Articles | Volume 12, issue 4
https://doi.org/10.5194/amt-12-2567-2019
https://doi.org/10.5194/amt-12-2567-2019
Research article
 | 
02 May 2019
Research article |  | 02 May 2019

Neural network radiative transfer for imaging spectroscopy

Brian D. Bue, David R. Thompson, Shubhankar Deshpande, Michael Eastwood, Robert O. Green, Vijay Natraj, Terry Mullen, and Mario Parente

Download

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

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Anna Wenzel on behalf of the Authors (25 Mar 2019)  Author's response
ED: Publish subject to technical corrections (26 Mar 2019) by Lars Hoffmann
AR by Brian Bue on behalf of the Authors (27 Mar 2019)  Author's response    Manuscript

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Brian Bue on behalf of the Authors (17 Apr 2019)   Author's adjustment   Manuscript
EA: Adjustments approved (17 Apr 2019) by Lars Hoffmann
Download
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.