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  Scene invariants for quantifying radiative transfer uncertainty

Thompson, D. R., Bohn, N., Braverman, A., Brodrick, P. G., Carmon, N., Eastwood, M. L., Fahlen, J. E., Green, R. O., Johnson, M. C., Roberts, D. A., Susiluoto, J. (2021): Scene invariants for quantifying radiative transfer uncertainty. - Remote Sensing of Environment, 260, 112432.
https://doi.org/10.1016/j.rse.2021.112432

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 Creators:
Thompson, David R.1, Author
Bohn, Niklas2, Author              
Braverman, Amy1, Author
Brodrick, Philip G.1, Author
Carmon, Nimrod1, Author
Eastwood, Michael L.1, Author
Fahlen, Jay E.1, Author
Green, Robert O.1, Author
Johnson, Margaret C.1, Author
Roberts, Dar A.1, Author
Susiluoto, Jouni1, Author
Affiliations:
1External Organizations, ou_persistent22              
21.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, ou_146028              

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Free keywords: Imaging spectroscopy; Hyperspectral imaging; Uncertainty quantification
 Abstract: Remote imaging spectroscopy, also known as hyperspectral imaging, uses Radiative Transfer Models (RTMs) to predict the measured radiance spectrum for a specific surface and atmospheric state. Discrepancies between RTM assumptions and physical reality can cause systematic errors in surface property estimates. We present a statistical method to quantify these model errors without invoking ground reference data. Our approach exploits scene invariants — properties of the environment which are stable over space or time — to estimate RTM discrepancies. We describe techniques for discovering these features opportunistically in flight data. We then demonstrate data-driven methods that estimate the aggregate errors due to model discrepancies without having to explicitly identify the underlying physical mechanisms. The resulting distributions can improve posterior uncertainty predictions in operational retrievals.

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 Dates: 20212021
 Publication Status: Finally published
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1016/j.rse.2021.112432
GFZPOF: p4 T5 Future Landscapes
OATYPE: Green Open Access
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Title: Remote Sensing of Environment
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 260 Sequence Number: 112432 Start / End Page: - Identifier: CoNE: https://gfzpublic.gfz-potsdam.de/cone/journals/resource/journals427
Publisher: Elsevier