Quantifying uncertainty for remote spectroscopy of surface composition
| Autor: | D. S. Connelly, Raymond F. Kokaly, Michael Turmon, Robert O. Green, Thomas H. Painter, Gregory S. Okin, Natalie M. Mahowald, Alberto Candela, Philip G. Brodrick, Gregg A. Swayze, Jouni Susilouto, Ron L. Miller, Roger N. Clark, Longlei Li, David Wettergreen, Amy Braverman, Nimrod Carmon, David R. Thompson |
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| Rok vydání: | 2020 |
| Předmět: |
Surface (mathematics)
Physical model 010504 meteorology & atmospheric sciences Spectrometer Instrument Data 0208 environmental biotechnology Soil Science Geology 02 engineering and technology Mineral dust 01 natural sciences Reflectivity Physics::Geophysics 020801 environmental engineering Physics::Space Physics Airborne visible/infrared imaging spectrometer Environmental science Astrophysics::Earth and Planetary Astrophysics Computers in Earth Sciences Spectroscopy 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Remote Sensing of Environment. 247:111898 |
| ISSN: | 0034-4257 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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