Evaluation of Atmospheric Correction Methods with Sentinel-2 Imagery
DOI:
https://doi.org/10.11137/1982-3908_2026_49_68044Keywords:
Remote sensing, Atmospheric effects, Spectral behaviorAbstract
To reduce the atmospheric effects on remote sensing imagery, there are methods to correct them, called atmospheric correction, which is an important stage of satellite image preprocessing. Thus, the present work aims to evaluate three different atmospheric correction techniques: Dark Object Subtraction, Sentinel Atmospheric Correction, and Atmospheric Correction, using images from the MSI sensor of the Sentinel satellite covering the Boa Esperança River basin, located in the city of Nova Friburgo, RJ. In this work, it was possible to conclude that two methods fulfilled the expectations of correcting images (Sen2cor and ATCOR with topographic correction). They are methods based on atmospheric modeling and present the expected behavior according to the literature. The Sen2Cor method, in the visible region, removed the interference of the atmospheric scattering, mainly in the blue band, with an average reduction of 75% of the reflectance values, while in the other visible bands, the average reduction was in the range of 43%. In the near infrared region, it had an increase of reflectance values in the order of 15%, due to the electromagnetic absorption of the radiation, according to the literature. On the other hand, the ATCOR method with topographic correction obtained an average decrease of about 70% in the visible range, while in the near infrared, the increase is around 8%.
Downloads
References
Acevedo, M.F.B., Groen, T.A., Hecker, C.A. & Skidmore, A.K. 2017, ‘Identifying leaf traits that signal stress in TIR spectra’, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 125, pp. 132–145, DOI: 10.1016/j.isprsjprs.2017.01.014.
Bernardo, N., Watanabe, F., Rodrigues, T. & Alcântara, E. 2017, ‘Atmospheric correction issues for retrieving total suspended matter concentrations in inland waters using OLI/Landsat-8 image’, Advances in Space Research, vol. 59, no. 9, pp. 2335–2348.
Chavez, P.S. 1988, ‘An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data’, Remote Sensing of Environment, vol. 24, no. 1, pp. 459–479.
Doxani, G., Vermote, E., Roger, J.C., Gascon, F., Adriaensen, S., Frantz, D., Hagolle, O., Hollstein, A., Kirches, G., Li, F., Louis, J., Mangin, A., Pahlevan, N., Pflug, B. & Vanhellemont, Q. 2018, ‘Atmospheric correction inter-comparison exercise’, Remote Sensing, vol. 10, no. 2.
ESA (Agência Espacial Europeia) 2017, ‘Sentinel Home’, viewed July 2017, https://earth.esa.int/web/sentinel/home.
Francisco, C. N., Ruiz, P.R.S., Almeida, C.M., Gruber, N.C. & Anjos, C.S. 2020, ‘Análise do impacto da correção atmosférica no cálculo do Índice de Vegetação por Diferença Normalizada a partir de Imagem Landsat 8/OLI’, Revista Brasileira de Geografia Física, viewed 19 June 2021, https://periodicos.ufpe.br/revistas/index.php/rbgfe/article/view/242936.
Frouin, R.J., Franz, B.A., Ibrahim, A., Knobelspiesse, K., Ahmad, Z., Cairns, B.Z., Chowdhary, J., Dierssen, H.M., Tan, J., Dubovik, O., Huang, X., Davis, A.B., Kalashnikova, O., Thompson, D.R., Remer, L.A., Boss, E., Coddington, O., Deschamps, P.Y., Gao, B.C., Gross, L., Hasekamp, O., Omar, A., Pelletier, B., Ramon, D., Steinmetz, F. & Zhai, P.W. 2019, ‘Atmospheric Correction of Satellite Ocean-Color Imagery During the PACE Era’, Frontiers in Earth Science, vol. 7, art. 145.
Gao, B.C., Montes, M.J., Davis, C.O. & Goetz, A.F.H. 2009, ‘Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean’, Remote Sensing of Environment, vol. 113, suppl. 1, pp. S17–S24.
Gaida, W., Breunig, F.M., Galvão, L.S. & Ponzoni, F.J. 2020, ‘Correção Atmosférica em Sensoriamento Remoto: Uma Revisão’, Revista Brasileira de Geografia Física, vol. 13, no. 1, pp. 229–248.
Gholizadeh, A. & Kopačková, V. 2019, ‘Detecting vegetation stress as a soil contamination proxy: a review of optical proximal and remote sensing techniques’, International Journal of Environmental Science and Technology, vol. 16, no. 4, pp. 2405–2424.
Hartoni, H., Siregar, V.P., Wouthuyzen, S. & Agus, S.B. 2022, ‘Object based classification of benthic habitat using Sentinel 2 imagery by applying support vector machine and random forest algorithms in shallow waters of Kepulauan Seribu, Indonesia’, Biodiversitas, vol. 23, no. 1, pp. 514–520.
Ilori, C.O., Pahlevan, N. & Knudby, A. 2019, ‘Analyzing performances of different atmospheric correction techniques for Landsat 8: Application for coastal remote sensing’, Remote Sensing, vol. 11, no. 4, art. 469.
INEA (Instituto Estadual do Ambiente) 2010, O estado do ambiente, viewed 6 July 2018, http://www.inea.rj.gov.br/cs/groups/public/documents/document/zwew/mde1/~edisp/inea0015448.pdf.
Jensen, J.R. 2009, Sensoriamento remoto do ambiente: uma perspectiva em recursos terrestres, Parêntese, São José dos Campos.
Kim, K. & Lee, K. 2022, ‘Accuracy Comparison of TOA and TOC Reflectance Products of KOMPSAT-3, WorldView-2 and Pléiades-1A Image Sets Using RadCalNet BTCN and BSCN Data’, Korean Journal of Remote Sensing, vol. 38, no. 1, pp. 21–32.
Lorenzzetti, J.A. 2015, Princípios físicos de sensoriamento remoto, Blucher, São Paulo.
Martins, V.S., Barbosa, C.C.F., De Carvalho, L.A.S., Jorge, D.S.F., Lobo, F.L. & Novo, E.M.L.M. 2017, ‘Assessment of atmospheric correction methods for Sentinel-2 MSI images applied to Amazon floodplain lakes’, Remote Sensing, vol. 9, no. 4, art. 322.
Muchsin, F., Dirghayu, D., Prasasti, I., Rahayu, M.I., Fibriawati, L., Pradono, K.A. & Hendayani 2019, ‘Comparison of atmospheric correction models: FLAASH and 6S code and their impact on vegetation indices (case study: Paddy field in Subang District, West Java)’, IOP Conference Series: Earth and Environmental Science, vol. 303, art. 012003.
Müller-Wilm, U., Devignot, O. & Pessiot, L. 2018, S2 MPC Sen2Cor Configuration and User Manual, Ref. S2-PDGS-MPC-L2A-SUM-V2.5.5, European Space Agency, https://step.esa.int/thirdparties/sen2cor/2.5.5/docs/S2-PDGS-MPC-L2A-SUM-V2.5.5_V2.pdf.
Novo, E.M.L.M. 2010, Sensoriamento remoto: princípios e aplicações, Blucher, São Paulo.
Pahlevan, N., Schott, J.R., Franz, B.A., Zibordi, G., Markham, B., Bailey, S., Schaaf, C.B., Ondrusek, M., Greb, S. & Strait, C.M. 2017a, ‘Landsat 8 remote sensing reflectance (Rrs) products: Evaluations, intercomparisons, and enhancements’, Remote Sensing of Environment, vol. 190, pp. 289–301.
Pahlevan, N., Smith, B., Binding, C.E. & O’Donnell, D.M. 2017b, ‘Spectral band adjustments for remote sensing reflectance spectra in coastal/inland waters’, Optics Express, vol. 25, no. 23, p. 28650.
Pahlevan, N., Roger, J.-C. & Ahmad, Z. 2017c, ‘Revisiting short-wave-infrared (SWIR) bands for atmospheric correction in coastal waters’, Optics Express, vol. 25, no. 6, p. 6015.
Pereira-Sandoval, M., Ruescas, A., Urrego, P., Ruiz-Verdú, A., Delegido, J., Tenjo, C., Soria-Perpinyà, X., Vicente, E., Soria, J. & Moreno, J. 2019, ‘Evaluation of atmospheric correction algorithms over Spanish inland waters for Sentinel-2 multi spectral imagery data’, Remote Sensing, vol. 11, no. 12, art. 1463.
Ponzoni, F.J. & Shimabukuro, Y.E. 2010, Sensoriamento remoto no estudo da vegetação, Parêntese, São José dos Campos.
Ren, H., Zhou, G. & Zhang, F. 2018, ‘Using negative soil adjustment factor in soil-adjusted vegetation index (SAVI) for aboveground living biomass estimation in arid grasslands’, Remote Sensing of Environment, vol. 209, pp. 439–445.
Rumora, L., Miler, M. & Medak, D. 2020, ‘Impact of various atmospheric corrections on Sentinel-2 land cover classification accuracy using machine learning classifiers’, ISPRS International Journal of Geo-Information, vol. 9, no. 4, art. 222.
Satellite Imaging Corporation 2017, Atmospheric & Topographic Correction (ATCOR), viewed 19 July 2018, https://www.satimagingcorp.com/services/atcor/.
Schläpfer, D. & Richter, R. 2017, ‘Atmospheric correction of imaging spectroscopy data using shadow-based quantification of aerosol scattering effects’, EARSeL eProceedings, vol. 16, no. 1, pp. 21–28.
USGS - Earth Explorer 2018, Earth Explorer, viewed 19 July 2018, https://earthexplorer.usgs.gov/.
Wachendorf, M., Fricke, T. & Möckel, T. 2018, ‘Remote sensing as a tool to assess botanical composition, structure, quantity and quality of temperate grasslands’, Grass and Forage Science, vol. 73, no. 1, pp. 1–14.
Wang, D., Jiang, Y., Zhao, Y., Deng, J., Zhou, Y. & Wang, Y. 2019, ‘The assessment of Landsat-8 OLI atmospheric correction algorithms for inland waters’, Remote Sensing, vol. 11, no. 2, art. 199.
Warren, M.A., Simis, S.G.H., Martinez-Vicente, V., Poser, K., Bresciani, M., Alikas, K., Spyrakos, E., Giardino, C. & Ansper, A. 2019, ‘Assessment of atmospheric correction algorithms for the Sentinel-2A MultiSpectral Imager over coastal and inland waters’, Remote Sensing of Environment, vol. 225, pp. 267–289.
Wei, J., Lee, Z., Garcia, R., Zoffoli, L., Armstrong, R.A., Shang, Z., Sheldon, P. & Chen, R.F. 2018, ‘An assessment of Landsat-8 atmospheric correction schemes and remote sensing reflectance products’, Remote Sensing of Environment, vol. 215, pp. 18–32.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
This journal is licensed under a Creative Commons — Attribution 4.0 International — CC BY 4.0, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
