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 sp...

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Bibliographic Details
Published in:Remote sensing of environment 2020-09, Vol.247, p.111898, Article 111898
Main Authors: Thompson, David R., Braverman, Amy, Brodrick, Philip G., Candela, Alberto, Carmon, Nimrod, Clark, Roger N., Connelly, David, Green, Robert O., Kokaly, Raymond F., Li, Longlei, Mahowald, Natalie, Miller, Ronald L., Okin, Gregory S., Painter, Thomas H., Swayze, Gregg A., Turmon, Michael, Susilouto, Jouni, Wettergreen, David S.
Format: Article
Language:English
Subjects:
Atmospheric correction
Atmospheric effects
Atmospheric models
AVIRIS
Calibration visible-shortwave infrared
Composition
Cuprite Nevada
Earth
Geologic mapping
Geology
Hyperspectral imagers
Imaging spectrometers
Imaging spectroscopy
In situ measurement
Infrared imaging
Infrared spectrometers
Lunar dust
Reflectance
Remote observing
Space exploration
Space missions
Spectral reflectance
Spectrometers
Spectroscopy
Statistical methods
Surface properties
Uncertainty
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Summary: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. •New remote imaging spectroscopy missions will measure global surface composition•Such missions require comprehensive uncertainty models to predict inversion errors•We track uncertainty for calibration, atmospheric inversion, and mineral retrieval•We evaluate model performance in a field experiment demonstrating mineral detection•Predicted uncertainties agree stataistically with remote/in-situ discrepancies
ISSN:0034-4257
1879-0704
DOI:10.1016/j.rse.2020.111898