Quantifying Scales of Spatial Variability of Cyanobacteria in a Large, Eutrophic Lake Using Multiplatform Remote Sensing Tools

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.

Wildfires in the western United States are mobilizing PM2.5-associated nutrients and may be contributing to downwind cyanobacteria blooms

“…68 Satellites measure the spectral shape at 681 nm that covers spectral peaks in chlorophyll absorption and fluorescence, and a spectral shape at 620 nm which is sensitive to phycocyanin. 69,70 Phycocyanin is used to distinguish cyanobacteria from phytoplankton. 70–72…”

Turning up the heat: Long‐term water quality responses to wildfires and climate change in a hypereutrophic lake

“…Originally referred to as (Ha)Ka-ba-tin, or Lupiyoma, meaning "Large or Big Water" by the local, indigenous Pomo (Clark & Williams, 1954;Mauldin, 1960), Clear Lake is the largest naturally formed lake located completely in California. Clear Lake is used for drinking water, irrigation, and recreation, as well as tribal cultural use and activities, but it is also a hypereutrophic lake with a history of high total phosphorus (TP) concentrations and cyanobacterial blooms (Richerson et al, 1994(Richerson et al, , 2008Sharp et al, 2021;Suchanek et al, 2002). The large extent and proximity of the fires to Clear Lake raised concerns among residents and water managers about the shortand long-term effects of the Mendocino Complex on lake water quality and associated ecosystem services.…”

Assessment of trophic state in Lake Malombe, Malawi, using Sentinel-2 Multi-Spectral Instrument imagery

“…Chl-a -a lightharvesting pigment in photosynthetic organisms, including algae, was used as a proxy for Lake Malombe's PP. The spectral shape algorithm proposed by Wynne et al (2008) was applied to derive the Chl-a index and estimate algal abundance in the lake (Sharp et al, 2021). However, we noted that MSI does not have bands used for the algorithm.…”