We present here the results of calibrating and validating a three-band model and, its special case, a two-band model, which use MEdium Resolution Imaging Spectrometer (MERIS) reflectances in the red and near-infrared spectral regions for estimating chlorophyll-a (chl- a) concentration in inland, estuarine, and coastal turbid productive waters. During four data collection campaigns in 2008 and one campaign in 2009 in the Taganrog Bay and the Azov Sea, Russia, water samples were collected, and concentrations of chl-a and total suspended solids were measured in the laboratory. The data collected in 2008 were used for model calibration, and the data collected in 2009 were used for model validation. The models were applied to MERIS images acquired within two days from the date of in situ data collection. Two different atmospheric correction procedures were considered for processing the MERIS images. The results illustrate the high potential of the models to estimate chl-a concentration in turbid productive (Case II) waters in real time from satellite data, which will be of immense value to scientists, natural resource managers, and decision makers involved in managing the inland and coastal aquatic ecosystems.

Significance: Russian rivers are the predominant source of riverine mercury to the Arctic Ocean, where methylmercury biomagnifies to high levels in food webs. Pollution controls are thought to have decreased late–20th-century mercury loading to Arctic watersheds, but there are no published long-term observations on mercury in Russian rivers. Here, we present a unique hydrochemistry dataset to determine trends in Russian river particulate mercury concentrations and fluxes in recent decades. Using hydrologic and mercury deposition modeling together with multivariate time series analysis, we determine that 70 to 90% declines in particulate mercury fluxes were driven by pollution reductions and sedimentation in reservoirs. Results suggest that Russian rivers likely dominated over all other sources of mercury to the Arctic Ocean until recently.

We present here results that demonstrate the potential of near-infrared (NIR)-red models to estimate chlorophyll- a (chl- a) concentration in coastal waters using data from the spaceborne Hyperspectral Imager for the Coastal Ocean (HICO). Since the recent demise of the MEdium Resolution Imaging Spectrometer (MERIS), the use of sensors such as HICO has become critical for coastal ocean color research. Algorithms based on two- and three-band NIR-red models, which were previously used very successfully with MERIS data, were applied to HICO images. The two- and three-band NIR-red algorithms yielded accurate estimates of chl- a concentration, with mean absolute errors that were only 10.92% and 9.58%, respectively, of the total range of chl- a concentrations measured over a period of several months in 2012 and 2013 on the Taganrog Bay in Russia. Given the uncertainties in the radiometric calibration of HICO, the results illustrate the robustness of the NIR-red algorithms and validate the radiometric, spectral, and atmospheric corrections applied to HICO data as they relate to estimating chl- a concentration in productive coastal waters. Inherent limitations due to the characteristics of the sensor and its orbit prohibit HICO from providing anywhere near the level of frequent global coverage as provided by standard multispectral ocean color sensors. Nevertheless, the results demonstrate the utility of HICO as a tool for determining water quality in select coastal areas and the cross-sensor applicability of NIR-red models and provide an indication of what could be achieved with future spaceborne hyperspectral sensors in estimating coastal water quality.