The Azov Sea is a shallow inland sea adjoined by Ukraine on the west and Russia on the east, and gets most of its water input from the runoff from the Don River, the Kuban River, and several smaller rivers; thus, its water quality is heavily influenced by the fluvial runoff. Tremendous industrialisation in the last couple of decades, leading to increased influx of terrigenous nutrients, has produced significant eutrophication in the Azov Sea. This has caused drastic changes in the ecosystem, resulting in tremendous loss of fish population.
The Azov Sea is a shallow inland sea adjoined by Ukraine on the west and Russia on the east, and gets most of its water input from the runoff from the Don River, the Kuban River, and several smaller rivers; thus, its water quality is heavily influenced by the fluvial runoff. Tremendous industrialisation in the last couple of decades, leading to increased influx of terrigenous nutrients, has produced significant eutrophication in the Azov Sea. This has caused drastic changes in the ecosystem, resulting in tremendous loss of fish population.
The Team used an extensive set of MERIS level-2 Full Resolution images, hyperspectral data from HICO and in situ data collected over a period of three years in the Azov Sea and found that two-band and three-band NIR-red algorithms gave consistently highly accurate estimates of Chl-a concentration. The results present a strong case for using the MERIS-based NIR-red algorithms for estimating Chl-a concentration in turbid productive waters. The algorithms also yielded consistently accurate estimates of Chl-a concentration when applied to an extensive set of MERIS data from the Azov Sea and the Taganrog Bay, field spectrometer data from the Chesapeake Bay, Lake Kinneret, and several lakes in Nebraska, and a large dataset of synthetically generated reflectance data, without the need for reparameterization.