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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2018-436
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2018-436
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 18 Jan 2019

Research article | 18 Jan 2019

Review status
This discussion paper is a preprint. A revision of this manuscript was accepted for the journal Atmospheric Measurement Techniques (AMT) and is expected to appear here in due course.

Neural Network Radiative Transfer for Imaging Spectroscopy

Brian D. Bue1, David R. Thompson1, Shubhankar Deshpande2, Michael Eastwood1, Robert O. Green1, Terry Mullen3, Vijay Natraj1, and Mario Parente3 Brian D. Bue et al.
  • 1Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 2The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
  • 3University of Massachusetts, Amherst, MA, USA

Abstract. Visible/Shortwave InfraRed imaging spectroscopy provides valuable remote measurements of Earth's surface and atmospheric properties. These measurements generally rely on inversions of computationally-intensive Radiative Transfer Models (RTMs). RTMs' computational expense makes them difficult to use with high volume imaging spectrometers, and forces approximations such as lookup table interpolation and surface/atmosphere decoupling. These compromises limit the accuracy and flexibility of the remote retrieval; dramatic speed improvements in radiative transfer models could significantly improve the utility and interpretability of remote spectroscopy for Earth science. This study demonstrates that nonparametric function approximation with neural networks can replicate Radiative Transfer calculations over a relevant range of surface/atmosphere parameters. Incorporating physical knowledge into the network structure provides improved interpretability and model efficiency. We evaluate the approach in atmospheric correction of data from the PRISM airborne imaging spectrometer, and demonstrate accurate emulation of radiative transfer calculations which run several orders of magnitude faster than first-principles models. These results are particularly amenable to iterative spectrum fitting approaches, providing analytical benefits including statistically rigorous treatment of uncertainty and the potential to recover information on spectrally-broad signals.

Brian D. Bue et al.
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Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Brian D. Bue et al.
Model code and software

dsmbgu8/isofit: Neural Network Radiative Transfer Emulation for ISOFIT D. R. Thompson and B. D. Bue https://doi.org/10.5281/zenodo.2542963

Brian D. Bue et al.
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Short summary
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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