Cyanobacterial pigment concentrations in inland waters: Novel semi-analytical algorithms for multi- and hyperspectral remote sensing data

Pravin Jeba Dev, Assaf Sukenik, Deepak R. Mishra, Ilia Ostrovsky

Research output: Contribution to journalArticlepeer-review

Abstract

Cyanobacteria are notorious for producing harmful algal blooms that present an ever-increasing serious threat to aquatic ecosystems worldwide, impacting the quality of drinking water and disrupting the recreational use of many water bodies. Remote sensing techniques for the detection and quantification of cyanobacterial blooms are required to monitor their initiation and spatiotemporal variability. In this study, we developed a novel semi-analytical approach to estimate the concentration of cyanobacteria-specific pigment phycocyanin (PC) and common phytoplankton pigment chlorophyll a (Chl a) from hyperspectral remote sensing data. The PC algorithm was derived from absorbance-concentration relationship, and the Chl a algorithm was devised based on a conceptual three-band structure model. The developed algorithms were applied to satellite imageries obtained by the Hyperspectral Imager for the Coastal Ocean (HICO™) sensor and tested in Lake Kinneret (Israel) during strong cyanobacterium Microcystis sp. bloom and out-of-bloom times. The sensitivity of the algorithms to errors was evaluated. The Chl a and PC concentrations were estimated with a mean absolute percentage difference (MAPD) of 16% and 28%, respectively. Sensitivity analysis shows that the influences of backscattering and other water constituents do not affect the estimation accuracy of PC (~2% MAPD). The reliable PC/Chl a ratios can be obtained at PC concentrations above 10 mg m−3. The computed PC/Chl a ratio depicts the contribution of cyanobacteria to the total phytoplankton biomass and permits investigating the role of ambient factors in the formation of a complex planktonic community. The novel algorithms have extensive practical applicability and should be suitable for the quantification of PC and Chl a in aquatic ecosystems using hyperspectral remote sensing data as well as data from future multispectral remote sensing satellites, if the respective bands are featured in the sensor.

Original languageEnglish
Article number150423
JournalScience of the Total Environment
Volume805
DOIs
StatePublished - 20 Jan 2022
Externally publishedYes

Bibliographical note

Funding Information:
We thank the anonymous reviewers and editor for their careful reading of our manuscript and their useful comments and suggestions. We also thank Theenathayalan Varunan and Rakesh Kumar Singh for helpful discussions. We used the Kinneret Limnological Laboratory database for phytoplankton and chlorophyll (courtesy T. Zohary), which is a part of the Lake Kinneret ecological monitoring program sponsored by the Israel Water Authority. This work was supported by Japan-Israel Cooperative Scientific Research Program of the Ministry of Science and Technology (MOST), grant 3-14952.

Funding Information:
We thank the anonymous reviewers and editor for their careful reading of our manuscript and their useful comments and suggestions. We also thank Theenathayalan Varunan and Rakesh Kumar Singh for helpful discussions. We used the Kinneret Limnological Laboratory database for phytoplankton and chlorophyll (courtesy T. Zohary), which is a part of the Lake Kinneret ecological monitoring program sponsored by the Israel Water Authority. This work was supported by Japan-Israel Cooperative Scientific Research Program of the Ministry of Science and Technology (MOST), grant 3-14952 .

Publisher Copyright:
© 2021 Elsevier B.V.

Keywords

  • Chlorophyll a
  • CyanoHAB
  • HICO™
  • Lake Kinneret
  • Microcystis
  • Phycocyanin

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • Waste Management and Disposal
  • Pollution

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