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.
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
Related authors
No articles found.
Antti Kukkurainen, Antti Mikkonen, Antti Arola, Antti Lipponen, Ville Kolehmainen, and Neus Sabater
Geosci. Model Dev., 18, 7529–7544, https://doi.org/10.5194/gmd-18-7529-2025, https://doi.org/10.5194/gmd-18-7529-2025, 2025
Short summary
Short summary
HAPI2LIBIS is a new software tool that enhances the capabilities of the radiative transfer model libRadtran. It simplifies high-wavelength-resolution simulations by using up-to-date molecular data from the HITRAN (High-Resolution Transmission Molecular Absorption) database and streamlining computations. This tool helps researchers analyze how gases interact with radiation in the Earth's atmosphere and at the surface, improving atmospheric studies and satellite observations and making detailed modeling more accurate and accessible.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
Short summary
Short summary
When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
Lammert Kooistra, Katja Berger, Benjamin Brede, Lukas Valentin Graf, Helge Aasen, Jean-Louis Roujean, Miriam Machwitz, Martin Schlerf, Clement Atzberger, Egor Prikaziuk, Dessislava Ganeva, Enrico Tomelleri, Holly Croft, Pablo Reyes Muñoz, Virginia Garcia Millan, Roshanak Darvishzadeh, Gerbrand Koren, Ittai Herrmann, Offer Rozenstein, Santiago Belda, Miina Rautiainen, Stein Rune Karlsen, Cláudio Figueira Silva, Sofia Cerasoli, Jon Pierre, Emine Tanır Kayıkçı, Andrej Halabuk, Esra Tunc Gormus, Frank Fluit, Zhanzhang Cai, Marlena Kycko, Thomas Udelhoven, and Jochem Verrelst
Biogeosciences, 21, 473–511, https://doi.org/10.5194/bg-21-473-2024, https://doi.org/10.5194/bg-21-473-2024, 2024
Short summary
Short summary
We reviewed optical remote sensing time series (TS) studies for monitoring vegetation productivity across ecosystems. Methods were categorized into trend analysis, land surface phenology, and assimilation into statistical or dynamic vegetation models. Due to progress in machine learning, TS processing methods will diversify, while modelling strategies will advance towards holistic processing. We propose integrating methods into a digital twin to improve the understanding of vegetation dynamics.
S. Hamzeh, M. Hajeb, S. K. Alavipanah, and J. Verrelst
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4-W1-2022, 271–277, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-271-2023, https://doi.org/10.5194/isprs-annals-X-4-W1-2022-271-2023, 2023
Antti Lipponen, Ville Kolehmainen, Pekka Kolmonen, Antti Kukkurainen, Tero Mielonen, Neus Sabater, Larisa Sogacheva, Timo H. Virtanen, and Antti Arola
Atmos. Meas. Tech., 14, 2981–2992, https://doi.org/10.5194/amt-14-2981-2021, https://doi.org/10.5194/amt-14-2981-2021, 2021
Short summary
Short summary
We have developed a new computational method to post-process-correct the satellite aerosol retrievals. The proposed method combines the conventional satellite aerosol retrievals relying on physics-based models and machine learning. The results show significantly improved accuracy in the aerosol data over the operational satellite data products. The correction can be applied to the existing satellite aerosol datasets with no need to fully reprocess the much larger original radiance data.
Cited articles
Abramowitz, M. and Stegun, I.: Handbook of Mathematical Functions, in: Applied
Mathematics Series, Volume 55, chap. 25.2, National Bureau of Standards,
Washington, USA, 1964. a
Barber, C., Dobkin, D., and Huhdanpaa, H.: The Quickhull Algorithm for Convex
Hulls, ACM Trans. Math. Softw., 22, 469–483, 1996. a
Bartels, R. H., Beatty, J. C., and Barsky, B. A.: Hermite and Cubic Spline Interpolation,
in: An Introduction to Splines for Use in Computer Graphics and Geometric
Modelling, 2nd ed., chap. 3, 9–17, Morgan Kaufmann, San Francisco, CA
(USA), 1998. a
Berk, A. and Hawes, F.: Validation of MODTRAN6 and its line-by-line
algorithm, J. Quant. Spectrosc. Rad. Transf., 203,
542–556, 2017. a
Berk, A., Anderson, G., Acharya, P., Bernstein, L., Muratov, L., Lee, J., Fox,
M., Adler-Golden, S., Chetwynd, J., Hoke, M., Lockwood, R., Gardner, J.,
Cooley, T., Borel, C., Lewis, P., and Shettle, E.:
MODTRAN™5: 2006 update, P. Soc. Photo.-Opt. Ins., 6233 II, https://doi.org/10.1117/12.665077, 2006. a, b, c
Berk, A., Conforti, P., Kennett, R., Perkins, T., Hawes, F., and Van Den Bosch,
J.: MODTRAN6: A major upgrade of the MODTRAN radiative transfer code,
P. Soc. Photo.-Opt. Ins.,
9088, https://doi.org/10.1117/12.2050433, 2014. a, b, c
Berk, A., Conforti, P., and Hawes, F.: An accelerated line-by-line option for
MODTRAN combining on-thefly generation of line center absorption within 0.1
cm-1 bins and pre-computed line tails, P. Soc. Photo.-Opt. Ins., 9472,
https://doi.org/10.1117/12.2177444, 2015. a
Berk, A., Bosch, J., Hawes, F., Perkins, T., Conforti, P. F., Anderson, G. P., Kennett, R. G., and Achary, P. K.: MODTRAN6.0 User's Manual, Spectral Sciences, Inc., Contract No. FA9453-12-C0262; Data Itel A007, 193 pp., 2017. a
Bratley, P. and Fox, B.: Algorithm 659 Implementing Sobol's Quasirandom
Sequence Generator, ACM Trans. Mathe. Softw., 14, 88–100,
1988. a
Brazile, J., Richter, R., Schläpfer, D., Schaepman, M., and Itten, K.:
Cluster versus grid for operational generation of ATCOR's modtran-based look
up tables, Parall. Comput., 34, 32–46, 2008. a
Callieco, F. and Dell'Acqua, F.: A comparison between two radiative transfer
models for atmospheric correction over a wide range of wavelengths,
Int. J. Remote Sens., 32, 1357–1370, 2011. a
Cooley, T., Anderson, G., Felde, G., Hoke, M., Ratkowski, A., Chetwynd, J.,
Gardner, J., Adler-Golden, S., Matthew, M., Berk, A., Bernstein, L., Acharya,
P., Miller, D., and Lewis, P.: FLAASH, a MODTRAN4-based atmospheric
correction algorithm, its applications and validation, in: International
Geoscience and Remote Sensing Symposium (IGARSS), vol. 3, 1414–1418,
2002. a
Debaecker, V., Louisand, J., Müller-Wilm, U., and Gascon, F.: Generation of
Look-Up-Tables for the atmospheric correction module of Sentinel-2 Level 2A
processor (Sen2Cor) using libRadtran and comparison with MODTRAN, in: Living
Planet Symposium, vol. 1909, 2016. a
Delaunay, B.: Sur la sphère vide. A la mémoire de Georges Voronoï,
Bulletin de l'Académie des Sciences de l'URSS. Classe des sciences
mathématiques et na, 793–800, 1934. a
Dubovik, O. and King, M.: A flexible inversion algorithm for retrieval of
aerosol optical properties from Sun and sky radiance measurements, J.
Geophys. Res.-Atmos., 105, 20673–20696, 2000. a
Dubovik, O., Holben, B., Eck, T., Smirnov, A., Kaufman, Y., King, M., Tanré,
D., and Slutsker, I.: Variability of absorption and optical properties of key
aerosol types observed in worldwide locations, J. Atmos.
Sci., 59, 590–608, 2002. a
Dubovik, O., Herman, M., Holdak, A., Lapyonok, T., Tanré, D., Deuzé, J. L., Ducos, F., Sinyuk, A., and Lopatin, A.: Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations, Atmos. Meas. Tech., 4, 975–1018, https://doi.org/10.5194/amt-4-975-2011, 2011. a
El Hajj, M., Bégué, A., Lafrance, B., Hagolle, O., Dedieu, G., and Rumeau,
M.: Relative radiometric normalization and atmospheric correction of a SPOT
5 time series, Sensors, 8, 2774–2791, 2008. a
Emde, C., Buras-Schnell, R., Kylling, A., Mayer, B., Gasteiger, J., Hamann, U., Kylling, J., Richter, B., Pause, C., Dowling, T., and Bugliaro, L.: The libRadtran software package for radiative transfer calculations (version 2.0.1), Geosci. Model Dev., 9, 1647–1672, https://doi.org/10.5194/gmd-9-1647-2016, 2016. a, b, c
Fell, F. and Fischer, J.: Numerical simulation of the light field in the
atmosphere-ocean system using the matrix-operator method, J.
Quant. Spectrosc. Ra. Transf., 69, 351–388,
https://doi.org/10.1016/S0022-4073(00)00089-3, 2001. a
Forster, P., Fomichev, V., Rozanov, E., Cagnazzo, C., Jonsson, A., Langematz,
U., Fomin, B., Iacono, M., Mayer, B., Mlawer, E., Myhre, G., Portmann, R.,
Akiyoshi, H., Falaleeva, V., Gillett, N., Karpechko, A., Li, J., Lemennais,
P., Morgenstern, O., Oberländer, S., Sigmond, M., and Shibata, K.:
Evaluation of radiation scheme performance within chemistry climate models,
J. Geophys. Res.-Atmos., 116, D10302, https://doi.org/10.1029/2010JD015361, 2011. a
Free Software Foundation, Inc: GNU Operating System official website,
available at: http://www.gnu.org/licenses/ (last access: 15 April 2020), 2018. a
Gasteiger, J. and Wiegner, M.: MOPSMAP v1.0: a versatile tool for the modeling of aerosol optical properties, Geosci. Model Dev., 11, 2739–2762, https://doi.org/10.5194/gmd-11-2739-2018, 2018. a
Gasteiger, J., Emde, C., Mayer, B., Buras, R., Buehler, S., and Lemke, O.:
Representative wavelengths absorption parameterization applied to satellite
channels and spectral bands, J. Quant. Spectrosc.
Rad. Transf., 148, 99–115, 2014. a
Gastellu-Etchegorry, J., Gascon, F., and Esteve, P.: An interpolation procedure
for generalizing a look-up table inversion method, Remote Sens.
Environ., 87, 55–71, 2003. a
Goody, R., West, R., Chen, L., and Crisp, D.: The correlated-k method for
radiation calculations in nonhomogeneous atmospheres, J. Quant.
Spectrosc. Ra. Transf., 42, 539–550, 1989. a
Hess, M., Koepke, P., and Schult, I.: Optical Properties of Aerosols and
Clouds: The Software Package OPAC, B. Am. Meteorol.
Soc., 79, 831–844, 1998. a
Huang, F., Zhou, J., Tao, J., Tan, X., Liang, S., and Cheng, J.: PMODTRAN: a
parallel implementation based on MODTRAN for massive remote sensing data
processing, Int. J. Dig. Earth, 9, 819–834, 2016. a
Huang, R.-J., Zhang, Y., Bozzetti, C., Ho, K.-F., Cao, J.-J., Han, Y.,
Daellenbach, K., Slowik, J., Platt, S., Canonaco, F., Zotter, P., Wolf, R.,
Pieber, S., Bruns, E., Crippa, M., Ciarelli, G., Piazzalunga, A.,
Schwikowski, M., Abbaszade, G., Schnelle-Kreis, J., Zimmermann, R., An, Z.,
Szidat, S., Baltensperger, U., El Haddad, I., and Prévot, A.: High
secondary aerosol contribution to particulate pollution during haze events in China, Nature, 514, 218–222, 2015. a
Iacono, M., Delamere, J., Mlawer, E., Shephard, M., Clough, S., and Collins,
W.: Radiative forcing by long-lived greenhouse gases: Calculations with the
AER radiative transfer models, J. Geophys. Res.-Atmos.,
113, D13103, https://doi.org/10.1029/2008JD009944, 2008. a
Isaacs, R. G., Wang, W.-C., Worsham, R. D., and Goldenberg, S.: Multiple
scattering lowtran and fascode models, Appl. Opt., 26, 1272–1281, 1987. a
Jacquemoud, S., Verhoef, W., Baret, F., Bacour, C., Zarco-Tejada, P., Asner,
G., François, C., and Ustin, S.: PROSPECT + SAIL models: A review of
use for vegetation characterization, Remote Sens. Environ., 113,
S56–S66, 2009. a
Kocis, L. and Whiten, W.: Computational Investigations of Low-Discrepancy
Sequences, ACM Trans. Mathe. Softw., 23, 266–294, 1988. a
Koepke, P., Gasteiger, J., and Hess, M.: Technical Note: Optical properties of desert aerosol with non-spherical mineral particles: data incorporated to OPAC, Atmos. Chem. Phys., 15, 5947–5956, https://doi.org/10.5194/acp-15-5947-2015, 2015. a
Kotchenova, S. and Vermote, E.: Validation of a vector version of the 6S
radiative transfer code for atmospheric correction of satellite data. Part
II: Homogeneous Lambertian and anisotropic surfaces, Appl. Opt., 46,
4455–4464, 2007. a
Kotchenova, S., Vermote, E., Matarrese, R., and Klemm Jr., F.: Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: Path radiance, Appl. Opt., 45, 6762–6774, 2006. a
Lenoble, J.: Radiative transfer in scattering and absorbing atmospheres:
Standard computational procedures, A. Deepak Publishing, Vol. 300, Hampton,
VA, USA, 1985. a
Liu, J., Pattey, E., and Jégo, G.: Assessment of vegetation indices for
regional crop green LAI estimation from Landsat images over multiple growing
seasons, Remote Sens. Environ., 123, 347–358, 2012. a
Matarrese, R., Roger, J.-C., Kotchenova, S., Morcrette, J., Tanré, D.,
Deuzé, J., Herman, M., and Vermote, E.: MODIS Land Surface Reflectance – Science Computing Facility, University of Maryland, available at: http://6s.ltdri.org/ (last access: 8 April 2018), 2015. a
Matthew, M., Adler-Golden, S., Berk, A., Richtsmeier, S., Levine, R.,
Bernstein, L., Acharya, P., Anderson, G., Felde, G., Hoke, M., Ratkowski, A.,
Burke, H., Kaiser, R., and Miller, D.: Status of atmospheric correction
using a MODTRAN4-based algorithm, Proc. SPIE – Algorithms for Multispectral,
Hyperspectral, and Ultraspectral Imagery VI, 4049, https://doi.org/10.1117/12.410341, 2000. a
Matthews, M., Bernard, S., and Winter, K.: Remote sensing of
cyanobacteria-dominant algal blooms and water quality parameters in
Zeekoevlei, a small hypertrophic lake, using MERIS, Remote Sens.
Environ., 114, 2070–2087, https://doi.org/10.1016/j.rse.2010.04.013, 2010. a
Mayer, B. and Kylling, A.: Technical note: The libRadtran software package for radiative transfer calculations - description and examples of use, Atmos. Chem. Phys., 5, 1855–1877, https://doi.org/10.5194/acp-5-1855-2005, 2005. a, b
Mayer, B., Emde, C., Gasteiger, J., and Kylling, A.: LibRadtran website,
availble at: http://www.libradtran.org/ (last access: 8 April 2018), 2017. a
McKay, M., Beckman, R., and Conover, W.: Comparison of three methods for
selecting values of input variables in the analysis of output from a computer
code, Technometrics, 21, 239–245, 1979. a
North, P., Brockmann, C., Fischer, J., Gomez-Chova, L., Grey, W., Heckel, A.,
Moreno, J., Preusker, R., and Regner, P.: MERIS/AATSR synergy algorithms for
cloud screening, aerosol retrieval and atmospheric correction, in: European
Space Agency, (Special Publication) ESA SP, 666 SP, 2008. a
Pedrós, R., Gómez-Amo, J., Marcos, C., Utrillas, M., Gandía, S., Tena,
F., and Lozano, J. M.: AEROgui: A Graphical User Interface for the Optical
Properties of Aerosols, B. Am. Meteorol. Soc., 95,
1863–1871, 2014. a
Proud, S., Fensholt, R., Rasmussen, M., and Sandholt, I.: A comparison of the
effectiveness of 6S and SMAC in correcting for atmospheric interference of
Meteosat Second Generation images, J. Geophys. Res., 115,
D17209, https://doi.org/10.1029/2009JD013693, 2010. a
Richter, R.: A spatially adaptive fast atmospheric correction algorithm,
Int. J. Remote Sens., 17, 1201–1214, 1996. a
Sabater, N., Vicent, J., Alonso, L., Verrelst, J., Middleton, E. M.,
Porcar-Castell, A., and Moreno, J.: Compensation of Oxygen Transmittance
Effects for Proximal Sensing Retrieval of Canopy–Leaving Sun–Induced
Chlorophyll Fluorescence, Remote Sens., 10, 1551, 2018. a
Saunders, R., Hocking, J., Turner, E., Rayer, P., Rundle, D., Brunel, P., Vidot, J., Roquet, P., Matricardi, M., Geer, A., Bormann, N., and Lupu, C.: An update on the RTTOV fast radiative transfer model (currently at version 12), Geosci. Model Dev., 11, 2717–2737, https://doi.org/10.5194/gmd-11-2717-2018, 2018. a
Segl, K., Guanter, L., Rogass, C., Kuester, T., Roessner, S., Kaufmann, H.,
Sang, B., Mogulsky, V., and Hofer, S.: EeteS – The EnMAP end-to-end
simulation tool, IEEE J. Sel. Top. Appl. Earth
Observ. Remote Sens., 5, 522–530, 2012. a
Seidel, F. C., Kokhanovsky, A. A., and Schaepman, M. E.: Fast and simple model for atmospheric radiative transfer, Atmos. Meas. Tech., 3, 1129–1141, https://doi.org/10.5194/amt-3-1129-2010, 2010. a, b
Shepard, D.: Two-dimensional interpolation function for irregularly-spaced
data, Proc 23rd Nat Conf, 517–524, https://doi.org/10.1145/800186.810616, 1968. a
Tenjo, C., Rivera, J., Sabater, N., Vicent, J., Alonso, L., Verrelst, J., and
Moreno, J.: Design of a generic 3D scene generator for passive optical
missions and its implementation for the ESA's FLEX/Sentinel-3 tandem
mission, IEEE Trans. Geosci. Remote Sens., 55, 1290–1307,
2017. a
Theys, N., Van Roozendael, M., Hendrick, F., Fayt, C., Hermans, C., Baray, J.-L., Goutail, F., Pommereau, J.-P., and De Mazière, M.: Retrieval of stratospheric and tropospheric BrO columns from multi-axis DOAS measurements at Reunion Island (21∘ S, 56∘ E), Atmos. Chem. Phys., 7, 4733–4749, https://doi.org/10.5194/acp-7-4733-2007, 2007. a
Thompson, D. R., Natraj, V., Green, R. O., Helmlinger, M. C., Gao, B.-C., and
Eastwood, M. L.: Optimal estimation for imaging spectrometer atmospheric
correction, Remote Sens. Environ., 216, 355–373, 2018. a
Verreslt, J. and Rivera-Caicedo, J. P.: ARTMO toolbox official website, available at: https://artmotoolbox.com/, last access: 15 April 2020. a
Verrelst, J., Romijn, E., and Kooistra, L.: Mapping Vegetation Density in a
Heterogeneous River Floodplain Ecosystem Using Pointable CHRIS/PROBA Data,
Remote Sens., 4, 2866–2889, 2012. a
Verrelst, J., Vicent, J., Rivera-Caicedo, J. P., Lumbierres, M.,
Morcillo-Pallarés, P., and Moreno, J.: Global Sensitivity Analysis of
Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval
from Top-of-Atmosphere Radiance Data, Remote Sens., 11, 1923, 2019. a
Verstraete, M., Diner, D., and Bézy, J.-L.: Planning for a spaceborne Earth
Observation mission: From user expectations to measurement requirements,
Environ. Sci. Pol., 54, 419–427,
https://doi.org/10.1016/j.envsci.2015.08.005, 2015. a
Vicent, J., Sabater, N., Tenjo, C., Acarreta, J., Manzano, M., Rivera, J.,
Jurado, P., Franco, R., Alonso, L., Verrelst, J., and Moreno, J.: FLEX
End-to-End Mission Performance Simulator, IEEE Trans. Geosci.
Remote Sens., 54, 4215–4223, 2016. a
Vicent, J., Verrelst, J., Sabater, N., Alonso, L., Rivera-Caicedo, J. P., Martino, L., Muñoz-Marí, J., and Moreno, J.: Atmospheric Look-up table Generator (ALG) v2.0, Zenodo, https://doi.org/10.5281/zenodo.3555575, 2019. a
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...