a b s t r a c tWe present here results that strongly support the use of MERIS-based NIR-red algorithms as standard tools for estimating chlorophyll-a (chl-a) concentration in turbid productive waters. The study was carried out as one of the steps in testing the potential of the universal applicability of previously developed NIR-red algorithms, which were earlier calibrated using a limited set of MERIS imagery and in situ data from the Azov Sea and the Taganrog Bay, Russia, and data that were synthetically generated using a radiative transfer model. We used an extensive set of MERIS imagery and in situ data collected over a period of three years in the Azov Sea and the Taganrog Bay for this validation task. We found that the two-band and three-band NIR-red algorithms gave consistently highly accurate estimates of chl-a concentration, with a mean absolute error of 4.32 mg m − 3 and 4.71 mg m − 3 , respectively, and a root mean square error as low as 5.92 mg m − 3 , for data with chl-a concentrations ranging from 1.09 mg m − 3 to 107.82 mg m − 3 . This obviates the need for case-specific reparameterization of the algorithms, as long as the specific absorption coefficient of phytoplankton in the water does not change drastically, and presents a strong case for the use of NIR-red algorithms as standard algorithms that can be routinely applied for near-real-time quantitative monitoring of chl-a concentration in the Azov Sea and the Taganrog Bay, and potentially elsewhere, which will be a real boon to ecologists, natural resource managers and environmental decision-makers. (W.J. Moses), agitelson2@unl.edu (A.A. Gitelson), berdnikov@ssc-ras.ru (S. Berdnikov), saprygin@ssc-ras.ru (V. Saprygin), povazhny@mmbi.krinc.ru (V. Povazhnyi).

We present here the results of chlorophyll-a (chl-a) concentration estimation using the red and near infrared (NIR) spectral bands of a Hyperspectral Imager for the Coastal Ocean (HICO) in productive turbid waters of the Azov Sea, Russia. During the data collection campaign in the summer of 2010 in Taganrog Bay and the Azov Sea, water samples were collected and concentrations of chl-a were measured analytically. The NIR-red models were tuned to optimize the spectral band selections and chl-a concentrations were retrieved from HICO data. The NIR-red three-band model with HICO-retrieved reflectances at wavelengths 684, 700, and 720 nm explained more than 85% of chl-a concentration variation in the range from 19.67 to 93.14 mg m −3 and was able to estimate chl-a with root mean square error below 10 mg m −3 . The results indicate the high potential of HICO data to estimate chl-a concentration in turbid productive (Case II) waters in real-time, which will be of immense value to scientists, natural resource managers, and decision makers involved in managing the inland and coastal aquatic ecosystems.

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.