In the case of dense forests, the accuracy of estimating underforest terrain using GEDI data and existing Tandem-X DEM digital terrain models has not been comprehensively evaluated. Aiming to focus on the dense forest situation as the main research object and using airborne data as real validation data. By extracting the longitude and latitude of the corresponding LiDAR spot, underforest terrain information, and data quality screening parameters of the GEDI L2A data product, to estimate underforest terrain data based on GEDI data. Compared with Tandem-X DEM data to estimate the underforest terrain under dense forest conditions, and further explore the effects of canopy height, forest coverage, and vegetation type on estimation accuracy. The R2 values of GEDI and Tandem-X DEM were 0.99 and 0.98, respectively. The RMSE, Average, and STD values of GEDI for estimating underforest terrain were 6.49m, -1.92m, and 4.42m, respectively. The RMSE, Average, and STD values of Tandem-X DEM for estimating underforest terrain were 18.15m, 14.63m, and 7.35m, respectively. In GEDI data, RMSE and Average were changed by 8.05m and 6.04m respectively in the case of mixed forest and sparse grassland, and in Tandem-X DEM data, RMSE and Average were changed by 21.63m and 26.43m respectively in the case of evergreen coniferous forest and farmland/natural vegetation. The experimental results indicated that there was a strong correlation between GEDI and Tandem-X DEM data and airborne validation data, and GEDI performed better evaluation criteria than Tandem-X DEM data. The surface vegetation types performed greater impact on the estimation of underforest terrain than canopy height and vegetation coverage.