David Thompson, Philip Brodrick, Niklas Bohn, Amy Braverman, Nimrod Carmon, David Connelly, Jay Fahlen, Robert Green, Robert Herman, Jonathan Hobbs, Margaret Johnson, Natalie Mahowald, Gregory Okin, Benjamin Poulter, Shawn Serbin, Alexey Shiklomanov, Jouni Susiluoto, Michael Turmon
Remote imaging spectroscopy's role in Earth science will grow in the coming decade as a series of globe-spanning spectroscopy missions launch from NASA, ESA, and other agencies. The nature of remote imaging spectroscopy will change, advancing from short regional studies to address global multi-year questions. The diversity of data will also grow with exposure to a wider range of biomes and atmospheric conditions. To execute these new investigations we must reconcile diverse observing conditions to derive consistent global maps. To this end, rigorous uncertainty quantification and propagation enables an optimal synthesis of data accounting for observing conditions and data quality. Understanding data uncertainties is also important for principled hypothesis testing, information content assessment, and informed decision making by end users. We survey prior efforts in uncertainty quantification for imaging spectroscopy, and describe methods for validating the accuracy of uncertainty predictions. We conclude with a discussion of remaining challenges and promising avenues for future research.
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