Item talk:Q253483
From geokb
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"name": "Quantifying uncertainty for remote spectroscopy of surface composition",
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"abstract": "Remote surface measurements by imaging spectrometers play an important role in planetary and Earth science.\nTo make these measurements, investigators calibrate instrument data to absolute units, invert physical models to\nestimate atmospheric effects, and then determine surface properties from the spectral reflectance. This study\nquantifies the uncertainty in this process. Global missions demand predictive uncertainty models that can estimate\nfuture errors for varied environments and observing conditions. Here we validate uncertainty predictions\nwith remote surface composition retrievals and in situ measurements in a field analogue of Earth and planetary\nexploration. We consider rover transects at Cuprite, Nevada, and remote observations by NASA's Next-\nGeneration Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). We show that accounting for input\nuncertainties can benefit mineral detection methods such as constrained spectrum fitting. This suggests that\noperational uncertainty estimates could improve future NASA missions like the Earth Mineral dust source\nInvesTigation (EMIT) and the Lunar Trailblazer mission, as well as NASA's Decadal Surface Biology and Geology (SBG) Investigation.",
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