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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 4, pp. 214-226

Classification of surface waters according to remote spectrometry data of visible range

B.L. Sukhorukov 1, 2 , N.V. Reshetnyak 1 
1 Hydrochemical Institute, Rostov-on-Don, Russia
2 Water Problems Institute RAS, South Division, Rostov-on-Don, Russia
Accepted: 25.07.2023
DOI: 10.21046/2070-7401-2023-20-4-214-226
The experimental data of long-term spectrometric measurements obtained on water bodies of Russia in the period from 2009 to 2020 are analyzed. Remote hyperspectral fieldwork was carried out from various platforms of the lower level from heights of 2 to 30 m. The main analyzed indicator was the spectrum of remote sensing reflectance (Rrs) of the radiation ascending from water in the visible region of the spectrum, 420–750 nm, with a spectral resolution of 1.8 nm. A classification graph of Rrs was constructed according to the shape of the spectra using the Ward’s method (divisive clustering). Average Rrs for each class and each group corresponding to the dendrogram are presented. The interpretation of Rrs of each of the groups is based on the knowledge of spectral features of optically active (visible) components of the aquatic ecosystem. The classification of Rrs according to purely spectral information makes it possible to single out three classes of spectra that obviously differ in shape. Further clustering is carried out within these classes using reference data obtained from analytical determination of water turbidity and trophicity of aquatic ecosystems. For waters of the second type, according to the spectral features, a total of 9 groups of Rrs were distinguished. A compact notation for each of the groups is proposed.
Keywords: surface waters, remote spectrometry, remote sensing reflectance, phytoplankton, chlorophyll, water ecosystem, clustering, dendrogram
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