We present and discuss here the results of our work using MODIS (moderate resolution imaging spectroradiometer) and MERIS (medium resolution imaging spectrometer) satellite data to estimate the concentration of chlorophyll-a (chl-a) in reservoirs of the Dnieper River and the Sea of Azov, which are typical case II waters, i.e., turbid and productive. Our objective was to test the potential of satellite remote sensing as a tool for near-real-time monitoring of chl-a distribution in these water bodies. We tested the performance of a recently developed three-band model, and its special case, a two-band model, which use the reflectance at red and near-infrared wavelengths for the retrieval of chl-a concentration. The higher spatial resolution and the availability of a spectral band at around 708 nm with the MERIS data offered great promise for these models. We compared results from several different atmospheric correction procedures available for MODIS and MERIS data. No one particular procedure was consistently and systematically better than the rest. Nevertheless, even in the absence of a perfect atmospheric correction procedure, both the three-band and the two-band models showed promising results when compared with in situ chl-a measurements. The challenges and limitations involved in satellite remote monitoring of the chl-a distribution in turbid productive waters are discussed.

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We present here results that demonstrate the potential of the recently launched Ocean and Land Colour Instrument (OLCI) onboard the satellite Sentinel-3A to deliver accurate estimates of chlorophyll-a (chl-a) concentration in coastal waters using reflectances in the red and near-infrared (NIR) spectral regions. Two-band and three-band NIR-red models that were previously used for data from the MEdium Resolution Imaging Spectrometer (MERIS) were applied to OLCI data from the Sea of Azov and the Taganrog Bay, Russia. Atmospherically corrected reflectance data from OLCI were compared to in situ reflectance data collected concurrently with a field spectrometer. Results show that the default atmospheric correction procedure currently applied to OLCI data performs well in preserving the spectral shape of chl-a-specific reflectance features in the red and NIR regions. Similar to what was achieved with MERIS data, the NIR-red models yield accurate estimates of chl-a concentration, with accuracies on the order of 90%, though the parameters of the NIR-red algorithms based on OLCI data are slightly different from what was obtained with MERIS data. More data, from various geographical locations, need to be analyzed to establish robust NIR-red algorithms for OLCI data.