Seasonal variability of the main optically active components of the marine environment according to remote sensing and simulation data
T.Ya. Shul’ga, V.V. Suslin
Federal State Budget Scientific Institution Federal Research Centre Marine Hydrophysical Institute of RAS, Sevastopol, Russia
Keywords: ocean color dataset, MODIS, hydrodynamic three-dimensional simulation, data assimilation, bio-optical parameters, the Sea of Azov
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.
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