статья в журнале

En

Izvestiya - Atmospheric and Ocean Physics

ISSN:00014338

2022

10.1134/S0001433822090158

Abstract: In this paper we use data from the Hyperspectral Imager for the Coastal Ocean (HICO, NASA) hyperspectral satellite sensor to identify and qualitatively estimate the content of cyanobacteria (CB) in the coastal waters of the Black and Azov seas. Three data processing algorithms are applied: two use a spectrum shape analysis and one is semianalytical. The first algorithm uses the determination of the minimum of the reflection coefficient spectrum for remote sensing in the spectral region of ~680 nm, the so-called CB presence index. Based on this algorithm, a second was developed to identify the presence of phycocyanin in water, which is a marker pigment for CB. The second algorithm analyzes the minimum of the reflectance spectrum for remote sensing (Rrs) in the region of 620 nm. The third semianalytical algorithm makes it possible to determine the concentration of phycocyanin. The results of automatic identification of the presence of the CB were compared with the visual analysis of the spectra. The results of applying three algorithms to images are compared. It is concluded that phycocyanin is present in the studied water areas and its presence is a consequence of the presence of CB. © 2022, Pleiades Publishing, Ltd.

Azov Sea, Black Sea, CB, HICO, Hyperspectral satellite data, phycocyanin

2023-03-10 11:54:44