Geophysical Research Letters · 2017 · 1.6K citations · 39 references
ComputingEarth SciencesGeoinformaticsDigital Elevation ModelingGeographic Information SystemsRemote SensingDigital Elevation ModelsDigital Terrain ModelingDigital Terrain ModelsTerrain ModelingTerrain MorphometryTopographic MappingAccuracyDem Accuracy AssessmentDem Error AnalysisDigital Elevation Model Error AnalysisDigital Elevation Model Error AssessmentStripe Noise RemovalTerrain-dependent Error AnalysisVertical Accuracy Assessment
Spaceborne digital elevation models are essential for geoscience but still contain significant height errors. This study introduces a high‑accuracy global DEM at 3″ resolution (~90 m) by eliminating major error components from existing models. The authors separated absolute bias, stripe noise, speckle noise, and tree‑height bias using multiple satellite datasets and filtering techniques. After error removal, the new DEM attains ±2 m vertical accuracy over 58 % of land, improves flat terrains, river networks, and hill‑valley structures, corrects slope distortions in major floodplains and swamp forests, and enhances terrain‑dependent geoscience applications.
Abstract Spaceborne digital elevation models (DEMs) are a fundamental input for many geoscience studies, but they still include nonnegligible height errors. Here we introduce a high‐accuracy global DEM at 3″ resolution (~90 m at the equator) by eliminating major error components from existing DEMs. We separated absolute bias, stripe noise, speckle noise, and tree height bias using multiple satellite data sets and filtering techniques. After the error removal, land areas mapped with ±2 m or better vertical accuracy were increased from 39% to 58%. Significant improvements were found in flat regions where height errors larger than topography variability, and landscapes such as river networks and hill‐valley structures, became clearly represented. We found the topography slope of previous DEMs was largely distorted in most of world major floodplains (e.g., Ganges, Nile, Niger, and Mekong) and swamp forests (e.g., Amazon, Congo, and Vasyugan). The newly developed DEM will enhance many geoscience applications which are terrain dependent.
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