Abstract
Harmful algal blooms of cyanobacteria are increasing in magnitude and frequency globally, degrading inland and coastal aquatic ecosystems and adversely affecting public health. Efforts to understand the structure and natural variability of these blooms range from point sampling methods to a wide array of remote sensing tools. This study aims to provide a comprehensive view of cyanobacterial blooms in Clear Lake, California — a shallow, polymictic, naturally eutrophic lake with a long record of episodic cyanobacteria blooms. To understand the spatial heterogeneity and temporal dynamics of cyanobacterial blooms, we evaluated a satellite remote sensing tool for estimating coarse cyanobacteria distribution with coincident, in situ measurements at varying scales and resolutions. The Cyanobacteria Index (CI) remote sensing algorithm was used to estimate cyanobacterial abundance in the top portion of the water column from data acquired from the Ocean and Land Color Instrument (OLCI) sensor on the Sentinel-3a satellite. We collected hyperspectral data from a handheld spectroradiometer; discrete 1 m integrated surface samples for chlorophyll-a and phycocyanin; multispectral imagery from small Unmanned Aerial System (sUAS) flights (∼12 cm resolution); Autonomous Underwater Vehicle (AUV) measurements of chlorophyll-a, turbidity, and colored dissolved organic matter (∼10 cm horizontal spacing, 1 m below the water surface); and meteorological forcing and lake temperature data to provide context to our cyanobacteria measurements. A semivariogram analysis of the high resolution AUV and sUAS data found the Critical Scale of Variability for cyanobacterial blooms to range from 70 to 175 m, which is finer than what is resolvable by the satellite data. We thus observed high spatial variability within each 300 m satellite pixel. Finally, we used the field spectroscopy data to evaluate the accuracy of both the original and revised CI algorithm. We found the revised CI algorithm was not effective in estimating cyanobacterial abundance for our study site. Satellite-based remote sensing tools are vital to researchers and water managers as they provide consistent, high-coverage data at a low cost and sampling effort. The findings of this research support continued development and refinement of remote sensing tools, which are essential for satellite monitoring of harmful algal blooms in lakes and reservoirs.
Highlights
Harmful algal blooms of toxin-producing cyanobacteria are increasing in magnitude and frequency globally, both degrading aquatic ecosystems and posing a risk to public health (Havens, 2008; Cheung et al, 2013; Taranu et al, 2015; Huisman et al, 2018; Ho et al, 2019)
We find the Autonomous Underwater Vehicle (AUV) fluorometer results correspond to a Critical Scale of Variability (CSV) of 70–100 m and the small Unmanned Aerial System (sUAS)-derived chlorophyll-a concentrations correspond to a CSV of approximately 175 m
In addition to the challenges associated with fluorometry measurements on the AUV, we found difficulties with the other remote sensing methods employed in this research
Summary
Harmful algal blooms of toxin-producing cyanobacteria (cyanoHABs) are increasing in magnitude and frequency globally, both degrading aquatic ecosystems and posing a risk to public health (Havens, 2008; Cheung et al, 2013; Taranu et al, 2015; Huisman et al, 2018; Ho et al, 2019). Monitoring these cyanoHABs is necessary to track their development and mitigate their effects in the context of a changing climate (Paerl et al, 2016; Visser et al, 2016). Even though spatial variability of cyanoHABs is a known issue with regards to remote sensing of blooms as measurements can vary substantially within a satellite pixel (Kutser, 2009), satellite remote sensing tools remain one of the key tools for monitoring cyanoHABs because of their global coverage
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
