Quantifying the Uncertainty in Remote Spectroscopy of Surface Composition

Remote surface measurements by imaging spectrometers play an important role in planetary and Earth science. To make thesemeasurements, investigators calibrate instrument data to absolute units, invert physical models to estimate atmospheric effects, andthen determine surface properties from the spectral reflectance. This study quantifies the uncertainty in this process. Globalmissions 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 analogueof Earth and planetary exploration. We consider rover transects at Cuprite, Nevada, and remote observations by NASA’s NextGeneration Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). We show that accounting for input uncertainties canbenefit mineral detection methods such as constrained spectrum fitting. This suggests that operational uncertainty estimates couldimprove future NASA missions like the Earth Mineral dust source InvesTigation (EMIT) and the Lunar Trailblazer mission, as wellas NASA’s Decadal Surface Biology and Geology (SBG) Investigation.