Seasonal Variability of the Main Optically Active Components of the Marine Environment According to Remote Sensing and Simulation Data

The purpose of the study is to describe the seasonal variability of optically active components in the Sea of Azov based on data 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 retrieving missing data in remote sensing images from the results of three-dimensional hydrodynamic simulation. The method has been tested for four main biooptical parameters: the concentration of chlorophyll-a and pheopigments (TChl), coefficients of light absorption by phytoplankton pigments (a(ph)(678)) and non-living organic matter (a(CDM)(438)), and light backscattering coefficient (b(bp)(438)). The results derived from the combined product were compared with in situ observations carried out in April-May 2019 at the scientific research vessel Professor Vodyanitsky. The deviations of the average TChl values according to MODIS and simulation data from in situ observations was 1.8 and 2.2 mg m(-3), respectively. The analysis of the calculated series of main biooptical parameters derived through regular assimilation of MODIS data into a hydrodynamic model made it possible to ascertain their seasonal variability in the central part of the Azov Sea in 2019. Among the biooptical parameters under study, the pronounced seasonal variability of TChl stands out with, an average annual of 2.98 +/- 1.22 mg m(-3). Changes in a(CDM)(438) and b(bp)(438) are characterized by two periods of maximal 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 suggested approach uses advantage of remote sensing data, which expand the capabilities of operational oceanological monitoring, and simulation data, which enable filling gaps in these data. The results provide complete continuous data sets on the distribution of main biooptical indicators, which are crucial in predicting the ecological state of sea basins.