Atmospheric correction of multispectral satellite imagery

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


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.


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.

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.

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.

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).

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).

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.

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.

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;

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.

Fundamentals of remote sensing