## 5 Conclusions  
In the computation of a 1\({}^{\prime\prime}\) bare-earth DTM for Costa Rica, CRDTM2020, we tested several analytical approaches for the reduction of VB from NASADEM, which is a recent DSM based on SRTM data processed with more advanced methods than its predecessors. LVIS datasets provided valuable insight into canopy structure and ground elevations and were extensively used for the validation of both canopy models and DEMs. The elimination of stochastic VB was possible thanks to the Global Forest Canopy Height 2019 Model [17], which is based on the laser ranging data from the GEDI mission and Landsat imagery. Further improvement is possible if GFCH is computed for the year 2000 using historical Landsat imagery and calibrated using GEDI data, as stated in future plans by [17]. Nevertheless, to overcome the difference between vegetation distribution in the year 2000 and 2019, we used the canopy density model for the year 2000 from the Global Forest Change 2000-2019 Model [7] and computed Canopy Height Model 2000. However, there was no possibility to make a reliable prediction and account for the natural growth/decay of vegetation between the years 2019 and 2000. This highlights the need for a model such as GFCH for the year 2000, as well as other years if it is sought to eliminate VB from other DSMs not based on SRTM data. It should be emphasised that GFHC2019 is a \"low hanging fruit\" for the calculation of a new global bare-earth 1\({}^{\prime\prime}\) DTM. The analytical approach used in this study to remove VB was discussed, and we believe that it can be used on a global scale. Further combination with models of forest types, climate zones, or land use could be investigated for tuning the coefficient that is used to scale the canopy heights for different regions. Additionally, we found that scaling by canopy density as defined in the Global Forest Change 2000-2019 Model helps to account for increased canopy penetration in low vegetation. Comparison and similarities in the findings of previous studies on SRTM signal penetration into vegetation and proposed models to reduce VB also support the claim that the simple analytical way of reducing VB presented in this study would be valid elsewhere. Further investigation on the penetration of the X-band radar signal from TanDEM-X, and VB in DSM based on optical observations (ASTER DEM, AW3D30) is required before the computation of bare-earth versions of the respective model. Evaluation of CRDTM2020 and nine publicly available DEMs was performed for Costa Rica for the first time using LVIS data collected over Costa Rica in 1998, 2005, and 2019 and using a historic levelling dataset. Among the 1\({}^{\prime\prime}\) DSMs, NASADEM had the best statistical metrics, followed by AW3D30 and SRTMGL1. The ASTER DEM had a smaller mean and median error but larger MAD and STD\({}^{\bullet}\). The 3\({}^{\prime\prime}\) DSMs SRTMv3, SRTMv4, and TanDEM-X performed worse over the study area as compared to 1\({}^{\prime\prime}\) DSMs, which can be explained by steep terrain slopes, which calls for smaller mesh size grids to represent the terrain. The MERIT DEM, a 3\({}^{\prime\prime}\) bare-earth global DTM, exhibited good performance over the study area. Its mean and median errors were close to zero, though other metrics were inferior to CRDTM2020. For Costa Rica, the regional bare-earth CRDTM2020 performed considerably better than any publicly available global DEM model. The mean error/uncertainty (STD\({}^{\bullet}\)) was 0.9 m/7.9 m using the LVIS2019 validation dataset, \(-3.1\) m/8.5 m using the LVIS1998 validation dataset, and \(-1.2\) m/10.0 m with the levelling dataset. Therefore, it is more suitable for studies in hydrology, prediction and monitoring of floods, landslides, erosion, coastal studies, etc., than other DEMs and could be also beneficial in other branches of science and engineering that require a bare-earth surface representation on a 1\({}^{\prime\prime}\) grid.