Vol. 37, issue 07, article # 5

Abstract:

The purpose of the study is to describe the seasonal variability of optically active components in the Sea of Azov based on continuous information obtained from the combined MODIS-Aqua/Terra satellite observation product and a three-dimensional hydrodynamic model. The paper discusses the results of testing a method for restoring missing data in remote sensing images using the results of three-dimensional hydrodynamic simulation. The method has been tested for four main bio-optical parameters: the concentration of chlorophyll-a and pheopigments (TChl), indicators of light absorption by phytoplankton pigments (a_ph(678)) and non-living organic matter (a_CDM (438)), and indicator of backscattering of light (b_bp(438)). The results obtained from the combined product were compared with in situ observations carried out in April–May 2019 on the SRV Professor Vodyanitsky. The deviation of the average TChl values according to MODIS and simulation data relative to in situ observations was 1.8 and 2.2 mg × m^-3, respectively. Analysis of the calculated series of main bio-optical parameters obtained through regular assimilation of MODIS data into a hydrodynamic model made it possible to establish their seasonal variability for the central part of the Azov Sea in 2019. Among the studied bio-optical parameters, a clear seasonal variability of TChl stands out with an average annual value of 2.98 ± 1.22 mg × m^-3. Changes in a_CDM(438) and b_bp(438) are characterized by two periods of greatest values: spring (March–May) and autumn (August–October), with corresponding annual averages of 0.42 ± 0.15 and 0.10 ± 0.03 m^-1. Maximal changes in a_ph(678) are observed from July to October with an annual average of 0.04 ± 0.03 m^-1. The proposed method takes advantage of remote sensing data, which expand the capabilities of operational oceanological monitoring, and simulation data, which allow filling information gaps in these data. The results provide complete continuous data sets on the distribution of basic bio-optical indicators, which are crucial in predicting the ecological state of sea basins.

Keywords:

ocean color dataset, MODIS, hydrodynamic three-dimensional simulation, data assimilation, bio-optical parameters, the Sea of Azov

References:

1. Konik M., Kowalevsk M., Bradtke K., Darecki M. The operational method of filling information gaps in satellite imagery using numerical models // Int. J. Appl. Earth. Obs. Geoinf. 2019. V. 75. P. 66–82. DOI: 10.1016/j.jag.2018.09.002.
2. Kopelevich O.V., Burenkov V.I., Sheberstov S.V., Vazyulya S.V., Zavialov S.P. Bio-optical characteristics of the Russian seas from satellite ocean color data of 1998–2010 // Proc. of the VI International Conference “Current Problems in Optics of Natural Waters”. St.-Petersburg, 2011. P. 181–182.
3. Bailey S.W., Werdell P.J. A multi-sensor approach for the on-orbit validation of ocean color satellite data products // Remote Sensing of Environment. 2006. V. 102. N 1–2, P. 12–23. DOI: 10.1016/j.rse.2006.01.015.
4. Shul’ga T.Ya., Suslin V.V., Stanichnaya R.R. Numerical research of the pollution surface and deep-sea evolution in the Sea of Azov using satellite observation data // Phys. Oceanogr. 2017. V. 6. P. 36–46. DOI: 10.22449/1573-160X-2017-6-36-46.
5. Gidrometeorologiya i gidroximiya morei SSSR. V. V: Azovskoe more / pod red. N.P. Goptareva i dr. SPb.: Gidrometeoizdat, 1991. 234 p.
6. Gidrometeorologicheskie usloviya morei Ukrainy. V. 1: Azovskoe more / pod red. Yu.P. Il'ina i dr. Sevastopol', 2009. 400 p.
7. Suslin V.V., Churilova T.Ya., Efimova T.V., Moiseeva N.A., Skorokhod E.Yu., Stepochkin I.E. Regional bio-optical algorithm for remote estimation of the Sea of Azov's IOPs // Proc. SPIE. 2020. V. 11560. P. 115600R.
8. Efimova T.V., Churilova T.Ya., Skorokhod E.Yu., Moiseeva N.A., Zemlianskaia E.A. Vertical distribution of bio-optical properties of the Azov – Black Sea basin waters in April–May, 2019 // Phys. Oceanogr. 2020. V. 27, N 5. P. 525–534. DOI: 10.22449/1573-160X-2020-5-525-534.
9. Gidrometeorologicheskii spravochnik Azovskogo morya / pod red. A.A. Aksenova. L.: Gidrometeoizdat, 1962. 247 p.
10. NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Moderate-resolution Imaging Spectroradiometer (MODIS) Aqua Ocean Color Data; 2022 Reprocessing. NASA OB.DAAC, Greenbelt, MD, USA. DOI: 10.5067/AQUA/MODIS/L2/OC/2022.
11. NASA Goddard Space Flight Center, Ocean Ecology Laboratory, Ocean Biology Processing Group. Moderate-resolution Imaging Spectroradiometer (MODIS) Terra Ocean Color Data; 2018 Reprocessing. NASA OB.DAAC, Greenbelt, MD, USA. DOI: 10.5067/TERRA/MODIS/L2/OC/2018.
12. Suetin V.S. Suslin V.V., Korolev S.N., Kucheryavyj A.A. Assessment of the variability of the optical properties of water in the Black Sea in the summer of 1998 using SEAWIFS satellite equipment // Phys. Oceanogr. 2002. 12, N 6. P. 331–340. DOI: 10.1023/A:1021729229168.
13. Churilova T.Ya., Efimova T.V., Moiseeva N.A., Skorokhod E.Yu. Spectral light absorption coefficient of particles and colored dissolved organic matter in the Sea of Azov // Fundam. Appl. Hydrophys. 2022. 15, N 3. P. 73–83. DOI: 10.48612/fpg/ex1p-9vtp-phu8.
14. Kalman R.E. A New approach to linear filtering and prediction problem // J. Bas. Eng. 1960. V. 82, N 1. P. 35–45.
15. Klimova E.G. Uproshchennye modeli dlya rascheta kovariatsionnyx matrits v algoritme fil'tra Kalmana // Meteorol. i gidrol. 2000. N 6. P. 18–30.