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\begin{document}

\title{Error and Uncertainty Degrade Topographic Corrections of Remotely Sensed
Data}



\author[1,1,1,1,1]{Jeff Dozier}%
\author[1,1,1,1,1]{Edward H. Bair}%
\author[2,2,2,2,2]{Latha Baskaran}%
\author[2,2,2,2,2]{Philip Gregory Brodrick}%
\author[3,3,3,3,3]{Nimrod Carmon}%
\author[4,4,5,5]{Raymond Kokaly}%
\author[6,6,6,6,6]{Charles E. Miller}%
\author[6,6,6,6,6]{Kimberley Miner}%
\author[7,7,7,7,7]{Thomas H. Painter}%
\author[2,2,2,2,2]{David Ray Thompson}%
\affil[1]{University of California, Santa Barbara}%
\affil[2]{Jet Propulsion Laboratory, California Institute of Technology}%
\affil[3]{Jet PRopulsion Laboratory, California Institute of Technology}%
\affil[4]{United States Geological Survey}%
\affil[5]{U.S. Geological Survey}%
\affil[6]{Jet Propulsion Laboratory}%
\affil[7]{UCLA}%


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  \date{May 18, 2024}


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\begin{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
\textasciitilde{}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.%
\end{abstract}%



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