Radiative Transfer Model Comparison with Satellite Observations over CEOS Calibration Site Libya-4
Abstract
:1. Introduction
2. Libya-4 Site Characterisation
- The surface BRF is characterised by the so-called RPV (Rahman–Pinty–Verstraete) model [28]. The values of the RPV parameters have been derived at 1 nm resolution with the method described in [25], including the improvements proposed by [21]. The 4-parameter version of the RPV model is used for this study [29].
- A specific aerosol optical thickness climatology assuming non-spherical particles representing Saharan dust is used as described in [21].
3. Description of Selected RTMs
3.1. 6sv Version 1.1
3.2. Rtmom Version 1
3.3. Libradtran Version 2
3.4. Artdeco Version 1.1
4. Description of the Satellite Data
5. Rtm Comparison Method
6. Results
6.1. Aqua/Modis Results
6.2. Envisat/Aatsr Results
6.3. Landsat-8/Oli and Sentinel-2A/MSI Results
6.4. Envisat/Meris and Sentinel-3A/OLCI Results
6.5. Spectral Region Analysis
7. Discussion and Conclusions
- In spectral regions dominated by scattering and in the presence of large particles, models based on adding–doubling or discrete ordinate should be operated with a large number of quadrature points. A numerical method such as Monte Carlo ray tracing should be favoured in the presence of large particles.
- In spectral regions where molecular absorption is dominant, i.e., with a corresponding transmittance lower than 0.98, it is essential that all active molecules are accounted for. There is, however, an intrinsic limitation to band-integrated transmittance methods of about 1% in the worst cases [16]
- There is also an intrinsic limitation to the 1D approximation when topography effects have to be considered [23]. The systematic usage of 3D RTM might be needed to deliver simulated radiance compatible with missions such as CLARREO or TRUTHS over complex terrains.
- Finally, it is essential to permanently benchmark RTMs using scenarios mimicking actual usage. The latest RAMI initiative [54], dedicated to the benchmarking of coupled surface-atmosphere radiative transfer models, is particularly relevant in this context. This type of benchmarking is based on scenarios of gradual complexity which will allow one to characterise and understand in great detail the discrepancies between the models.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Acknowledgments
Conflicts of Interest
References
- Vicent, J.; Sabater, N.; Tenjo, C.; Acarreta, J.R.; Manzano, M.; Rivera, J.P.; Jurado, P.; Franco, R.; Alonso, L.; Verrelst, J.; et al. FLEX End-to-End Mission Performance Simulator. IEEE Trans. Geosci. Remote Sens. 2016, 54, 4215–4223. [Google Scholar] [CrossRef]
- Luffarelli, M.; Govaerts, Y. Joint Retrieval of Surface Reflectance and Aerosol Properties with Continuous Variation of the State Variables in the Solution Space—Part 2: Application to Geostationary and Polar-Orbiting Satellite Observations. Atmos. Meas. Tech. 2019, 12, 791–809. [Google Scholar] [CrossRef][Green Version]
- Vicent, J.; Verrelst, J.; Sabater, N.; Alonso, L.; Rivera-Caicedo, J.P.; Martino, L.; Muñoz-Marí, J.; Moreno, J. Comparative Analysis of Atmospheric Radiative Transfer Models Using the Atmospheric Look-up Table Generator (ALG) Toolbox (Version 2.0). Geosci. Model Dev. 2020, 13, 1945–1957. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Vidot, J. Overview of the Status of Radiative Transfer Models for Satellite Data Assimilation. In Proceedings of the Seminar on Use of Satellite Observations in Numerical Weather Prediction, Reading, UK, 8–12 September 2014; p. 14. [Google Scholar]
- Lamquin, N.; Woolliams, E.; Bruniquel, V.; Gascon, F.; Gorroño, J.; Govaerts, Y.; Leroy, V.; Lonjou, V.; Alhammoud, B.; Barsi, J.A.; et al. An Inter-Comparison Exercise of Sentinel-2 Radiometric Validations Assessed by Independent Expert Groups. Remote Sens. Environ. 2019, 233, 111369. [Google Scholar] [CrossRef]
- Lyapustin, A.I. Radiative Transfer Code SHARM for Atmospheric and Terrestrial Applications. Appl. Opt. 2005, 44, 7764–7772. [Google Scholar] [CrossRef]
- Korkin, S.; Lyapustin, A.; Sinyuk, A.; Holben, B.; Kokhanovsky, A. Vector Radiative Transfer Code SORD: Performance Analysis and Quick Start Guide. J. Quant. Spectrosc. Radiat. Transf. 2017, 200, 295–310. [Google Scholar] [CrossRef]
- Ota, Y.; Higurashi, A.; Nakajima, T.; Yokota, T. Matrix Formulations of Radiative Transfer Including the Polarization Effect in a Coupled Atmosphere–Ocean System. J. Quant. Spectrosc. Radiat. Transf. 2010, 111, 878–894. [Google Scholar] [CrossRef]
- Doicu, A.; Efremenko, D.; Trautmann, T. A Multi-Dimensional Vector Spherical Harmonics Discrete Ordinate Method for Atmospheric Radiative Transfer. J. Quant. Spectrosc. Radiat. Transf. 2013, 118, 121–131. [Google Scholar] [CrossRef]
- Anderson, G.P.; Clough, S.A.; Kneizys, F.X.; Chetwynd, J.H.; Shettle, E.P. AFGL Atmospheric Constituent Profiles (0.120 km). 1986. Available online: https://ui.adsabs.harvard.edu/abs/1986afgl.rept.....A/abstract (accessed on 14 June 2021).
- Kneizys, F.X.; Shettle, E.P.; Abreu, J.L.W.; Chetwynd, J.H.; Anderson, G.P.; Gallery, W.O.; Selby, J.E.A.; Clough, S.A. User’s Guide to LOWTRAN 7; Air Force Geophysics Lab.: Hanscom, MA, USA, 1988. [Google Scholar]
- Berk, A.; Conforti, P.; Kennett, R.; Perkins, T.; Hawes, F.; van den Bosch, J. MODTRAN6: A Major Upgrade of the MODTRAN Radiative Transfer Code. In Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, Baltimore, MD, USA, 5–9 May 2014; Volume 9088, p. 90880H. [Google Scholar] [CrossRef]
- Emde, C.; Buras-Schnell, R.; Kylling, A.; Mayer, B.; Gasteiger, J.; Hamann, U.; Kylling, J.; Richter, B.; Pause, C.; Dowling, T.; et al. The libRadtran Software Package for Radiative Transfer Calculations (Version 2.0.1). Geosci. Model Dev. 2016, 9, 1647–1672. [Google Scholar] [CrossRef]
- Kotchenova, S.Y.; Vermote, E.F.; Matarrese, R.; Klemm, F.J. Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: Path radiance. Appl. Opt. 2006, 45, 6762–6774. [Google Scholar] [CrossRef][Green Version]
- Kato, S.; Ackerman, T.P.; Mather, J.H.; Clothiaux, E.E. The k-distribution method and correlated-k approximation for a shortwave radiative transfer model. J. Quant. Spectrosc. Radiat. Transf. 1999, 62, 109–121. [Google Scholar] [CrossRef]
- Gasteiger, J.; Emde, C.; Mayer, B.; Buras, R.; Buehler, S.A.; Lemke, O. Representative Wavelengths Absorption Parameterization Applied to Satellite Channels and Spectral Bands. J. Quant. Spectrosc. Radiat. Transf. 2014, 148, 99–115. [Google Scholar] [CrossRef]
- Pinty, B.; Gobron, N.; Widlowski, J.L.; Gerstl, S.A.W.; Verstraete, M.M.; Antunes, M.; Bacour, C.; Gascon, F.; Gastellu, J.P.; Goel, N.; et al. Radiation Transfer Model Intercomparison (RAMI) Exercise. J. Geophys. Res. Atmos. 2001, 106, 11937–11956. [Google Scholar] [CrossRef]
- Kotchenova, S.Y.; Vermote, E.F.; Levy, R.; Lyapustin, A. Radiative Transfer Codes for Atmospheric Correction and Aerosol Retrieval: Intercomparison Study. Appl. Opt. 2008, 47, 2215–2226. [Google Scholar] [CrossRef][Green Version]
- Callieco, F.; Dell’Acqua, F. A comparison between two radiative transfer models for atmospheric correction over a wide range of wavelengths. Int. J. Remote Sens. 2011, 32, 1357–1370. [Google Scholar] [CrossRef]
- Escribano, J.; Bozzo, A.; Dubuisson, P.; Flemming, J.; Hogan, R.J.; Labonnote, C.L.; Boucher, O. A benchmark for testing the accuracy and computational cost of shortwave top-of-atmosphere reflectance calculations in clear-sky aerosol-laden atmospheres. Geosci. Model Dev. 2019, 12, 805–827. [Google Scholar] [CrossRef][Green Version]
- Govaerts, Y.; Sterckx, S.; Adriaensen, S. Use of Simulated Reflectances over Bright Desert Target as an Absolute Calibration Reference. Remote Sens. Lett. 2013, 4, 523–531. [Google Scholar] [CrossRef]
- Cooke, R.U.; Warren, A.; Goudie, A.S. Desert Geomorphology; CRC Press: London, UK, 1993. [Google Scholar] [CrossRef]
- Govaerts, Y.M. Sand Dune Ridge Alignment Effects on Surface BRF over the Libya-4 CEOS Calibration Site. Sensors 2015, 15, 3453–3470. [Google Scholar] [CrossRef]
- Sterckx, S.; Adriaensen, S.; Dierckx, W.; Bouvet, M. In-Orbit Radiometric Calibration and Stability Monitoring of the PROBA-V Instrument. Remote Sens. 2016, 8, 546. [Google Scholar] [CrossRef][Green Version]
- Govaerts, Y.M.; Clerici, M. Evaluation of Radiative Transfer Simulations over Bright Desert Calibration Sites. IEEE Trans. Geosci. Remote Sens. 2004, 42, 176–187. [Google Scholar] [CrossRef]
- Govaerts, Y.M.; Rüthrich, F.; John, V.O.; Quast, R. Climate Data Records from Meteosat First Generation Part I: Simulation of Accurate Top-of-Atmosphere Spectral Radiance over Pseudo-Invariant Calibration Sites for the Retrieval of the In-Flight Visible Spectral Response. Remote Sens. 2018, 10, 1959. [Google Scholar] [CrossRef][Green Version]
- Govaerts, M.Y.; Estimating the Accuracy of 1D Radiative Transfer Models over the Libya-4 Site. Technical Report RTMPV-WO1-2.3, Rayference. 2019. Available online: https://www.eradiate.eu/resources/docs/reports/report-assessment_calibration_libya4-2.3-20191007.pdf (accessed on 19 October 2022).
- Rahman, H.; Pinty, B.; Verstraete, M.M. Coupled Surface-Atmosphere Reflectance (CSAR) Model. 2. Semiempirical Surface Model Usable with NOAA Advanced Very High Resolution Radiometer Data. J. Geophys. Res. 1993, 98, 20791–20801. [Google Scholar] [CrossRef]
- Pinty, B.; Roveda, F.; Verstraete, M.M.; Gobron, N.; Govaerts, Y.; Martonchik, J.V.; Diner, D.J.; Kahn, R.A. Surface Albedo Retrieval from Meteosat: Part 1: Theory. J. Geophys. Res. 2000, 105, 18099–18112. [Google Scholar] [CrossRef]
- Guillory, A. ERA5. 2017. Available online: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 (accessed on 7 September 2022).
- Rothman, L.S.; Rinsland, C.P.; Goldman, A.; Massie, S.T.; Edwards, D.P.; Flaud, J.M.; Perrin, A.; Camy-peyret, C.; Dana, V.; Mandin, J.Y.; et al. The HITRAN Molecular Spectroscopic Database And Hawks (HITRAN Atmospheric Workstation): 1996 EDITION. J. Quant. Spectrosc. Radiat. Transf. 1998, 60, 665–710. [Google Scholar] [CrossRef]
- Vermote, E.F.; Tanré, D.; Deuzé, J.L.; Herman, M.; Morcrette, J.J. Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: An Overview. IEEE Trans. Geosci. Remote Sens. 1997, 35, 675–686. [Google Scholar] [CrossRef][Green Version]
- Goody, R.M. Atmospheric Radiation (I): Theoretical Basis.; Oxford University Press: Oxford, UK, 1965; Volume 91. [Google Scholar]
- Malkmus, W. Random Lorentz Band Model with Exponential-Tailed S-1 Line-Intensity Distribution Function. J. Opt. Soc. Am. 1967, 57, 323–329. [Google Scholar] [CrossRef]
- Vermote, E.; Tanr, D.; Deuze, J.L.; Herman, M.; Morcrette, J.J. Second Simulation of the Satellite Signal in the Solar Spectrum (6S): User’s Guide, Version 1; University of Maryland and NASA-Goddard Space Flight Center: Greenbelt, MD, USA, 1995. [Google Scholar]
- Evans, K.F.; Stephens, G.L. A New Polarized Atmospheric Radiative Transfer Model. J. Quant. Spectrosc. Radiat. Transf. 1991, 46, 413–423. [Google Scholar] [CrossRef]
- Acharya, P.K.; Berk, A.; Anderson, G.P.; Anderson, G.P.; Larsen, N.F.; Tsay, S.; Stamnes, K.H. MODTRAN4: Multiple Scattering and Bi-Directional Reflectance Distribution Function (BRDF) Upgrades to MODTRAN. Proc. SPIE 1999, 3756, 354–362. [Google Scholar]
- Govaerts, Y.M. RTMOM V0B.10 User’s Manual; Technical Report; EUMETSAT: Darmstadt, Germany, 2006. [Google Scholar]
- Fischer, J.; Grassl, H. Radiative transfer in an atmosphere-ocean system: An azimuthally dependent matrix-operator approach. Appl. Opt. 1984, 23, 1032–1039. [Google Scholar] [CrossRef]
- Fell, F.; Fischer, J. Numerical Simulation of the Light Field in the Atmosphere-Ocean System Using the Matrix-Operator Method. J. Quant. Spectrosc. Radiat. Transf. 2001, 69, 351–388. [Google Scholar] [CrossRef]
- Liu, Q.; Ruprecht, E. Radiative Transfer Model: Matrix Operator Method. Appl. Opt. 1996, 35, 4229–4237. [Google Scholar] [CrossRef] [PubMed]
- Wiscombe, W.J. The Delta-M Method: Rapid Yet Accurate Radiative Flux Calculations for Strongly Asymmetric Phase Functions. J. Atmos. Sci. 1977, 34, 1408–1422. [Google Scholar] [CrossRef]
- Stamnes, K.; Tsay, S.C.; Wiscombe, W.; Jayaweera, K. Numerically Stable Algorithm for Discrete-Ordinate-Method Radiative Transfer in Multiple Scattering and Emitting Layered Media. Appl. Opt. 1988, 27, 2502. [Google Scholar] [CrossRef] [PubMed]
- Emde, C.; Buras, R.; Mayer, B.; Blumthaler, M. The Impact of Aerosols on Polarized Sky Radiance: Model Development, Validation, and Applications. Atmos. Chem. Phys. 2010, 10, 383–396. [Google Scholar] [CrossRef][Green Version]
- Emde, C.; Buras, R.; Mayer, B. ALIS: An Efficient Method to Compute High Spectral Resolution Polarized Solar Radiances Using the Monte Carlo Approach. J. Quant. Spectrosc. Radiat. Transf. 2011, 112, 1622–1631. [Google Scholar] [CrossRef][Green Version]
- Xiong, X.; Butler, J.J. MODIS and VIIRS Calibration History and Future Outlook. Remote Sens. 2020, 12, 2523. [Google Scholar] [CrossRef]
- RADIS Release 0.12.0. 2022. Available online: https://github.com/radis/radis/releases/tag/0.12.0 (accessed on 15 September 2022).
- Pannier, E.; Laux, C.O. RADIS: A nonequilibrium line-by-line radiative code for CO_{2} and HITRAN-like database species. J. Quant. Spectrosc. Radiat. Transf. 2019, 222–223, 12–25. [Google Scholar] [CrossRef][Green Version]
- Van den Bekerom, D.; Pannier, E. A discrete integral transform for rapid spectral synthesis. J. Quant. Spectrosc. Radiat. Transf. 2021, 261, 107476. [Google Scholar] [CrossRef]
- Gordon, I.; Rothman, L.; Hargreaves, R.; Hashemi, R.; Karlovets, E.; Skinner, F.; Conway, E.; Hill, C.; Kochanov, R.; Tan, Y.; et al. The HITRAN2020 molecular spectroscopic database. J. Quant. Spectrosc. Radiat. Transf. 2022, 277, 107949. [Google Scholar] [CrossRef]
- Gamache, R.R.; Vispoel, B.; Rey, M.; Nikitin, A.; Tyuterev, V.; Egorov, O.; Gordon, I.E.; Boudon, V. Total internal partition sums for the HITRAN2020 database. J. Quant. Spectrosc. Radiat. Transf. 2021, 271, 107713. [Google Scholar] [CrossRef]
- Salehi, F.; Thome, K.; Wenny, B.N.; Lockwood, R.; Wang, Z. Band-Averaged Response Sensitivity Study of an Imaging Spectrometer for the CLARREO Pathfinder Mission. Remote Sens. 2022, 14, 2302. [Google Scholar] [CrossRef]
- Fox, N.; Green, P. Traceable Radiometry Underpinning Terrestrial- and Helio-Studies (TRUTHS): An Element of a Space-Based Climate and Calibration Observatory. Remote Sens. 2020, 12, 2400. [Google Scholar] [CrossRef]
- European Commission—RAMI4ATM. 2022. Available online: https://rami-benchmark.jrc.ec.europa.eu/_www/RAMI4ATM.php (accessed on 15 September 2022).
Platform | Radiometer | ${\mathit{N}}_{\mathit{t}}$ | Bands | |||||
---|---|---|---|---|---|---|---|---|
Blue | Green | Red | NIR | SWIR 1 | SWIR 2 | |||
0.46 $\mathsf{\mu}$m | 0.55 $\mathsf{\mu}$m | 0.65 $\mathsf{\mu}$m | 0.86 $\mathsf{\mu}$m | 1.60 $\mathsf{\mu}$m | 2.20 $\mathsf{\mu}$m | |||
AQUA | MODIS | 190 | B03 | B04 | B01 | B02 | B06 | B07 |
Envisat | AATSR | 150 | – | B1 | B2 | B3 | B4 | – |
Landsat-8 | OLI | 52 | B1,B2 | B3 | B4 | B5 | B6 | B7 |
Sentinel-2A | MSI | 58 | B1,B2 | B3 | B4 | B8,B8A | B11 | B12 |
Envisat | MERIS | 102 | B2,B3 | B5 | B7,B8 | B13,B14 | – | – |
Sentinel-3A | OLCI | 54 | Oa3,Oa4 | Os6 | Oa7-10 | Oa17 | – | – |
Molecules | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
Band | H_{2}O | O_{3} | CO_{2} | N_{2}O | CH_{4} | Total | ||||
Conc. | 14.39 | 345.75 | 330 ^{(1)} | 420 ^{(2)} | 320 | 1700 ^{(1)} | 1900 ^{(2)} | ^{(1)} | ^{(2)} | |
Units | kg m^{−2} | DU | ppmv | ppbv | ppbv | |||||
MODIS | B06 | 99.6 | 100.0 | 99.4 | 99.3 | 100.0 | 99.5 | 99.5 | 98.6 | 98.4 |
OLI | B6 | 99.4 | 100.0 | 98.3 | 98.0 | 100.0 | 99.7 | 99.7 | 97.5 | 97.1 |
MSI | B11 | 99.4 | 100.0 | 98.4 | 98.0 | 100.0 | 99.6 | 99.6 | 97.4 | 97.0 |
AATSR | B4 | 99.3 | 100.0 | 98.0 | 97.6 | 100.0 | 99.9 | 99.9 | 97.3 | 96.8 |
MODIS | B07 | 95.6 | 100.0 | 98.5 | 98.1 | 99.8 | 99.9 | 99.9 | 93.9 | 93.6 |
OLI | B7 | 96.4 | 100.0 | 99.9 | 99.8 | 99.8 | 97.7 | 97.5 | 93.9 | 93.6 |
MSI | B12 | 96.4 | 100.0 | 99.9 | 99.9 | 99.8 | 97.8 | 97.6 | 94.0 | 93.8 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Govaerts, Y.; Nollet, Y.; Leroy, V. Radiative Transfer Model Comparison with Satellite Observations over CEOS Calibration Site Libya-4. Atmosphere 2022, 13, 1759. https://doi.org/10.3390/atmos13111759
Govaerts Y, Nollet Y, Leroy V. Radiative Transfer Model Comparison with Satellite Observations over CEOS Calibration Site Libya-4. Atmosphere. 2022; 13(11):1759. https://doi.org/10.3390/atmos13111759
Chicago/Turabian StyleGovaerts, Yves, Yvan Nollet, and Vincent Leroy. 2022. "Radiative Transfer Model Comparison with Satellite Observations over CEOS Calibration Site Libya-4" Atmosphere 13, no. 11: 1759. https://doi.org/10.3390/atmos13111759