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Error and Uncertainty Degrade Topographic Corrections of Remotely Sensed Data
  • +7
  • Jeff Dozier,
  • Edward H. Bair,
  • Latha Baskaran,
  • Philip Gregory Brodrick,
  • Nimrod Carmon,
  • Raymond Kokaly,
  • Charles E. Miller,
  • Kimberley Miner,
  • Thomas H. Painter,
  • David Ray Thompson
Jeff Dozier
University of California, Santa Barbara, University of California, Santa Barbara, University of California, Santa Barbara, University of California, Santa Barbara, University of California, Santa Barbara

Corresponding Author:[email protected]

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Edward H. Bair
University of California, Santa Barbara, University of California, Santa Barbara, University of California, Santa Barbara, University of California, Santa Barbara, University of California, Santa Barbara
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Latha Baskaran
Jet Propulsion Laboratory, California Institute of Technology, Jet Propulsion Laboratory, California Institute of Technology, Jet Propulsion Laboratory, California Institute of Technology, Jet Propulsion Laboratory, California Institute of Technology, Jet Propulsion Laboratory, California Institute of Technology
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Philip Gregory Brodrick
Jet Propulsion Laboratory, California Institute of Technology, Jet Propulsion Laboratory, California Institute of Technology, Jet Propulsion Laboratory, California Institute of Technology, Jet Propulsion Laboratory, California Institute of Technology, Jet Propulsion Laboratory, California Institute of Technology
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Nimrod Carmon
Jet PRopulsion Laboratory, California Institute of Technology, Jet PRopulsion Laboratory, California Institute of Technology, Jet PRopulsion Laboratory, California Institute of Technology, Jet PRopulsion Laboratory, California Institute of Technology, Jet PRopulsion Laboratory, California Institute of Technology
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Raymond Kokaly
United States Geological Survey, United States Geological Survey, U.S. Geological Survey, U.S. Geological Survey
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Charles E. Miller
Jet Propulsion Laboratory, Jet Propulsion Laboratory, Jet Propulsion Laboratory, Jet Propulsion Laboratory, Jet Propulsion Laboratory
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Kimberley Miner
Jet Propulsion Laboratory, Jet Propulsion Laboratory, Jet Propulsion Laboratory, Jet Propulsion Laboratory, Jet Propulsion Laboratory
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Thomas H. Painter
UCLA, UCLA, UCLA, UCLA, UCLA
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David Ray Thompson
Jet Propulsion Laboratory, California Institute of Technology, Jet Propulsion Laboratory, California Institute of Technology, Jet Propulsion Laboratory, California Institute of Technology, Jet Propulsion Laboratory, California Institute of Technology, Jet Propulsion Laboratory, California Institute of Technology
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Abstract

Chemical and biological composition of surface materials and physical structure and arrangement of those materials determine the intrinsic reflectance of Earth’s land surface. The apparent reflectance—as measured by a spaceborne or airborne sensor that has been corrected for atmospheric attenuation—depends also on topography, surface roughness, and the atmosphere. Especially in Earth’s mountains, estimating properties of scientific interest from remotely sensed data requires compensation for topography. Doing so requires information from digital elevation models (DEMs). Available DEMs with global coverage are derived from spaceborne interferometric radar and stereo-photogrammetry at ~30 m spatial resolution. Locally or regionally, lidar altimetry, interferometric radar, or stereo-photogrammetry produces DEMs with finer resolutions. Characterization of their quality typically expresses the root-mean-square (RMS) error of the elevation, but the accuracy of remotely sensed retrievals is sensitive to uncertainties in topographic properties that affect incoming and reflected radiation and that are inadequately represented by the RMS error of the elevation. The most essential variables are the cosine of the local solar illumination angle on a slope, the shadows cast by neighboring terrain, and the view factor, the fraction of the overlying hemisphere open to the sky. Comparison of global DEMs with locally available fine-scale DEMs shows that calculations with the global products consistently underestimate the cosine of the solar angle and underrepresent shadows. Analyzing imagery of Earth’s mountains from current and future spaceborne missions requires addressing the uncertainty introduced by errors in DEMs on algorithms that analyze remotely sensed data to produce information about Earth’s surface.
Nov 2022Published in Journal of Geophysical Research: Biogeosciences volume 127 issue 11. 10.1029/2022JG007147