Articles | Volume 12, issue 4
https://doi.org/10.5194/amt-12-2567-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-12-2567-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 May 2019
Research article |
| 02 May 2019
| 02 May 2019
Neural network radiative transfer for imaging spectroscopy
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
David R. Thompson
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Shubhankar Deshpande
The Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
Michael Eastwood
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Robert O. Green
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Vijay Natraj
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Terry Mullen
University of Massachusetts, Amherst, MA, USA
Mario Parente
University of Massachusetts, Amherst, MA, USA
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26 citations as recorded by crossref.
- Radiative Transfer Speed-Up Combining Optimal Spectral Sampling With a Machine Learning Approach S. Mauceri et al. https://doi.org/10.3389/frsen.2022.932548
- A unified approach to estimate land and water reflectances with uncertainties for coastal imaging spectroscopy D. Thompson et al. https://doi.org/10.1016/j.rse.2019.05.017
- Use of A Neural Network-Based Ocean Body Radiative Transfer Model for Aerosol Retrievals from Multi-Angle Polarimetric Measurements C. Fan et al. https://doi.org/10.3390/rs11232877
- Emulation-based self-supervised SIF retrieval in the O2-A absorption band with HyPlant J. Buffat et al. https://doi.org/10.1016/j.rse.2025.115203
- RTM Surrogate Modeling in Optical Remote Sensing: A Review of Emulation for Vegetation and Atmosphere Applications J. Verrelst et al. https://doi.org/10.3390/rs17213618
- Reconstructing 10-km-resolution direct normal irradiance dataset through a hybrid algorithm J. Wu et al. https://doi.org/10.1016/j.rser.2024.114805
- Benefits of Stochastic Weight Averaging in Developing Neural Network Radiation Scheme for Numerical Weather Prediction H. Song et al. https://doi.org/10.1029/2021MS002921
- Optimal estimation framework for ocean color atmospheric correction and pixel-level uncertainty quantification A. Ibrahim et al. https://doi.org/10.1364/AO.461861
- Multifidelity Gaussian Process Emulation for Atmospheric Radiative Transfer Models J. Vicent Servera et al. https://doi.org/10.1109/TGRS.2023.3300460
- Emulation of Sun-Induced Fluorescence from Radiance Data Recorded by the HyPlant Airborne Imaging Spectrometer M. Morata et al. https://doi.org/10.3390/rs13214368
- Scene invariants for quantifying radiative transfer uncertainty D. Thompson et al. https://doi.org/10.1016/j.rse.2021.112432
- A Deep-Learning-Based Microwave Radiative Transfer Emulator for Data Assimilation and Remote Sensing X. Liang et al. https://doi.org/10.1109/JSTARS.2022.3210491
- Neural Network Emulation of Synthetic Hyperspectral Sentinel-2-Like Imagery With Uncertainty M. Morata et al. https://doi.org/10.1109/JSTARS.2022.3231380
- Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 2: Model Architecture and Assessment X. Liang & Q. Liu https://doi.org/10.3390/rs12223825
- Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data V. Sagan et al. https://doi.org/10.1109/TGRS.2021.3091409
- Physics constraint Deep Learning based radiative transfer model Q. Liu & X. Liang https://doi.org/10.1364/OE.493818
- A radiative transfer deep learning model coupled into WRF with a generic fortran torch adaptor B. Mu et al. https://doi.org/10.3389/feart.2023.1149566
- Estimating global downward shortwave radiation from VIIRS data using a transfer-learning neural network R. Li et al. https://doi.org/10.1016/j.rse.2022.112999
- Multi-Fidelity Emulation of Atmospheric Correction Coefficients with Physics-Guided Kolmogorov–Arnold Networks M. Mazid & N. Rishe https://doi.org/10.3390/rs18111826
- Generalized radiative transfer emulation for imaging spectroscopy reflectance retrievals P. Brodrick et al. https://doi.org/10.1016/j.rse.2021.112476
- UVBoost: An erythemal weighted ultraviolet radiation estimator based on a machine learning gradient boosting algorithm M. Corrêa https://doi.org/10.1016/j.jqsrt.2023.108490
- Enhanced RF–GAM for Visibility Nowcasting and PM Forecasting: A Case Study in Hefei, China Z. Zhang et al. https://doi.org/10.1007/s41810-026-00407-7
- Atmospheric Lengthscales for Global VSWIR Imaging Spectroscopy D. Thompson et al. https://doi.org/10.1029/2021JG006711
- Discrete Ordinate Radiative Transfer Model With the Neural Network Based Eigenvalue Solver: proof Of Concept D. Efremenko https://doi.org/10.33383/2020-075
- Two Machine Learning Based Schemes for Solving Direct and Inverse Problems of Radiative Transfer Theory D. Efremenko et al. https://doi.org/10.51130/graphicon-2020-2-3-45
- Accurate Machine-learning Atmospheric Retrieval via a Neural-network Surrogate Model for Radiative Transfer M. Himes et al. https://doi.org/10.3847/PSJ/abe3fd
26 citations as recorded by crossref.
- Radiative Transfer Speed-Up Combining Optimal Spectral Sampling With a Machine Learning Approach S. Mauceri et al. https://doi.org/10.3389/frsen.2022.932548
- A unified approach to estimate land and water reflectances with uncertainties for coastal imaging spectroscopy D. Thompson et al. https://doi.org/10.1016/j.rse.2019.05.017
- Use of A Neural Network-Based Ocean Body Radiative Transfer Model for Aerosol Retrievals from Multi-Angle Polarimetric Measurements C. Fan et al. https://doi.org/10.3390/rs11232877
- Emulation-based self-supervised SIF retrieval in the O2-A absorption band with HyPlant J. Buffat et al. https://doi.org/10.1016/j.rse.2025.115203
- RTM Surrogate Modeling in Optical Remote Sensing: A Review of Emulation for Vegetation and Atmosphere Applications J. Verrelst et al. https://doi.org/10.3390/rs17213618
- Reconstructing 10-km-resolution direct normal irradiance dataset through a hybrid algorithm J. Wu et al. https://doi.org/10.1016/j.rser.2024.114805
- Benefits of Stochastic Weight Averaging in Developing Neural Network Radiation Scheme for Numerical Weather Prediction H. Song et al. https://doi.org/10.1029/2021MS002921
- Optimal estimation framework for ocean color atmospheric correction and pixel-level uncertainty quantification A. Ibrahim et al. https://doi.org/10.1364/AO.461861
- Multifidelity Gaussian Process Emulation for Atmospheric Radiative Transfer Models J. Vicent Servera et al. https://doi.org/10.1109/TGRS.2023.3300460
- Emulation of Sun-Induced Fluorescence from Radiance Data Recorded by the HyPlant Airborne Imaging Spectrometer M. Morata et al. https://doi.org/10.3390/rs13214368
- Scene invariants for quantifying radiative transfer uncertainty D. Thompson et al. https://doi.org/10.1016/j.rse.2021.112432
- A Deep-Learning-Based Microwave Radiative Transfer Emulator for Data Assimilation and Remote Sensing X. Liang et al. https://doi.org/10.1109/JSTARS.2022.3210491
- Neural Network Emulation of Synthetic Hyperspectral Sentinel-2-Like Imagery With Uncertainty M. Morata et al. https://doi.org/10.1109/JSTARS.2022.3231380
- Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 2: Model Architecture and Assessment X. Liang & Q. Liu https://doi.org/10.3390/rs12223825
- Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data V. Sagan et al. https://doi.org/10.1109/TGRS.2021.3091409
- Physics constraint Deep Learning based radiative transfer model Q. Liu & X. Liang https://doi.org/10.1364/OE.493818
- A radiative transfer deep learning model coupled into WRF with a generic fortran torch adaptor B. Mu et al. https://doi.org/10.3389/feart.2023.1149566
- Estimating global downward shortwave radiation from VIIRS data using a transfer-learning neural network R. Li et al. https://doi.org/10.1016/j.rse.2022.112999
- Multi-Fidelity Emulation of Atmospheric Correction Coefficients with Physics-Guided Kolmogorov–Arnold Networks M. Mazid & N. Rishe https://doi.org/10.3390/rs18111826
- Generalized radiative transfer emulation for imaging spectroscopy reflectance retrievals P. Brodrick et al. https://doi.org/10.1016/j.rse.2021.112476
- UVBoost: An erythemal weighted ultraviolet radiation estimator based on a machine learning gradient boosting algorithm M. Corrêa https://doi.org/10.1016/j.jqsrt.2023.108490
- Enhanced RF–GAM for Visibility Nowcasting and PM Forecasting: A Case Study in Hefei, China Z. Zhang et al. https://doi.org/10.1007/s41810-026-00407-7
- Atmospheric Lengthscales for Global VSWIR Imaging Spectroscopy D. Thompson et al. https://doi.org/10.1029/2021JG006711
- Discrete Ordinate Radiative Transfer Model With the Neural Network Based Eigenvalue Solver: proof Of Concept D. Efremenko https://doi.org/10.33383/2020-075
- Two Machine Learning Based Schemes for Solving Direct and Inverse Problems of Radiative Transfer Theory D. Efremenko et al. https://doi.org/10.51130/graphicon-2020-2-3-45
- Accurate Machine-learning Atmospheric Retrieval via a Neural-network Surrogate Model for Radiative Transfer M. Himes et al. https://doi.org/10.3847/PSJ/abe3fd
Saved (final revised paper)
Latest update: 14 Jul 2026
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....
