A high‐accuracy map of global terrain elevations

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.

[1]  Thomas A. Hennig,et al.  The Shuttle Radar Topography Mission , 2001, Digital Earth Moving.

[2]  L. Hess,et al.  Outgassing from Amazonian rivers and wetlands as a large tropical source of atmospheric CO2 , 2002, Nature.

[3]  Jasdev Singh,et al.  Building the National GeoBase for Canada , 2003 .

[4]  D. Cahoon,et al.  Global carbon sequestration in tidal, saline wetland soils , 2003 .

[5]  D. Harding,et al.  ICESat waveform measurements of within‐footprint topographic relief and vegetation vertical structure , 2005 .

[6]  Adrian A. Borsa,et al.  Assessment of ICESat performance at the salar de Uyuni, Bolivia , 2005 .

[7]  William B. Krabill,et al.  ICESat range and mounting bias estimation over precisely‐surveyed terrain , 2005 .

[8]  E. Rodríguez,et al.  A Global Assessment of the SRTM Performance , 2006 .

[9]  David J. Harding,et al.  SRTM C-band and ICESat Laser Altimetry Elevation Comparisons as a Function of Tree Cover and Relief , 2006 .

[10]  Gerhard Krieger,et al.  TanDEM-X: A Satellite Formation for High-Resolution SAR Interferometry , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Andrew Jarvis,et al.  Hole-filled SRTM for the globe Version 4 , 2008 .

[12]  J. C. Gallant,et al.  Enhancing the SRTM Da ta for Australia , 2009 .

[13]  V. Titov,et al.  Development, testing, and applications of site‐specific tsunami inundation models for real‐time forecasting , 2009 .

[14]  J. Kaplan,et al.  The prehistoric and preindustrial deforestation of Europe , 2009 .

[15]  Susan E. Hough,et al.  Localized damage caused by topographic amplification during the 2010 M 7.0 Haiti earthquake , 2010 .

[16]  Richard Smith,et al.  ACE2: The New Global Digital Elevation Model , 2010 .

[17]  W. Featherstone,et al.  Comparison and validation of the recent freely available ASTER-GDEM ver1, SRTM ver4.1 and GEODATA DEM-9S ver3 digital elevation models over Australia , 2010 .

[18]  Martin Berggren,et al.  Patterns and Dynamics of Dissolved Organic Carbon (DOC) in Boreal Streams: The Role of Processes, Connectivity, and Scaling , 2011, Ecosystems.

[19]  A. Baccini,et al.  Mapping forest canopy height globally with spaceborne lidar , 2011 .

[20]  T. I. Dowling,et al.  Continental hydrologic assessment using the 1 second (30m) resolution Shuttle Radar Topographic Mission DEM of Australia , 2011 .

[21]  D. Gesch,et al.  Global multi-resolution terrain elevation data 2010 (GMTED2010) , 2011 .

[22]  Akira Iwasaki,et al.  Characteristics of ASTER GDEM version 2 , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[23]  J. Poesen,et al.  Predicting soil erosion and sediment yield at regional scales: Where do we stand? , 2013 .

[24]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[25]  Takahiro Sayama,et al.  CORRECTION OF SRTM DEM ARTEFACTS BY FOURIER TRANSFORM FOR FLOOD INUNDATION MODELING , 2013 .

[26]  Paul D. Bates,et al.  SRTM vegetation removal and hydrodynamic modeling accuracy , 2013 .

[27]  Michael J. Oimoen,et al.  Accuracy assessment of the U.S. Geological Survey National Elevation Dataset, and comparison with other large-area elevation datasets: SRTM and ASTER , 2014 .

[28]  S. Kanae,et al.  Regional flood dynamics in a bifurcating mega delta simulated in a global river model , 2014 .

[29]  Paul D. Bates,et al.  Technology: Fight floods on a global scale , 2014, Nature.

[30]  Paul D. Bates,et al.  Flooding dynamics on the lower Amazon floodplain: 1. Hydraulic controls on water elevation, inundation extent, and river‐floodplain discharge , 2014 .

[31]  Robert P. Guralnick,et al.  EarthEnv-DEM90: A nearly-global, void-free, multi-scale smoothed, 90m digital elevation model from fused ASTER and SRTM data , 2014 .

[32]  Lisa-Maria Rebelo,et al.  Development of a global inundation map at high spatial resolution from topographic downscaling of coarse-scale remote sensing data , 2015 .

[33]  V. Thiel,et al.  Assessing the utility of trace and rare earth elements as biosignatures in microbial iron oxyhydroxides , 2015, Front. Earth Sci..

[34]  Takeo Tadono,et al.  Status of “ALOS World 3D (AW3D)” global DSM generation , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[35]  Paul D. Bates,et al.  A high‐resolution global flood hazard model† , 2015, Water resources research.

[36]  Mark A. Trigg,et al.  Development of a global ~90m water body map using multi-temporal Landsat images , 2015, Remote Sensing of Environment.

[37]  Takeo Tadono,et al.  Quality status of high resolution global DSM generated from ALOS PRISM , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[38]  P. Bates,et al.  Usefulness and limitations of global flood risk models , 2015 .

[39]  R. Maxwell,et al.  Connections between groundwater flow and transpiration partitioning , 2016, Science.

[40]  Paul D. Bates,et al.  ICESat‐derived inland water surface spot heights , 2016 .

[41]  C. Synolakis,et al.  Development of MOST for Real-Time Tsunami Forecasting , 2016 .

[42]  Paul D. Bates,et al.  Perspectives on Open Access High Resolution Digital Elevation Models to Produce Global Flood Hazard Layers , 2016, Front. Earth Sci..

[43]  Takeo Tadono,et al.  Adaptive filter for improving quality of ALOS PRISM DSM , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[44]  Maxim Neumann,et al.  NASADEM GLOBAL ELEVATION MODEL: METHODS AND PROGRESS , 2016 .

[45]  F. O'Loughlin,et al.  A multi-sensor approach towards a global vegetation corrected SRTM DEM product , 2016 .

[46]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[47]  J. Townshend,et al.  Annual Global Automated MODIS Vegetation Continuous Fields (MOD44B) at 250 m Spatial Resolution for Data Years Beginning Day 65, 2000 - 2010 , 2017 .