Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain – Dataset

<strong>Dataset of Hugonnet et al. (2022), Uncertainty Analysis of Digital Elevation Models by Spatial Inference From Stable Terrain.</strong> The data is composed of: <strong>For the Mont-Blanc case study: </strong>the Pléiades reference DEM, the SPOT-6 DEM, the Pléiades–SPOT-6 elevation difference, and the forest mask generated from the ESA CCI landcover (delainey polygonization); <strong>For the Northern Patagonian Icefield case study: </strong>the ASTER reference DEM, the SPOT-5 DEM, the ASTER–SPOT-5 elevation difference, and the quality of stereo-correlation of the ASTER DEM from MicMac. The filenames correspond to those used in the <strong>associated GitHub repository</strong>: https://github.com/rhugonnet/dem_error_study. The shapefiles used for masking glaciers are available directly from the <strong>Randolph Glacier Inventory 6.0</strong> at https://www.glims.org/RGI/. The date of the DEMs is in their original format: <strong>year-month-day for all but ASTER</strong> that has the original naming of AST L1A products. <strong>Units are meters</strong> for the DEMs and elevation differences, <strong>and percentages</strong> for the quality of stereo-correlation.