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The potential of different reflectance-based algorithms to retrieve phycocyanin concentration through remote sensing. Application to a hypereutrophic Mediterranean lake
* 1 , 2 , 3 , 4
1  universit√© de tours
2  Lebanese CNRS, Remote sensing center
3  Lebanese university
4  Lebanese CNRS
Academic Editor: Luca Lelli

https://doi.org/10.3390/ECRS2023-16840 (registering DOI)
Abstract:

Eutrophication of lakes promotes the development of cyanobacterial blooms that threaten aquatic environment and human health. They can produce cyanotoxins that prevents the proper use of water, result in human intoxication and fish kill. Cyanobacterial biomass can be estimated using traditional field sampling techniques, laboratory analysis, and cell counting method. Despite being accurate, this method is time-consuming, labor-intensive, and cost-ineffective. Remote sensing is considered an alternative monitoring method that is cost and time efficient, and feasible for repetitive and continuous monitoring. The aim of this research is to test the potential of various algorithms (models) to retrieve phycocyanin concentration in a Mediterranean Lake after comparison with standard classical methods. For that, field spectroradiometric measurements to produce spectral signatures, and field sampling were performed during 2016 and 2017. Field and laboratory analysis showed that phycocyanin concentration varied between 18 and 170 µg/l during the different field campaigns in 2016 and 2017. Phycocyanin was heterogenous throughout the lake and showed considerable variation in 02 November 2016. Two main cyanobacterial genera (Microcystis sp. and Chrysoporum sp.) were identified during the campaigns in which field spectroradiometer measurements were performed in 2016 and 2017. The potential of 10 developed algorithms was tested to retrieve phycocynanin concentration. Results obtained proved that various ratio-models can be used for the estimation of phycocyanin with the model R700/R600 (reflectance ratio of wavelengths 700 and 600) being the most suitable model presenting the highest coefficient of correlation (R2 = 0.716). The importance of these algorithms is that they can be used as indicators to choose between different satellite imagery to map cyanobacterial blooms, based on their visible and NIR bands. The opportunities of using different potential satellite images from Worldview-2, Sentinel-2 images, and to a lesser extent from Landsat-8 images is to directly derive phycocyanin to help map and manage cyanobacterial blooms.

Keywords: Spectroradiometer; Cyanobacteria, Algorithms, Water, Satellite images
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