Quantifying uncertainty for remote spectroscopy of surface composition

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.