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In this study, univariate linear regression model was developed for monitoring secchi depth in the Caspian Sea using level 1b and level 2 data. The in-situ measurements were carried outduring 25 one-day campaigns in the Caspian Sea. In this study, 25 level 1b MERIS images were utilized and then converted to level 2 data using C2R processor. The ability of univariate linear regression for the extraction of secchi depth from level 1b and level 2 wasinvestigated. The results demonstrated that the level 2 data lead to better performance than level 1b data. The level 2 data are able to retrieve the secchi depth in the Caspian Sea with correlation coefficient and percent of absolute error about 0.86 and 28.6%, respectively. Finally, secchi depth map of Caspian Sea was retrieved from MERIS level 2 images using developed univariate empirical model.

Chlorophyll-a is a main pigment for photosynthetic proccess in marine phytoplankton and by it either amount of biomass can be estimated or can be considered as an index for trophic level of aquatic ecosystem. In year 1996,81 secchi disc depth were measured and 1601 samples of chlorophyll-a were collected seasonaly from the southern Caspian Sea. The results showed that average chlorophyll-a in seasonals of spring, summer, autumn and winter were 1.072,0.550, 1.848,0.966 mg/m3 and depth of transparency were 6.42,8.86, 5.62 and 5.22m, respectively. The statistical analysis showed a significant correlation (P