Quantifying uncertainty for remote spectroscopy of surface composition

David R. Thompson*, Amy Braverman, Philip G. 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 S. Wettergreen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number111898
JournalRemote Sensing of Environment
StatePublished - 15 Sep 2020
Externally publishedYes


  • Atmospheric correction
  • Calibration visible-shortwave infrared
  • Cuprite Nevada
  • Geologic mapping
  • Hyperspectral imagers
  • Imaging spectroscopy
  • Statistical methods


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