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Quantifying uncertainty for remote spectroscopy of surface composition

May 30, 2020

Remote surface measurements by imaging spectrometers play an important role in planetary and Earth science.
To make these measurements, investigators calibrate instrument data to absolute units, invert physical models to
estimate atmospheric effects, and then determine surface properties from the spectral reflectance. This study
quantifies the uncertainty in this process. Global missions demand predictive uncertainty models that can estimate
future errors for varied environments and observing conditions. Here we validate uncertainty predictions
with remote surface composition retrievals and in situ measurements in a field analogue of Earth and planetary
exploration. We consider rover transects at Cuprite, Nevada, and remote observations by NASA's Next-
Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). We show that accounting for input
uncertainties can benefit mineral detection methods such as constrained spectrum fitting. This suggests that
operational uncertainty estimates could improve future NASA missions like the Earth Mineral dust source
InvesTigation (EMIT) and the Lunar Trailblazer mission, as well as NASA's Decadal Surface Biology and Geology (SBG) Investigation.

Publication Year 2020
Title Quantifying uncertainty for remote spectroscopy of surface composition
DOI 10.1016/j.rse.2020.111898
Authors David R. Thompson, Amy Braverman, Philip Brodrick, Alberto Candela, Nimrod Carmon, Roger N. Clark, David Connelly, Robert O. Green, Raymond F. Kokaly, Longlei Li, Natalie Mahowald, Ronald L. Miller, Gregory S. Okin, Thomas H. Painter, Gregg A. Swayze, Michael Turmon, Jouni Susilouto, David Wettergreen
Publication Type Article
Publication Subtype Journal Article
Series Title Remote Sensing of Environment
Index ID 70210388
Record Source USGS Publications Warehouse
USGS Organization Geology, Geophysics, and Geochemistry Science Center