Articles | Volume 13, issue 4
https://doi.org/10.5194/gmd-13-1945-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-13-1945-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
20 Apr 2020
Model description paper |
| 20 Apr 2020
| 20 Apr 2020
Comparative analysis of atmospheric radiative transfer models using the Atmospheric Look-up table Generator (ALG) toolbox (version 2.0)
Magellium, Toulouse, France
Image Processing Laboratory, Universitat de València, 46980 Paterna, Valencia, Spain
Jochem Verrelst
Image Processing Laboratory, Universitat de València, 46980 Paterna, Valencia, Spain
Neus Sabater
Finnish Meteorological Institute, Erik Palménin aukio 1, 00560 Helsinki, Finland
Luis Alonso
Image Processing Laboratory, Universitat de València, 46980 Paterna, Valencia, Spain
Juan Pablo Rivera-Caicedo
Secretary of Research and Graduate Studies, CONACYT-UAN, 63155 Tepic, Nayarit, Mexico
Luca Martino
Departamento de Teoría de la Señal y Comunicaciones, Universidad Rey Juan Carlos, 28943 Fuenlabrada, Madrid, Spain
Jordi Muñoz-Marí
Image Processing Laboratory, Universitat de València, 46980 Paterna, Valencia, Spain
José Moreno
Image Processing Laboratory, Universitat de València, 46980 Paterna, Valencia, Spain
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- Challenges in the atmospheric characterization for the retrieval of spectrally resolved fluorescence and PRI region dynamics from space N. Sabater et al. 10.1016/j.rse.2020.112226
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- A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data K. Berger et al. 10.3390/rs13020287
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- Radiative Transfer Model Comparison with Satellite Observations over CEOS Calibration Site Libya-4 Y. Govaerts et al. 10.3390/atmos13111759
- Retrieval of aboveground crop nitrogen content with a hybrid machine learning method K. Berger et al. 10.1016/j.jag.2020.102174
- Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within‐The‐Atmosphere Observations: A Three‐Way Street R. Kahn et al. 10.1029/2022RG000796
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- A Review of Remote Sensing of Submerged Aquatic Vegetation for Non-Specialists G. Rowan & M. Kalacska 10.3390/rs13040623
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19 citations as recorded by crossref.
- Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow J. Estévez et al. 10.3390/rs13081589
- Studying the Feasibility of Assimilating Sentinel-2 and PlanetScope Imagery into the SAFY Crop Model to Predict Within-Field Wheat Yield V. Manivasagam et al. 10.3390/rs13122395
- Atmospheric Correction Inter-comparison eXercise, ACIX-II Land: An assessment of atmospheric correction processors for Landsat 8 and Sentinel-2 over land G. Doxani et al. 10.1016/j.rse.2022.113412
- Generalized radiative transfer emulation for imaging spectroscopy reflectance retrievals P. Brodrick et al. 10.1016/j.rse.2021.112476
- Challenges in the atmospheric characterization for the retrieval of spectrally resolved fluorescence and PRI region dynamics from space N. Sabater et al. 10.1016/j.rse.2020.112226
- Multifidelity Gaussian Process Emulation for Atmospheric Radiative Transfer Models J. Vicent Servera et al. 10.1109/TGRS.2023.3300460
- A Survey of Active Learning for Quantifying Vegetation Traits from Terrestrial Earth Observation Data K. Berger et al. 10.3390/rs13020287
- Refining Atmosphere Profiles for Aerial Target Detection Models R. Grimming et al. 10.3390/s21217067
- Quantifying Fundamental Vegetation Traits over Europe Using the Sentinel-3 OLCI Catalogue in Google Earth Engine P. Reyes-Muñoz et al. 10.3390/rs14061347
- Radiative Transfer Model Comparison with Satellite Observations over CEOS Calibration Site Libya-4 Y. Govaerts et al. 10.3390/atmos13111759
- Retrieval of aboveground crop nitrogen content with a hybrid machine learning method K. Berger et al. 10.1016/j.jag.2020.102174
- Reducing Aerosol Forcing Uncertainty by Combining Models With Satellite and Within‐The‐Atmosphere Observations: A Three‐Way Street R. Kahn et al. 10.1029/2022RG000796
- Systematic Assessment of MODTRAN Emulators for Atmospheric Correction J. Vicent Servera et al. 10.1109/TGRS.2021.3071376
- Cloud-Free Global Maps of Essential Vegetation Traits Processed from the TOA Sentinel-3 Catalogue in Google Earth Engine D. Kovács et al. 10.3390/rs15133404
- Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery A. Pascual-Venteo et al. 10.3390/rs16071211
- ALiDAn: Spatiotemporal and Multiwavelength Atmospheric Lidar Data Augmentation A. Vainiger et al. 10.1109/TGRS.2022.3201436
- Multioutput Feature Selection for Emulation and Sensitivity Analysis J. Vicent Servera et al. 10.1109/TGRS.2024.3358231
- Gaussian processes retrieval of crop traits in Google Earth Engine based on Sentinel-2 top-of-atmosphere data J. Estévez et al. 10.1016/j.rse.2022.112958
- Laser Beam Atmospheric Propagation Modelling for Aerospace LIDAR Applications T. Fahey et al. 10.3390/atmos12070918
Latest update: 19 Apr 2024
Short summary
The modeling of light propagation through the atmosphere is key to process satellite images and to understand atmospheric processes. However, existing atmospheric models can be complex to use in practical applications. Here we aim at providing a new software tool to facilitate using advanced models and to generate large databases of simulated data. As a test case, we use this tool to analyze differences between several atmospheric models, showing the capabilities of this open-source tool.
The modeling of light propagation through the atmosphere is key to process satellite images and...
