEM), including the Shuttle Radar Topography Mission (SRTM), are used in many branches of geoscience as an ultimate dataset representing our planet’s surface, making it possible to investigate processes that are shaping our world. The SRTM model exhibits elevation bias or systematic error over forests and vegetated areas due to the microwaves’ peculiar properties that penetrate the vegetation layer to a certain depth. Numerous investigations identifi ed that the penetration depth depends on the forest density and height. In this contribution, two methods are proposed to remove the impact of the vegetation impenetrability eff ect. The first method is founded on the multiple regression of two forest characteristics, namely forest height and forest density. The second method uses a lookup table approach. The lookup table and the multiple regression explanatory variables are taken from the freely available datasets, including the forest density data (MODIS_VCF) and global tree height map (GTHM). An important role in this research is played by the Ice, Clouds, and Land Elevation Satellite (ICESat) data. The accuracy tests indicate that the fi rst method eliminates approximately 68% of the elevation bias, while the second method appears to be more effective, leading to almost complete removal of the vegetation bias from the SRTM data. The methods are fi ne-tuned to the local coniferous forests in Poland. Additional studies are required to fi netune the methods for the leaf-off state of deciduous forests. However, a new set of parameters for both methods can be quickly developed for diff erent locations and forest types. Both methods’ functionality and eff ectiveness can be improved once more accurate forest tree height and vegetation density data become available. These methods are universal in mitigating the vegetation bias from the Synthetic Aperture Radar Interferometry (InSAR) derived model and photogrammetric models.