The cyanobacteria index (CI) has been applied to Medium Resolution Imaging Spectrometer (MERIS) imagery to characterize cyanobacterial biomass in diverse regions. While a consistent qualitative estimate of cyanobacterial biomass is useful, establishing universal relationships to chlorophyll a (chl) concentration, the dominant photosynthetic pigment in phytoplankton, would quantify blooms for regional comparisons. Relationships between chl concentrations were determined from water reflectance measurements and chl concentration from water samples taken from cyanobacterial blooms in eutrophic lakes in Florida, where chl ranged from 16 to 115 µg L⁻¹. When the chl relationship was applied to simultaneous satellite and field data, the chl concentration determined from satellite CI showed negligible bias and root mean square error of 27%. The generic CI-based chl algorithm presented here seems suitable for consistent quantification of chl concentration within cyanobacterial blooms.

We describe the application of a Neural Network (NN) previously developed by us, to the detection and tracking, of Karenia brevis Harmful Algal Blooms (KB HABs) that plague the coasts of the West Florida Shelf (WFS) using Visible Infrared Imaging Radiometer Suite (VIIRS) satellite observations. Previous approaches for the detection of KB HABs in the WFS primarily used observations from the Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-A) satellite. They depended on the remote sensing reflectance signal at the 678 nm chlorophyll fluorescence band (Rrs678) needed for both the normalized fluorescence height (nFLH) and Red Band Difference algorithms (RBD) currently used. VIIRS which has replaced MODIS-A, unfortunately does not have a 678 nm fluorescence channel so we customized the NN approach to retrieve phytoplankton absorption at 443 nm (a_ph443) using only Rrs measurements from existing VIIRS channels at 486, 551 and 671 nm. The a_ph443 values in these retrieved VIIRS images, can in turn be correlated to chlorophyll-a concentrations [Chla] and KB cell counts. To retrieve KB values, the VIIRS NN retrieved a_ph443 images are filtered by applying limiting constraints, defined by (i) low backscatter at Rrs 551 nm and (ii) a minimum a_ph443 value known to be associated with KB HABs in the WFS. The resulting filtered residual images, are then used to delineate and quantify the existing KB HABs. Comparisons with KB HABs satellite retrievals obtained using other techniques, including nFLH, as well as with in situ measurements reported over a four year period, confirm the viability of the NN technique, when combined with the filtering constraints devised, for effective detection of KB HABs.

This manuscript describes methods for evaluating the efficacy of five satellite-based Chlorophyll-a algorithms in Chesapeake Bay, spanning three separate sensors: Ocean Land Color Imager (OLCI), Visible Infrared Imaging Radiometer Suite (VIIRS), and MODerate Resolution Imaging Spectroradiometer (MODIS). The algorithms were compared using in situ Chlorophyll-a measurements from 38 separate stations, provided through the Chesapeake Bay Program (CBP). These stations span nearly the entire 300 km length of the optically complex Chesapeake Bay, the largest estuary in the United States. Overall accuracy was examined for the entire dataset, in addition to assessing the differences related to the distance from the turbidity maximum to the north by grouping the results into the upper bay, middle bay, or lower bay. The mean bias and the Mean Absolute Error (MAE) as well as the median bias and Median Absolute Error (MedAE) were conducted for comparison. A two-band algorithm, that is based on the red-edge portion of the electromagnetic spectrum (RE10), when applied to OLCI imagery, exhibited the lowest overall MedAE of 36% at all stations. As a result, it is recommended that the RE10 algorithm be applied to OLCI and provided as an operational product through NOAA’s CoastWatch program. The paper will conclude with results from a brief climatological analysis using the OLCI RE10 algorithm.

A high resolution depth attenuation product (K_d^hires) was developed using MODIS 500 m and 250 m spectral bands. The K_d^hires was compared with Wang’s operational K_d for the Chesapeake Bay. Minimal differences were observed between the two methods, with greatest deviation occurring in areas of high turbidity in the tributaries. After tuning the new K_d^hires, the mean absolute error and bias between the two algorithms was 0.22 m⁻¹ and 0.026 m⁻¹, indicating good agreement. Higher spatial resolution provides for improved retrievals along the coast and into the narrow sections of the tributaries, coinciding with areas of concern to estuarine health and coastal management applications.

Introduction: Harmful algal blooms of Alexandrium monilatum and Margalefidinium polykrikoides are a common occurrence in the tributaries of lower Chesapeake Bay, United States of America. This study evaluated whether daily processed imagery from the Ocean and Land Colour Instrument (OLCI) can accurately measure chlorophyll a (chl a ) concentrations in these turbid, bloom-prone tributaries. Since HAB cell concentrations provide a more direct measure of risk than chl a , a second objective was to quantify the accuracy of bloom cell concentrations derived from satellite chl a . Methods: Field collected chl a concentrations and processed OLCI data were used to evaluate the accuracy of satellite-derived chl a concentrations in the York and James Rivers. Field, laboratory, and literature data were used to quantify the precision of satellite-based cell estimates. Results: Satellite-derived chl a estimates correlated well with laboratory extracted chl a from the field and were stable over time for the entire OLCI satellite image dataset. The results of laboratory experiments and field studies showed that variations in the pg chl a cell ^-1 used to convert chl a to cell estimates, often varied five- to hundred-fold, depending on prevailing environmental growth conditions and the chl a contributed by co-occurring phytoplankton. Discussion: The satellite-derived chl a concentrations proved accurate over a wide range of chl a concentrations, confirming their usefulness in tracking high biomass blooms and guiding sampling efforts required to evaluate potential adverse impacts, and as a basis for potentially estimating relative HAB cell concentrations. However, field, literature, and laboratory data showed that chl a cell ^-1 estimates at a given chl a concentration can vary by up to 100-fold. Consequently, satellite-based cell concentrations will be highly inaccurate. More accurate HAB cell estimates might be possible if chl a cell ^-1 is measured periodically during a bloom and used to estimate cell concentration from satellite chl a in real-time.