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Journal Article

Scene invariants for quantifying radiative transfer uncertainty


Thompson,  David R.
External Organizations;


Bohn,  Niklas
1.4 Remote Sensing, 1.0 Geodesy, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum;

Braverman,  Amy
External Organizations;

Brodrick,  Philip G.
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Carmon,  Nimrod
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Eastwood,  Michael L.
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Fahlen,  Jay E.
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Green,  Robert O.
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Johnson,  Margaret C.
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Roberts,  Dar A.
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Susiluoto,  Jouni
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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.

Cite as: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5006586
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.