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A High-Accuracy Map of Global Terrain Elevations
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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To correct the effect of vegetation on most pixels, the adopted solution is to develop an empirical model that relates vegetation height, or the magnitude of the error vegetation causes in DEMs, to the correction.
“To correct the effect of vegetation on most pixels, the solution that has been adopted is to develop an empirical model that relates the height of the vegetation, or more precisely, the magnitude of the error that the vegetation causes in DEMs [22][23][24].”
From ANADEM: A Digital Terrain Model for South America (2024) · cites this paper as [23]
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High-resolution mapping of global surface water and its long-term changes
Pekel, J.-F., Cottam, A., Gorelick, N., & Belward, A. S. (2016) · Nature · doi:10.1038/nature20584 · Cited by 5,513
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SCDNA: a serially complete precipitation and temperature dataset for North America from 1979 to 2018
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ANADEM: A Digital Terrain Model for South America
Laipelt, L., Comini de Andrade, B., Collischonn, W., de Amorim Teixeira, A., Paiva, R. C. D. D., & Ruhoff, A. (2024) · Remote Sensing · doi:10.3390/rs16132321 · Cited by 79
The North American tree‐ring fire‐scar network
Margolis, E. Q., Guiterman, C. H., Chavardès, R. D., Coop, J. D., Copes‐Gerbitz, K., Dawe, D. A., Falk, D. A., Johnston, J. D., Larson, E., Li, H., et al. (2022) · Ecosphere · doi:10.1002/ecs2.4159 · Cited by 75
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A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015
Kitambo, B. M., Papa, F., Paris, A., Tshimanga, R. M., Frappart, F., Calmant, S., Elmi, O., Fleischmann, A. S., Becker, M., Tourian, M. J., et al. (2023) · Earth System Science Data · doi:10.5194/essd-15-2957-2023 · Cited by 19
Metadata
- Authors
- Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., & Bates, P. D.
- Affiliations
- Department of Integrated Climate Change Projection Research Japan Agency for Marine‐Earth Science and Technology Yokohama Japan
- Department of Civil and Environmental Engineering Tokyo Institute of Technology Tokyo Japan
- Institute of Space and Astronautical Science Japan Aerospace Exploration Agency Sagamihara Japan
- UCD School of Civil Engineering University College Dublin Dublin Ireland
- School of Geographical Sciences University of Bristol Bristol UK
- SSBN Bristol UK
- ORCID
- Venue
- Geophysical Research Letters
- Publisher
- American Geophysical Union (AGU)
- Published
- 2017-06-14
- Volume
- 44
- Issue
- 11
- Pages
- 5844-5853
- ISSN
- 0094-8276
- 1944-8007
- Cited by
- 1,326
- References
- 2
- Language
- en
- Funders
- Japan Society for the Promotion of Science (16H06101, 16H06291)
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