A comparison between two radiative transfer models for atmospheric correction over a wide range of wavelengths

Abstract. Atmospheric radiative transfer models (RTMs) are software tools that help researchers in understanding the radiative processes occurring in the Earth's atmosphere. Given their importance in remote sensing applications, the intercomparison of atmospheric RTMs is therefore one of the main tasks used to evaluate model performance and identify the characteristics that differ between models. This can be a tedious tasks that requires good knowledge of the model inputs/outputs and the generation of large databases of consistent simulations. With the evolution of these software tools, their increase in complexity bears implications for their use in practical applications and model intercomparison. Existing RTM-specific graphical user interfaces are not optimized for performing intercomparison studies of a wide variety of atmospheric RTMs. In this paper, we present the Atmospheric Look-up table Generator (ALG) version 2.0, a new software tool that facilitates generating large databases for a variety of atmospheric RTMs. ALG facilitates consistent and intuitive user interaction to enable the running of model executions and storing of RTM data for any spectral configuration in the optical domain. We demonstrate the utility of ALG in performing intercomparison studies of radiance simulations from broadly used atmospheric RTMs (6SV, MODTRAN, and libRadtran) through global sensitivity analysis. We expect that providing ALG to the research community will facilitate the usage of atmospheric RTMs to a wide range of applications in Earth observation.

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Neural network radiative transfer for imaging spectroscopy

Atmospheric Measurement Techniques ◽

10.5194/amt-12-2567-2019 ◽

2019 ◽

Vol 12 (4) ◽

pp. 2567-2578 ◽

Cited By ~ 4

Author(s):

Brian D. Bue ◽

David R. Thompson ◽

Shubhankar Deshpande ◽

Michael Eastwood ◽

Robert O. Green ◽

...

Keyword(s):

Radiative Transfer ◽

Infrared Imaging ◽

Atmospheric Correction ◽

Imaging Spectroscopy ◽

High Volume ◽

Lookup Table ◽

Diverse Range ◽

Physical Knowledge ◽

Radiative Transfer Models ◽

Transfer Models

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 and generate accurate radiance spectra at multiple wavelengths over a diverse range of surface and atmosphere state parameters. We also demonstrate such models can act as surrogate forward models for atmospheric correction procedures. 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.

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Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)

10.5194/gmd-2019-188 ◽

2019 ◽

Cited By ~ 1

Author(s):

Jorge Vicent ◽

Jochem Verrelst ◽

Neus Sabater ◽

Luis Alonso ◽

Juan Pablo Rivera-Caicedo ◽

...

Keyword(s):

Radiative Transfer ◽

Software Tools ◽

Radiative Processes ◽

Radiative Transfer Models ◽

Look Up Table ◽

Large Databases ◽

Wide Range ◽

Version 2.0 ◽

Atmospheric Radiative Transfer ◽

Transfer Models

Abstract. Atmospheric radiative transfer models (RTMs) are software tools that help researchers in understanding the radiative processes occurring in the Earth’s atmosphere. Given their importance in remote sensing applications, the intercomparison of atmospheric RTMs is therefore one of the main tasks to evaluate model performance and identify the characteristics that differ between models. This can be a tedious tasks that requires a good knowledge of the model inputs-outputs and generation of large databases of consistent simulations. With the evolution of these software tools, their increase in complexity bears implications towards their use in practical applications and model intercomparison. Existing RTM-specific graphical user interfaces are not optimized for performing intercomparison studies of a wide variety of atmospheric RTMs. In this paper, we present the Atmospheric Look-up table Generator (ALG) version 2.0, a new software tool that facilitates generating large databases for a variety of atmospheric RTMs. ALG facilitates consistent and intuitive user interaction to enable running model executions and storing RTM data for any spectral configuration in the optical domain. We demonstrate the utility of ALG to perform intercomparison studies and global sensitivity analysis of broadly used atmospheric RTMs (6SV, MODTRAN, libRadtran). We expect that providing ALG to the research community will facilitate the usage of atmospheric RTMs to a wide range of applications in Earth Observation.

Download Full-text

Neural Network Radiative Transfer for Imaging Spectroscopy

10.5194/amt-2018-436 ◽

2019 ◽

Author(s):

Brian D. Bue ◽

David R. Thompson ◽

Shubhankar Deshpande ◽

Michael Eastwood ◽

Robert O. Green ◽

...

Keyword(s):

Radiative Transfer ◽

Infrared Imaging ◽

Atmospheric Correction ◽

Imaging Spectroscopy ◽

High Volume ◽

Lookup Table ◽

Physical Knowledge ◽

Radiative Transfer Models ◽

Surface Atmosphere ◽

Transfer Models

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.

Download Full-text