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AMT | Articles | Volume 12, issue 4
Atmos. Meas. Tech., 12, 2567–2578, 2019
https://doi.org/10.5194/amt-12-2567-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 12, 2567–2578, 2019
https://doi.org/10.5194/amt-12-2567-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 May 2019

Research article | 02 May 2019

Neural network radiative transfer for imaging spectroscopy

Brian D. Bue et al.
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Asner, G., Martin, R., Knapp, D., Tupayachi, R., Anderson, C., Sinca, F., Vaughn, N., and Llactayo, W.: Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation, Science, 355, 385–389, 2017. a
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Buehler, S. A., John, V. O., Kottayil, A., Milz, M., and Eriksson, P.: ARTICLE IN PRESS, J. Quant. Spectr. Ra., 111, 602–615, https://doi.org/10.1016/j.jqsrt.2009.10.018, 2009. a
Emde, C., Buras-Schnell, R., Kylling, A., Mayer, B., Gasteiger, J., Hamann, U., Kylling, J., Richter, B., Pause, C., Dowling, T., and Bugliaro, L.: The libRadtran software package for radiative transfer calculations (version 2.0.1), Geosci. Model Dev., 9, 1647–1672, https://doi.org/10.5194/gmd-9-1647-2016, 2016. a
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Imaging spectrometers provide valuable remote measurements of Earth's surface and atmosphere. These measurements rely on computationally expensive radiative transfer models (RTMs). Spectrometers produce too much data to process with RTMs directly, requiring approximations that trade accuracy for speed. We demonstrate that neural networks can quickly emulate RTM calculations more accurately than current approaches, enabling the application of more sophisticated RTMs than current methods permit.
Imaging spectrometers provide valuable remote measurements of Earth's surface and atmosphere....
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