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High Mountain Asia (HMA), recognized as a third pole, needs regular and intense studies as it is susceptible to climate change. An accurate and high-resolution Digital Elevation Model (DEM) for this region enables us to analyze it in a 3D environment and understand its intricate role as the Water Tower of Asia. The science teams of NASA realized an 8-m DEM using satellite stereo imagery for HMA, termed HMA 8-m DEM. In this research, we assessed the vertical accuracy of HMA 8-m DEM using reference elevations from ICESat-2 geolocated photons at three test sites of varied topography and land covers. Inferences were made from statistical quantifiers and elevation profiles. For the world's highest mountain, Mount Everest, and its surroundings, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) resulted in 1.94 m and 1.66 m, respectively; however, a uniform positive bias observed in the elevation profiles indicates the seasonal snow cover change will dent the accurate estimation of the elevation in this sort of test sites. The second test site containing gentle slopes with forest patches has exhibited the Digital Surface Model (DSM) features with RMSE and MAE of 0.58 m and 0.52 m, respectively. The third test site, situated in the Zanda County of the Qinghai-Tibet, is a relatively flat terrain bed, mostly bare earth with sudden river cuts, and has minimal errors with RMSE and MAE of 0.32 m and 0.29 m, respectively, and with a negligible bias. Additionally, in one more test site, the feasibility of detecting the glacial lakes was tested, which resulted in exhibiting a flat surface over the surface of the lakes, indicating the potential of HMA 8-m DEM for deriving the hydrological parameters. The results accrued in this investigation confirm that the HMA 8-m DEM has the best vertical accuracy and should be of high use for analyzing natural hazards and monitoring glacier surfaces. [ABSTRACT FROM AUTHOR]

This data descriptor elaborates the details of a high-resolution digital bathymetric elevation model generated for the region, namely, Adam's Bridge, which encompasses a chain of shoals between Rameswaram Island, off the southeastern coast of Tamil Nadu, India, and Mannar Island, off the northwestern coast of Sri Lanka. The proposed dataset has taken advantage of the photon penetrability in the shallow waters by the green laser of ICESat-2 LiDAR to derive the seabed topography. Seafloor depths from ~0.2 million geolocated photons of ICESat-2 for the study area were accrued and interpolated to generate a 10 m digital bathymetric elevation model. Adam's Bridge, an isthmus and submerged reefal assemblage in shallow and super-shallow waters, is a feature of scientific curiosity. Our dataset has the potential to enhance the understanding of Adam's Bridge structure by providing substantial information to reconstruct its evolution. [ABSTRACT FROM AUTHOR]

Bathymetric data represents estimated seafloor topography and aids in understanding the intricacies of earth and ocean interaction processes. The General Bathymetric Chart of the Oceans (GEBCO) released the GEBCO_2023 gridded bathymetric data as an interim dataset in connection with the ambitious task of producing the definitive ocean floor map for the globe by 2023. Evaluating the data of scientific importance is essential to ensure its fitness for the applications; however, the procedure needs qualified reference data of higher accuracy for comparison. This article discusses the methods and results of performance validation on GEBCO_2023 using the seafloor obtained from the ICESat-2 geolocated photons as a reference. The validation was carried out at three test sites containing shallow waters in the Indian Ocean. In two of the test sites, where the coastal waters have minimal influence from the continental sediment flux, the trend of the seafloor from both the data sources is similar, and the quantified accuracy of GEBCO_2023 in terms of RMSE is less than 3 m. In the extent of the third test site, where mostly perennial turbidity prevails, the accuracy of the GEBCO's seafloor depth in terms of RMSE and MAE is in the range of 5–6 m, with underestimation of the seafloor. The reasons for the errors in the GEBCO_2023 grid were analyzed based on the associated metadata, namely, the Type-Identifier grid that informs the source of depth data for a given grid cell. In summary, the GEBCO_2023 grid is the best available and resourceful bathymetric data in the present scenario where challenges and complications exist for mapping the ocean surface besides technological advancements. [ABSTRACT FROM AUTHOR]