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Digital elevation models play a crucial role in many earth sciences-related disciplines and are essential for spatial data infrastructure in a geospatial computational environment. A digital terrain model, a.k.a bare-earth model, excludes the bias due to the heights from urban and forests will enable the quantitative terrain characterization. Digital bare-earth models exemplify themselves as an essential input in numerous environmental analyses, especially in floodplain mapping. A forest and buildings removed Copernicus digital elevation model, referred to as FABDEM, is a first, global, open-access, machine learning-based, and 30 m spatial resolution data that simulates a bare-earth model. This article presents a novel approach to validate a digital bare-earth model by considering FABDEM as a case. Highly accurate elevations from the ground reflected photons from Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) were used as a reference to check the agreement of FABDEM’s tendency as a bare-earth model. Visual analytics done using the elevation profiles and error metrics confirms the FABDEM’s successful reduction of bias due to urban and forests to a certain extent from its source DEM. However, the error metric shows a positive offset of ~ 3 m while validating the FABDEM’s building removal algorithm, indicating a scope to decrease the building heights to achieve its anticipated objective of generating a bare-earth model in urban areas. In the case of the forest removal algorithm of FABDEM, it has successfully reduced the canopy heights to approximately 50% of its source. Still, the error metrics show a mean absolute error of ~ 14 m, ~ 10 m, and ~ 3 m in the test sites that fall in mountainous areas, rolling hills, and flat regions, respectively, that host different tree types and canopy structures. Our research has also investigated the possible sources of uncertainties and performance factors of FABDEM; these include the predictor variables, the number of regions of training sets, and errors that can accumulate from its original elevation source, i.e., Copernicus GLO-30 DEM.

Lake ice phenology is a temporally integrated response to the seasonal cycles of meteorology, and its study results in obtaining the periods of ice growth and melt process. The recently launched spaceborne laser altimetry satellite called Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) hosts a single sensor titled Advanced Topographic Laser Altimeter System (ATLAS) is equipped with photon-counting technology. In this research, we have investigated the applicability of a data product, namely ATL03 from the ICESat-2 to infer the state of alpine lake ice. Three alpine lakes situated in the Himalayan region, which are above 4200 m ASL and exhibit annual freeze–thaw cycles were studied to understand the interaction of ICESat-2 photons with the lake surface during various phases of the ice growth and decay process. Elevation profiles were generated from the photon beams of ICESat-2 over these lakes during various stages of its surface cover like meltwater conditions, ice thickening stage, frozen state, and ice breakup period. These elevation profiles besides giving the lake surface height, the pattern of photons in the profile has contributed to envisage the status of the ice surface over the lake. Photons from ICESat-2 during meltwater conditions can penetrate the subsurface, and this feature helps in distinguishing meltwater from frozen ice cover over the lake surface. Further, it was observed that during the stage of ice thickening stage, a certain number of photons have penetrated up to a depth of ~ 35 m but the number of photons that penetrated is significantly less when compared with that of penetrating photons during meltwater conditions. Similarly, during the ice breakup periods, the photon data of ICESat-2 are proven to identify the exposed water columns in the ice sheet. The results obtained from this study prove that the photon data from ICESat-2 over alpine lakes can result in advance the understanding of the lake ice phenology.