Operational MERIS-based NIR-red algorithms for estimating chlorophyll-a concentrations in coastal waters - The Azov Sea case study

We 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.

► NIR-red algorithms accurately estimate chlorophyll-a concentration. ► NIR-red algorithms do not need case-specific reparameterization. ► Spectral resolution of MERIS is sufficient for remote chlorophyll-a estimation. ► MERIS-based NIR-red algorithms can be standard tools for water quality monitoring.