A change in the spectral shape at 681 nm is used to distinguish blooms of cyanobacteria from blooms of other phytoplankton via MERIS satellite sensor imagery. During large cyanobacterial blooms, the spectral shape around 681 nm is not a positive quantity as scattering due to cyanobacteria overwhelms the fluorescence signal, thus creating a negative spectral shape. This relationship is consistent in both remotely sensed and in situ data.

Lake Erie, the world’s tenth largest freshwater lake by area, has had recurring blooms of toxic cyanobacteria for the past two decades. These blooms pose potential health risks for recreation, and impact the treatment of drinking water. Understanding the timing and distribution of the blooms may aid in planning by local communities and resources managers. Satellite data provides a means of examining spatial patterns of the blooms. Data sets from MERIS (2002–2012) and MODIS (2012–2014) were analyzed to evaluate bloom patterns and frequencies. The blooms were identified using previously published algorithms to detect cyanobacteria (~25,000 cells mL⁻¹), as well as a variation of these algorithms to account for the saturation of the MODIS ocean color bands. Images were binned into 10-day composites to reduce cloud and mixing artifacts. The 13 years of composites were used to determine frequency of presence of both detectable cyanobacteria and high risk (>100,000 cells mL⁻¹) blooms. The bloom season according to the satellite observations falls within June 1 and October 31. Maps show the pattern of development and areas most commonly impacted during all years (with minor and severe blooms). Frequencies during years with just severe blooms (minor bloom years were not included in the analysis) were examined in the same fashion. With the annual forecasts of bloom severity, these frequency maps can provide public water suppliers and health departments with guidance on the timing of potential risk.

A spectral shape algorithm applied to Medium Resolution Imaging Spectrometer (MERIS) imagery has detected cyanobacterial blooms, with extensive examples in Lake Erie. The detection algorithm uses an approximation of the second derivative as a measure of spectral shape around the 681 nm band S _2d(681). With the end of the MERIS mission on 8 April 2012, an analogue was developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) to continue monitoring for these blooms. The MODIS analogue uses the standard ρ_s (Rayleigh-corrected reflectance) to determine S _2d(678), which is computationally equivalent to the negative of the MODIS fluorescent line height (FLH). A comparison was made of the two products from image pairs during a period of relatively severe blooms of cyanobacteria (2008–2011). When the MODIS bands do not saturate due to surface scums from high cyanobacteria biomass or conditions of glint or dense aerosols, the algorithms produce comparable results with a linear transform of the MODIS S _2d(678). The results indicate that MODIS can be used to monitor these blooms. Dense cyanobacteria blooms will produce negative FLH showing a limitation of FLH for bloom detection. The S _2d(678) offers a tool to support monitoring for dense algal blooms.

The ability to forecast the transport of harmful cyanobacterial blooms in the Laurentian Great Lakes is beneficial to natural resource managers concerned with public health. This manuscript describes a method that improves the prediction of cyanobacterial bloom transport with the use of a preoperational hydrodynamic model and high temporal resolution satellite imagery. Two scenarios were examined from separate cyanobacterial blooms in western Lake Erie, USA. The first scenario modeled bloom position and extent over the span of 13 days. A geographic center, or centroid, was calculated and assigned to the bloom from observed satellite imagery. The bloom centroid was projected forward in time, and the projected position was compared to the final observed bloom centroid. Image pixels flagged as cyanobacterial bloom were compared between the initial image and the final image, and this was assumed as persistence. The second bloom scenario was modeled for a period of 12 days, and the results were framed in an ecological context in an effort to gain further understanding of cyanobacterial bloom dynamics. These modeling techniques can be incorporated into an operational forecasting system.

Satellite imagery has been used to monitor and assess Harmful Algal Blooms (HABs), specifically, cyanobacterial blooms in Lake Erie (the USA and Canada) for over twelve years. In recent years, imagery has been applied to the other Great Lakes as well as other U.S. lakes. The key algorithm used in this monitoring system is the cyanobacterial index (CI), a measure of the chlorophyll found in cyanobacterial blooms. The CI is a “spectral shape” (or curvature) algorithm, which is a form of the second derivative around the 681 nm (MERIS/OLCI) or 678 nm (MODIS) band, which is robust and implicitly includes an atmospheric correction, allowing reliable use for many more scenes than analytical algorithms. Monitoring of cyanobacterial blooms with the CI began with the European Space Agency’s (ESA) Medium Resolution Imaging Spectrometer (MERIS) sensor (2002–2012). With the loss of data from MERIS in the spring of 2012, the monitoring system shifted to using NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS). MODIS has bands that allow computation of a CI product, which was intercalibrated with MERIS at the time to establish a conversion of MODIS CI to MERIS CI. In 2016, ESA launched the Ocean and Land Color Imager (OLCI), the replacement for MERIS, on the Sentinel-3 spacecraft. MODIS can serve two purposes. It can provide a critical data set for the blooms of 2012–2015, and it offers a bridge from MERIS to OLCI. We propose a basin-wide integrated technique for intercalibrating the CI algorithm from MODIS to both MERIS and OLCI. This method allowed us to intercalibrate OLCI CI to MERIS CI, which would then allow the production of a 20-year and ongoing record of cyanobacterial bloom activity. This approach also allows updates as sensor calibrations change or new sensors are launched, and it could be readily applied to spectral shape algorithms.