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Algorithm for the Atmospheric Correction of Shortwave Channels of the MSU-MR Radiometer of the Meteor-M No. 2 Satellite

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Abstract

The problem of atmospheric correction for shortwave channels of a multispectral low-resolution scanning radiometer onboard the Meteor-M No. 2 satellite is considered. The existing atmospheric correction algorithms are analyzed. An atmospheric correction algorithm is developed based on special lookup tables (LUTs) generated by the authors. The LUTs contain information about the reflectance of the radiometer channels for different atmospheric conditions and observation geometry. The results of atmospheric correction have been validated for the first channel of the radiometer. The validation showed a high correlation with the reference reflectance taken from the Surface Albedo Validation Sites EUMETSAT portal. The algorithm has been additionally validated with the data from the first channel of the AVHRR radiometer onboard the MetOp-A satellite. The correlation between the reference values and the results of atmospheric correction are comparable for both radiometers.

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REFERENCES

  1. Adler-Golden, S., Berk, A., Bernstein, L.S., Richtsmeier, S., Acharya, P.K., Matthew, M.W., Anderson, G.P., Allred, C.L., Jeong, L.S., and Chetwynd, J.H., FLAASH, a MODTRAN4 atmospheric correction package for hyperspectral data retrievals and simulations, Jet Propulsion Lab., 1998, vol. 1, pp. 9–14.

    Google Scholar 

  2. Akimov, N.P., Badaev, K.V., Gektin, Yu.M., Ryzhakov, A.V., Smelyanskii, M.B., and Frolov, A.G., MSU-MR multiband scanner of low spatial resolution for space-based Meteor-M informational system. Principle of operation and development prospects, Raketn.-Kosm. Priborostr. Inf. Sist., 2015, vol. 4, pp. 30–39. https://doi.org/10.17238/issn2409-0239.2015.4.30

    Article  Google Scholar 

  3. Andreev, A.I., Shamilova, Yu.A., and Kholodov, E.I., Using convolutional neural networks for cloud detection from Meteor-M no. 2 MSU-MR data, Russ. Meteorol. Hydrol., 2019, vol. 44, pp. 459–466.

    Article  Google Scholar 

  4. Bakhrushin, V.E., Methods for assessing the characteristics of nonlinear statistical relationships, Sist. Tekhnol., 2011, no. 2, pp. 9–14.

  5. Belyaev, M.Yu., Belyaev, B.I., Ivanov, D.A., Katkovskiy, L.V., Martinov, A.O., Ryazantsev, V.V., Sarmin, E.E., Silyuk, O.O., and Shukaylo, V.G., Atmospheric correction of data registered on board the ISS. Part I. Methodology for spectra, Sovr. Probl. Dist. Zond. Zemli Kosmosa, 2018, vol. 15, no. 6, pp. 213–222. https://doi.org/10.21046/2070-7401-2018-15-6-213-222

    Article  Google Scholar 

  6. Berk, A., Conforti, P., Kennett, R., Perkins, T., Hawes, F., and Bosch, J., MODTRAN6: a major upgrade of the MODTRAN radiative transfer code, in Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX (June 13, 2014), 90880H. https://doi.org/10.1117/12.2050433.

  7. Bernstein, L.S., Jin, X., Gregor, B., and Adler-Golden, S., Quick atmospheric correction code: algorithm description and recent upgrades, Opt. Eng., 2012, vol. 51, no. 11, pp. 1–11. https://doi.org/10.1117/1.OE.51.11.111719

    Article  Google Scholar 

  8. Breon, F.M., Reflectance of broken cloud fields: simulation and parameterization, J. Atmos. Sci., 1992, vol. 49, no. 14, pp. 1221–1232.https://doi.org/10.1175/1520-0469(1988)045EIOH I> 2.0.CO;2

  9. Coulson, K.L. Dave, J.V., and Sekera, Z., Tables related to radiation emerging from a planetary atmosphere with Rayleigh scattering, Geophys. J. Int., 1961, vol. 5, no. 1, p. 87. https://doi.org/10.1093/gji/5.1.87

    Article  Google Scholar 

  10. Dodysheva, A.A., Calculation of NDVI values and atmospheric correction, Reshetnevskie chteniya, 2010, vol. 1, pp. 188–189.

  11. Evans, K.F. and Stephens, G.L., A new polarized atmospheric radiative transfer model, J. Quant. Spectrosc. Radiat. Transfer, 1991, vol. 46, no. 5, pp. 413–423. https://doi.org/10.1016/0022-4073(91)90043-P

    Article  Google Scholar 

  12. Filey, A.A., Rublev, A.N., and Zaytsev, A.A., Radiometric cross-calibration of shortwave channels of multi-channel scanning unit on board Meteor-M no. 2 relative to spectroradiometer AVHRR on board Meteor-A, Sovr. Probl. Dist. Zond. Zemli Kosmosa, 2016, vol. 13, no. 6, pp. 251–263. https://doi.org/10.21046/2070-7401-2016-13-6-251-263

    Article  Google Scholar 

  13. Jarvis, A., Reuter, H.I., Nelson, A., and Guevara, E., Hole-filled SRTM for the globe Version4, available from the CGIARCSI SRTM 90m Database. 2008. EUME-TSAT. Surface Albedo Validation Sites, European Organisation for the Exploitation of Meteorological Satellites, 2015. https://doi.org/10.15770/EUM_SEC_CLM_1001

    Book  Google Scholar 

  14. Kaufman, Y.J., Tanre, D., Remer, L.A., Vermote, E.F., Chu, A., and Holben, B.N., Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer, J. Geophys. Res., 1997, vol. 102, pp. 17051–17068.

    Article  Google Scholar 

  15. Kotchenova, S.Y. and Vermote, E.F., A vector version of the 6S radiative transfer code for atmospheric correction of satellite data: an overview, in 29th Review of Atmospheric Transmission Models Meeting (13–14 June 2007, Lexington, Mass., USA), 2007.

  16. Kotchenova, S.Y., Vermote, E.F., Levy, R., and Lyapustin, A., Radiative transfer codes for atmospheric correction and aerosol retrieval: intercomparison study, Appl. Opt., 2008, vol. 47, no. 13, pp. 2215–2226. https://doi.org/10.1364/AO.47.002215

    Article  Google Scholar 

  17. Liang, S.L., Fang, H.L., and Chen, M.Z., Atmospheric correction of Landsat ETM+ land surface imagery, I. Methods. IEEE Trans. Geosci. Remote Sens., 2001, vol. 39, no. 11, pp. 2490–2498. https://doi.org/10.1109/36.964986

    Article  Google Scholar 

  18. Muldashev, T.Z., Lyapustin, A.I., and Sultangazin, U.M., Spherical harmonics method in the problem of radiative transfer in the atmosphere-surface system, J. Quant. Spectrosc. Radiat. Transfer, 1999, vol. 61, no. 3, pp. 393–404. https://doi.org/10.1016/S0022-4073(98)00025-9

    Article  Google Scholar 

  19. Nguyen, H.C., Jung, J., Lee, J., Choi, S., Hong, S., and Heo, J., Optimal atmospheric correction for above-ground forest biomass estimation with the ETM+ remote sensor, Sensors, 2015, vol. 15, pp. 18865–18886. https://doi.org/10.3390/s150818865

    Article  Google Scholar 

  20. Nikolaeva, O.V., An algorithm for eliminating gas absorption effects on hyperspectral remote sensing data, Komp’yut. Opt., 2018, no. 2, pp. 328–337.

  21. Pacifici, F., Longbotham, N., and Emery, W.J., The importance of physical quantities for the analysis of multitemporal and multiangular optical very high spatial resolution images, IEEE Trans. Geosci. Remote Sens., 2014, vol. 52, no. 10, pp. 6241–6256. https://doi.org/10.1109/TGRS.2013.2295819

    Article  Google Scholar 

  22. Samarskii, A.A. and Gulin, A.V., Chislennye metody: Uchebnoe posobie dlya vuzov (Numerical Methods: Textbook for Universities), Moscow: Nauka, 1989.

  23. Simonenko, E.V., Chudin, A.O., and Davidenko, A.N., The differential method for calculation of cloud motion vectors, Russ. Meteorol. Hydrol., 2017, vol. 42, pp. 159–167.

    Article  Google Scholar 

  24. Smith, M.J., A comparison of DG AComp, FLAASH and QUAC atmospheric compensation algorithms using WorldView-2 imager, Master’s Report (Department of Civil Engineering, 2015).

    Google Scholar 

  25. Timofeev, Yu.M. and Vasil’ev, A.V., Osnovy teoreticheskoi atmosfernoi optiki: Uchebno-metodicheskoe posobie (Fundamentals of Theoretical Atmospheric Optics: Educational-Methodical Manual), St. Petersburg: Fiz. F-t SPbGU, 2007, p. 152.

  26. Wilson, R.T., Py6S: a python interface to the 6S radiative transfer model, Comput. Geosci., 2013, vol. 51, pp. 166–171. https://doi.org/10.1016/j.cageo.2012.08.002

    Article  Google Scholar 

  27. Zubkova, K.I., Permitina, L.I., and Chaban, L.N., Influence of atmospheric correction methods at the calculation of vegetation indices on hyperspectral images, Geod. Kartogr., 2015, spec. vol., pp. 84–87.

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ACKNOWLEDGMENTS

We are grateful to Dr. Sci. (Phys.–Math.) Leonid Katkovsky, head of the laboratory at the Sevchenko Institute of Applied Physical Problems of Belarusian State University and professor in the Department of Physics and Aerospace Technologies at Belarusian State University for advice and important comments during this work.

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Correspondence to M. O. Kuchma.

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Translated by O. Ponomareva

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Kuchma, M.O., Bloshchinskiy, V.D. Algorithm for the Atmospheric Correction of Shortwave Channels of the MSU-MR Radiometer of the Meteor-M No. 2 Satellite. Izv. Atmos. Ocean. Phys. 56, 909–915 (2020). https://doi.org/10.1134/S0001433820090145

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