High-Precision Multi-Sensor Digital Surface Model Dataset Based on UAV and Satellite Data for Greenland Glacier Monitoring

Abstract

This study presents a methodological framework for developing a high-precisiondigitalsurface model (DSM) dataset based on complementary unmanned aerialvehicle (UAV) light detection and ranging (LiDAR) and satellite-derived data for RussellGlacier terminus, Greenland. Field surveys were conducted across four annualcampaigns (2022-2025), acquiring UAV LiDAR data that were subsequently cross-referencedwith three satellite-derived DSMs, ASTER GDEM V3, WorldDEM, and Arctic-DEM Mosaic v4.1. Vertical rectification using inverse distance weighting interpolationwith six ground control points was applied to all datasets, achieving a remarkable94.1% improvement in accuracy, reducing mean root mean square error (RMSE) from19.05 to 1.12 m. WorldDEM demonstrated the most substantial improvement (98.6%reduction), while post-correction ArcticDEM achieved the highest accuracy (0.20 mRMSE). UAV LiDAR maintained centimeter-level precision (0.51 m RMSE). Spatiotemporalanalysis revealed significant morpho-dynamic changes, including proglaciallake expansion, moraine evolution, and surface elevation variations. Over 20 years(2000-2025), terminus-region elevation differences reached to 20 m. The verticalrectification methodology demonstrates the effectiveness of employing multi-sensordatasets derived from complementary platforms to overcome individual sensor limitations. This dataset supports glacier mass balance research, dynamics investigations,and climate change impact assessments. The dataset is publicly available through theKorea Polar Data Center (KPDC).