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

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Schlagwörter: Imaging spectroscopy; Hyperspectral imaging; Uncertainty quantification
 Zusammenfassung: 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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 Datum: 20212021
 Publikationsstatus: Final veröffentlicht
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 Identifikatoren: DOI: 10.1016/j.rse.2021.112432
GFZPOF: p4 T5 Future Landscapes
OATYPE: Green Open Access
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Titel: Remote Sensing of Environment
Genre der Quelle: Zeitschrift, SCI, Scopus
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Seiten: - Band / Heft: 260 Artikelnummer: 112432 Start- / Endseite: - Identifikator: CoNE: https://gfzpublic.gfz-potsdam.de/cone/journals/resource/journals427
Publisher: Elsevier