Assessing ESA Climate Change Initiative data for the monitoring of phytoplankton abundance and phenology in deep lakes: investigation on Lake Geneva

Bonnier, Mona; Anneville, Orlane; Iestyn Woolway, R.; Thackeray, Stephen J. ORCID: https://orcid.org/0000-0003-3274-2706; Morin, Guillaume P.; Reynaud, Nathalie; Soulignac, Frédéric; Tormos, Thierry; Harmel, Tristan. 2024 Assessing ESA Climate Change Initiative data for the monitoring of phytoplankton abundance and phenology in deep lakes: investigation on Lake Geneva. Journal of Great Lakes Research, 50 (4), 102372. 14, pp. https://doi.org/10.1016/j.jglr.2024.102372

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Official URL: http://dx.doi.org/10.1016/j.jglr.2024.102372

Abstract/Summary

Lake water quality assessment requires quantification of phytoplankton abundance. Optical satellite imagery allows us to map this information within the entire lake area. The ESA Climate Change Initiative (ESA-CCI) estimates Chl-a concentrations, based on medium resolution satellite data, on a global scale. Chl-a concentrations provided by the ESA-CCI consortium were analyzed to assess their representativeness for water quality monitoring and subsequent phenology studies in Lake Geneva. Based on vertically resolved in-situ data, those datasets were evaluated through match-up comparisons. Because the underlying algorithms do not take into account the vertical distribution of phytoplankton, a specific analysis was performed to evaluate any potential biases in remote sensing estimation, and consequences for observed phenological trends. Different approaches to data averaging were performed to reconstruct Chl-a estimates provided by the remote sensing algorithms. Strong correlation (R-value > 0.89) and acceptable discrepancies (rmse ∼ 1.4 mg.m−3) were observed for the ESA-CCI data. This approach permitted recalibration of the ESA CCI data for Lake Geneva. Finally, merging satellite and in-situ data provided a consistent time series for long term analysis of phytoplankton phenology and its interannual variability since 2002. This combination of in-situ and satellite data improved the temporal resolution of the time series, enabling a more accurate identification of the timing of specific spring events characterising phytoplankton phenology.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.jglr.2024.102372
UKCEH and CEH Sections/Science Areas:	Water Resources (Science Area 2017-)
ISSN:	0380-1330
Additional Information. Not used in RCUK Gateway to Research.:	Open Access paper - full text available via Official URL link.
Additional Keywords:	water quality, earth observation, peri-alpine lake, long-term trends, remote sensing, chlorophyll a
NORA Subject Terms:	Ecology and Environment Hydrology Data and Information
Date made live:	10 Jun 2024 11:01 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/537538

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.jglr.2024.102372

UKCEH and CEH Sections/Science Areas:

Water Resources (Science Area 2017-)

ISSN:

0380-1330

Additional Information. Not used in RCUK Gateway to Research.:

Open Access paper - full text available via Official URL link.

Additional Keywords:

water quality, earth observation, peri-alpine lake, long-term trends, remote sensing, chlorophyll a