Algorithm for Additional Correction of Remote Sensing Reflectance in the Presence of Absorbing Aerosol: Case Study

E. B. Shybanov, A. S. Papkova✉

Marine Hydrophysical Institute of RAS, Sevastopol, Russian Federation

✉ e-mail: hanna.papkova@gmail.com

Abstract

Purpose. The main goal of this work is to develop an algorithm for additional correction of Level 2 remote sensing reflectance ocean color satellite data, taking into account the presence of absorbing aerosol over the Black Sea, where a large number of dust transfers from the Sahara are observed annually.

Methods and Results. The research method is based on the comparison of satellite data about remote sensing reflectance from MODIS-Aqua/Terra scanner and in situ measurements from AErosol ROboties NETwork Ocean Color (AERONET-OC) stations. The Python mathematical package was used for the data processing: analysis and visualization of satellite images were made in SeaDAS. As a basis for an additional correction algorithm, theoretical calculations were provided to take into account aerosol stratification in the radiative transfer equation, it is shown that for absorbing aerosol the atmospheric correction error is proportional to λ−4. The analytical conclusions were confirmed during the validation of the satellite and the in situ measurements using principal component analysis (PCA). The new algorithm is based on the constancy of the color index value, characteristic of the selected region. For the Black Sea, the average value of color index at 412 and 443 nm (CI(412/443)) is approximately equal to 0.80 ± 0.08, a small standard deviation indicates that the sample is slightly variable and considered as the reference value.

Conclusions. The model values of the remote sensing reflectance (Rrs) had a better agreement with the in situ values than the satellite Rrs(λ) at Level 2. In the case of the dust aerosol presence, the developed model increases the coefficient of determination between the satellite and the in situ values of Rrs(λ) by more than twice at 412 nm, the difference is also noticeable at 443 and 488 nm. The color indices calculated from the model values of Rrs(λ), which are necessary for calculating chlorophyll a, are also in better agreement with the AERONET data (an increase in correlation by 20 %).

Keywords

optical characteristics, chlorophyll a concentration, ocean color, seawater, absorbing aerosol, dust, MODIS-Aqua, AERONET, Black Sea

Acknowledgements

The work was carried out within the framework of the state task of the MHI RAS on the theme FNNN-2021-0003 “Development of operational oceanology methods based on interdisciplinary research of processes of the marine environment formation and evolution and on mathematical modeling using data of remote and contact measurements” (“Operational Oceanology” code). The authors thank Giuseppe Zibordi for processing the measurements obtained at Galata_Platform and Gloria AERONET stations, and for the possibility of using high-quality in situ ocean color measurements; and NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group (2018) for the information provided from MODIS-Aqua satellite in Ocean Color.

For citation

Shybanov, E.B. and Papkova, A.S., 2022. Algorithm for Additional Correction of Remote Sensing Reflectance in the Presence of Absorbing Aerosol: Case Study. Physical Oceanography, 29(6), pp. 688-706. doi:10.22449/1573-160X-2022-6-688-706

DOI

10.22449/1573-160X-2022-6-688-706

References

  1. Werdell, P.J. and Bailey, S.W., 2005. An Improved In-Situ Bio-Optical Data Set for Ocean Color Algorithm Development and Satellite Data Product Validation. Remote Sensing of Environment, 98(1), pp. 122-140. https://doi.org/10.1016/j.rse.2005.07.001
  2. Gordon, H.R., 2021. Evolution of Ocean Color Atmospheric Correction: 1970–2005. Remote Sensing, 13(24), 5051. https://doi.org/10.3390/rs13245051
  3. Thuillier, G., Hersé, M., Labs, D., Foujols, T., Peetermans, W., Gillotay, D., Simon, P.C. and Mandel, H., 2003. The Solar Spectral Irradiance from 200 to 2400 nm as Measured by the SOLSPEC Spectrometer from the Atlas and Eureca Missions. Solar Physics, 214(1), pp. 1-22. doi:10.1023/A:1024048429145
  4. Viollier, M., 1982. Radiometric calibration of the Coastal Zone Color Scanner on Nimbus 7: a proposed adjustment. Applied Optics, 21(6), pp. 1142-1145. https://doi.org/10.1364/AO.21.001142
  5. Gordon, H.R., 1989. Can the Lambert-Beer Low Be Applied to the Diffuse Attenuation Coefficient of Ocean Water? Limnology and Oceanography, 34(8), pp. 1389-1409. https://doi.org/10.4319/lo.1989.34.8.1389
  6. Deschamps, P.Y., Herman, M. and Tanre, D., 1983. Modeling of the Atmospheric Effects and Its Application to the Remote Sensing of Ocean Color. Applied Optics, 22(23), pp. 3751-3758. https://doi.org/10.1364/AO.22.003751
  7. Gordon, H.R. and Wang, M., 1994. Retrieval of Water-Leaving Radiance and Aerosol Optical Thickness over the Oceans with SeaWiFS: A Preliminary Algorithm. Applied Optics, 33(3), pp. 443-452. https://doi.org/10.1364/AO.33.000443
  8. Korchemkina, E.N., Shybanov, E.B. and Lee, M.E., 2009. Atmospheric Correction Improvement for the Remote Sensing of Black Sea Waters. Issledovanie Zemli iz Kosmosa, (6), pp. 2430 (in Russian).
  9. Lee, S. and Meister, G., 2017. MODIS Aqua Optical Throughput Degradation Impact on Relative Spectral Response and Calibration of Ocean Color Products. IEEE Transactions on Geoscience and Remote Sensing, 55(9), pp. 5214-5219. https://doi.org/10.1109/TGRS.2017.2703672
  10. Kalinskaya, D.V. and Papkova, A.S., 2022. Why Is It Important to Consider Dust Aerosol in the Sevastopol and Black Sea Region during Remote Sensing Tasks? A Case Study. Remote Sensing, 14(8), 1890. https://doi.org/10.3390/rs14081890
  11. Suetin, V.S and Korolev, S.N., 2021. Application of Satellite Data for Retrieving the Light Absorption Characteristics in the Black Sea Waters. Physical Oceanography, 28(2), pp. 205214. https://doi.org/10.22449/1573-160X-2021-2-205-214
  12. Omar, A.H., Winker, D.M., Vaughan, M.A., Hu, Y., Trepte, C.R., Ferrare, R.A., Lee, K., Hostetler, C.A., Kittaka, C., Rogers, R.R., Kuehn, R.E. and Liu, Z., 2009. The CALIPSO Automated Aerosol Classification and Lidar Ratio Selection Algorithm. Journal of Atmospheric and Oceanic Technology, 26(10), pp. 1994-2014. https://doi.org/10.1175/2009JTECHA1231.1
  13. Gordon, H.R., Du, T. and Zhang, T., 1997. Remote Sensing of Ocean Color and Aerosol Properties: Resolving the Issue of Aerosol Absorption. Applied Optics, 36(33), pp. 8670-8684. https://doi.org/10.1364/AO.36.008670
  14. Chomko, R.M. and Gordon, H.R., 1998. Atmospheric Correction of Ocean Color Imagery: Use of the Junge Power-Law Aerosol Size Distribution with Variable Refractive Index to Handle Aerosol Absorption. Applied Optics, 37(24), pp. 5560-5572. https://doi.org/10.1364/AO.37.005560
  15. Antoine, D. and Morel, A., 1999. A Multiple Scattering Algorithm for Atmospheric Correction of Remotely Sensed Ocean Color (MERIS Instrument): Principle and Implementation for Atmospheres Carrying Various Aerosol Including Absorbing Ones. International Journal of Remote Sensing, 20(9), pp. 1875-1916. https://doi.org/10.1080/014311699212533
  16. Morel, A. and Prieur, L., 1977. Analysis of Variations in Ocean Color. Limnology and Oceanography, 22(4), pp. 709-722. https://doi.org/10.4319/lo.1977.22.4.0709
  17. Themistocleous, K., Papoutsa, C., Michaelides, S. and Hadjimitsis, D., 2020. Investigating Detection of Floating Plastic Litter from Space Using Sentinel-2 Imagery. Remote Sensing, 12(16), 2648. https://doi.org/10.3390/rs12162648
  18. Parshikov, S.V.and Li, M.E., 1992. Remote Sensing of Optically Active Impurities Using the Short-wave-length Part of the Spectrum. In: MHI, 1992. Automated Systems for Monitoring the State of the Marine Environment. Sevastopol, MHI NAS of Ukraine, 1992. P. 65-78.
  19. Shibanov, E.B. and Korchemkina, E.N., 2008. Retrieving of the Biooptical Characteristics of Black-Sea Waters under the Conditions of Constant Reflectance at a Wavelength of 400 nm. Physical Oceanography, 18(1), pp. 25-37. https://doi.org/10.1007/s11110-008-9007-5
  20. Smirnov, A., Holben, B.N., Slutsker, I., Giles, D.M., McClain, C.R., Eck, T.F., Sakerin, S.M., Macke, A., Croot, P. [et al.], 2009. Maritime Aerosol Network as a Component of Aerosol Robotic Network. Journal of Geophysical Research: Atmospheres, 114(D6), D06204. doi:10.1029/2008JD011257
  21. Zibordi, G., Mélin, F., Berthon, J., Holben, B., Slutsker, I., Giles, D., D’Alimonte, D., Vandemark, D., Feng, H. [et al.], 2009. AERONET-OC: A Network for the Validation of Ocean Color Primary Products. Journal of Atmospheric and Oceanic Technology, 26(8), pp. 1634-1651. https://doi.org/10.1175/2009JTECHO654.1
  22. O'Reilly, J.E., Maritorena, S., Mitchell, B.G., Siegel, D.A., Carder, K.L., Garver, S.A., Kahru, M. and McClain, C., 1998. Ocean Color Chlorophyll Algorithms for SeaWiFS. Journal of Geophysical Research: Oceans, 103(C11), pp. 24937-24953. doi:10.1029/98JC02160
  23. Morel, A. and Maritorena, S., 2001. Bio-Optical Properties of Oceanic Waters: A Reappraisal. Journal of Geophysical Research: Oceans, 106(C4), pp. 7163-7180. doi:10.1029/2000jc000319
  24. Preisendorfer, R.W., 1976. Hydrologic Optics. Vol. II, Foundations. Chapter 3, The Interaction Principle. U.S. Department of Commerce, pp. 188-400.
  25. Kattawar, G.W., Plass, G.N. and Catchings, F.E., 1973. Matrix Operator Theory of Radiative Transfer. 2: Scattering from Maritime Haze. Applied Optics, 12(5), pp. 1071-1084. https://doi.org/10.1364/AO.12.001071
  26. Shibanov, E.B., 2005. Numerical Method for the Solution of the Equation of Radiation Transfer. Reflection and Transmission Coefficients for an Optically Thin Plane-Parallel Layer. Physical Oceanography, 15(3), pp. 192-202. doi:10.1007/s11110-005-0041-2
  27. Schoeberl, M.R. and Newman, P.A., 1995. A Multiple-Level Trajectory Analysis of Vortex Filaments. Journal of Geophysical Research: Atmospheres, 100(D12), pp. 25801-25815. https://doi.org/10.1029/95JD02414
  28. Werdell, P.J., Bailey, S., Fargion, G., Pietras, C., Knobelspiesse, K., Feldman, G. and McClain, C., 2003. Unique Data Repository Facilitates Ocean Color Satellite Validation. Eos, Transactions, American Geophysical Union, 84(38), pp. 377-387. https://doi.org/10.1029/2003EO380001
  29. Kopelevich, O.V., Sheberstov, S.V., Yunev, O., Basturk, O., Finenko, Z.Z., Nikonov, S. and Vedernikov, V.I., 2002. Surface Chlorophyll in the Black Sea over 1978-1986 Derived from Satellite and In Situ Data. Journal of Marine Systems, 36(3–4), pp. 145-160. https://doi.org/10.1016/S0924-7963(02)00184-7
  30. Zibordi, G., Mélin, F., Berthon, J.-F. and Talone, M., 2015. In Situ Autonomous Optical Radiometry Measurements for Satellite Ocean Color Validation in the Western Black Sea. Ocean Science, 11(2), pp. 275-286. doi:10.5194/os-11-275-2015
  31. Stagl, J.C. and Hattermann, F.F., 2015. Impacts of Climate Change on the Hydrological Regime of the Danube River and Its Tributaries Using an Ensemble of Climate Scenarios. Water, 7(11), pp. 6139-6172. https://doi.org/10.3390/w7116139
  32. Kopelevich, O.V., Burenkov, V.I., Sheberstov, S.V., Vazyulya, S.V., Kravchishina, M.D., Pautova, L., Silkin, V.A., Artemiev, V.A. and Grigoriev, A., 2014. Satellite Monitoring of Coccolithophore Blooms in the Black Sea from Ocean Color Data. Remote Sensing of Environment, 146, pp. 113-123. https://doi.org/10.1016/j.rse.2013.09.009

Download the article (PDF)