Atmospheric correction of multispectral satellite imagery

Authors

DOI:

https://doi.org/10.36023/ujrs.2020.25.170

Keywords:

satellite imagery, broadband sensor, multispectral image, atmospheric model, analytical formulae, reflectance, regression equations

Abstract

Atmospheric correction is a necessary step in the processing of remote sensing data acquired in the visible and NIR spectral bands.
The paper describes the developed atmospheric correction technique for multispectral satellite data with a small number of relatively broad spectral bands (not hyperspectral). The technique is based on the proposed analytical formulae that expressed the spectrum of outgoing radiation at the top of a cloudless atmosphere with rather high accuracy. The technique uses a model of the atmosphere and its optical and physical parameters that are significant from the point of view of radiation transfer, the atmosphere is considered homogeneous within a satellite image. To solve the system of equations containing the measured radiance of the outgoing radiation in the bands of the satellite sensor, the number of which is less than the number of unknowns of the model, it is proposed to use various additional relations, including regression relations between the optical parameters of the atmosphere. For a particular image pixel selected in a special way, unknown atmospheric parameters are found, which are then used to calculate the reflectance for all other pixels.
Testing the proposed technique on OLI sensor data of Landsat 8 satellite showed higher accuracy in comparison with the FLAASH and QUAC methods implemented in the well-known ENVI image processing software. The technique is fast and there is using no additional information about the atmosphere or land surface except images under correction.

References

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., Chetwynd, J. H. (1998). FLAASH, a MODTRAN4 atmospheric correction package for hyperspectral data retrievals and simulations. Jet Propulsion Laboratory, 1, 9–14. https://doi.org/10.1109/aero.1999.792088

Belyaev, B.I., Belyaev, M. Yu., Desinov, L. V., Katkovsky, L. V., Sarmin, E. E. (2014). Spectra and images processing from Photospectral system in space experiment "Hurricane" on the ISS. Issledovanie Zemli iz kosmosa, 6, 54–65. (in Russian).

Berk, A., Conforti, P., Kennett, R., Perkins, T., Hawes, F., Bosch, J. (2014). MODTRAN6: a major upgrade of the MODTRAN radiative transfer code. Proc. SPIE 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, 90880H. https://doi.org/10.1117/12.2050433

Bernstein, L. S., Jin, X., Gregor, B., Adler-Golden, S. (2012). Quick Atmospheric Correction Code: Algorithm Description and Recent Upgrades. Optical Engineering, 51(11), 111719-1–111719-11. https://doi.org/10.1117/1.oe.51.11.111719

Katkovsky, L.V. (2016). The parameterization of the outgoing radiation for rapid atmospheric correction of hyperspectral images. Optika Atmosfery i Okeana, 29(9), 778–784. (in Russian).

Katkovsky, L.V., Martinov, A.O., Siliuk, V.A., Ivanov, D.A. (2018). SHARC method for fast atmospheric correction of hyperspectral data. Proc. SPIE 10786, Remote Sensing of Clouds and the Atmosphere XXIII, 1078609 (9). https://doi.org/10.1117/12.2323455

Katkovsky, L.V., Martinov, A.O., Siliuk, V.A., Ivanov, D.A., Kokhanovsky, A.A. (2018). Fast Atmospheric Correction Method for Hyperspectral Data. Remote Sensing, 10 (1698). https://doi.org/10.3390/rs10111698

Kokhanovsky, A.A., Mayer, B., Rozanov, V.V. (2005). A parameterization of the diffuse transmittance and reflectance for aerosol remote sensing problems. Atmospheric Research, 73, 37–43. https://doi.org/10.1016/j.atmosres.2004.07.004

Kotchenova, S. Y., Vermote, E. F. (2007). A vector version of the 6S radiative transfer code for atmospheric correction of satellite data: an Overview. 29th Review of Atmospheric Transmission Models Meeting, Lexington, Massachusetts, USA. https://doi.org/10.1364/ao.46.004455

She, L., Mei, L., Xue, Y., Che, Y., Guang, J. (2017). SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm. Remote Sensing, 9(3), 253; https://doi.org/10.3390/rs9030253

Smith, M. J. (2015). A Comparison of DG AComp, FLAASH and QUAC Atmospheric Compensation Algorithms Using WorldView-2 Imager. Department of Civil Engineering Master’s Report.

Vasilev, A. V., Melnikova, I. N., Kuznetsov, A. D. (2015). Approximation of multiply scattered solar radiation in the framework of a single scattering. International Symposium «Atmospheric Radiation and Dynamics», Book of Absracts, 131, Saint-Petersburg- Petrodvorets. (in Russian).

Vermote, E., Roger, J. C., Franch B., Skakun, S. (2018). LaSRC (Land Surface Reflectance Code): Overview, application and validation using MODIS, VIIRS, LANDSAT and Sentinel 2 data's. IEEE International Geoscience and Remote Sensing Symposium, 8173-8176, Valencia. https://doi.org/10.1109/igarss.2018.8517622

Downloads

Published

2020-06-25

Issue

No. 25 (2020)

Section

Fundamentals of remote sensing

License

Licensing conditions: the authors retain their copyrights and grant the journal the right of first publication of a work, simultaneously licensed in accordance with the Creative Commons Attribution License International CC-BY, which allows you to share the work with proof of authorship of the work and initial publication in this journal.

The authors, directing the manuscript to the editorial office of the Ukrainian Journal of Remote Sensing of the Earth, agree that the editorial board transfers the rights to protection and use of the manuscript (material submitted to the journal editorial board, including such protected copyright objects as photographs of the author, drawings, charts, tables, etc.), including reproduction in print and on the Internet; for distribution; to translate the manuscript into any languages; export and import of copies of the journal with the article of the authors for the purpose of distribution, informing the public. The above rights are transferred by the authors to the editors, without limitation of their validity, and in the territory of all countries of the world without limitation, including in Ukraine.

The authors guarantee that they have exclusive rights to use the submitted material. The editors are not liable to third parties for breach of data by the authors of the guarantees. The authors retain the right to use the published material, its fragments and parts for personal, including scientific and educational purposes. The rights to the manuscript are considered to be transferred by the authors of the editorial board from the moment of the publication of the issue of the journal in which it is published. Reprinting of materials published in the journal by other individuals and legal entities is possible only with the consent of the publisher, with the obligatory indication of the issue of the journal in which the material was published.