Jennifer Cannizzaro

Performance of the MODIS semi-analytical ocean color algorithm for chlorophyll-a

The Moderate Resolution Imaging Spectroradiometer (MODIS) semi-analytical (SA) algorithm calculates the spectral absorption properties of surface waters, splitting them into those associated with phytoplankton, aph(λ), colored dissolved organic matter or gelbstoff, ag(λ), and water, aw(λ). The phytoplankton absorption coefficient, aph(675), is then used to derive the concentration of chlorophyll-a, Chlor_a_3. The SA algorithm is designed to respond to variable ratios of aph(λ) to ag(λ) and to wide ranges in the chlorophyll-specific phytoplankton absorption coefficient, a*ph(λ), for a given chlorophyll-a level. In this paper, the SA algorithm is expanded to include environments consistent with strong upwelling zones and high latitudes. Spatial and temporal differences in MODIS Terra chlorophyll-a retrievals are examined between Chlor_a_3 and an empirical algorithm, Chlor_a_2, developed to mimic the performance of the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) OC-4 chlorophyll-a algorithm. The greatest differences observed are for upwelling regions and for southern high-latitude waters during austral spring where Chlor_a_2 values are on average about half of field and Chlor_a_3 values due to lower chlorophyll-specific phytoplankton absorption coefficients typical of this region. Preliminary match-up results indicate strong linearity and good agreement between in situ chlorophyll-a concentrations and MODIS-derived Chlor_a_3 compared to Chlor_a_2.

On the Accuracy of SeaWiFS Ocean Color Data Products on the West Florida Shelf

ABSTRACT Cannizzaro, J.P.; Hu, C.; Carder, K.L.; Kelble, C.R.; Melo, N.; Johns, E.M.; Vargo, G.A., and Heil, C.A., 2013. On the accuracy of SeaWiFS ocean color data products on the West Florida Shelf. Despite the importance of the West Florida Shelf (WFS) on regional ecology and local economy, systematic shelf-wide assessment of the ocean biology has not been conducted, primarily because of budgetary limitations for routine field campaigns and unknown accuracy of satellite-based data products. Here, using shipboard spectral normalized water-leaving radiance (nLw[λ]) data and chlorophyll-a concentrations (Chl-a) collected regularly during two multiyear field programs spanning >10 years, the accuracies of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) standard data products were evaluated. The in situ data covered a wide dynamic range, with about one order of magnitude in nLw(490) (0.47 to 4.01 mW cm−2 μm−1 sr−1) and two orders of magnitude in Chl-a (0.07 to 10.6 mg m−3). Near-concurrent in situ and satellite nLw(λ) data showed absolute percent differences (APD) increasing from 7–9% to 10–14% when data with elevated aerosol optical thicknesses at 865 nm (τa865) were included. Most of this uncertainty, however, canceled in the maximal blue-to-green reflectance band ratios traditionally used for estimating Chl-a. SeaWiFS OC4 Chl-a showed a root mean square (RMS) uncertainty of 0.106 for log-transformed data in waters offshore of the 20-m isobath that increased to 0.255 when all data were considered. The increased likelihood for nearshore SeaWiFS Chl-a greater than ∼0.5 mg m−3 to be overestimated was shown to be caused by a variety of factors (colored dissolved organic matter [CDOM], suspended sediments, and bottom reflectance) that varied in both time and space. In the future, more sophisticated algorithms capable of taking these factors into consideration are required to improve remote determinations of Chl-a in nearshore waters of the WFS.

VIIRS Observations of a Karenia brevis Bloom in the Northeastern Gulf of Mexico in the Absence of a Fluorescence Band

The Visible Infrared Imager Radiometer Suite (VIIRS) is not equipped with a fluorescence band, which may affect its ability to detect and quantify harmful algal blooms (HABs) in coastal waters rich in colored dissolved organic matter. Such a deficiency has previously been demonstrated for a bloom of the toxic dinoflagellate Karenia brevis in the northeastern Gulf of Mexico (NEGOM) in summer 2014. Here, using data collected in the field and by VIIRS and Moderate Resolution Imaging Spectroradiometer (MODIS), we show that such a deficiency may be partially overcome using a red-green-chlorophyll-a index (RGCI). A relationship between near-concurrent (±4 hours) VIIRS RGCI (Rrs(672)/Rrs(551)) and field-measured chlorophyll-a (Chla; in mg m-3) was developed and evaluated using calibrated Chla obtained by a flowthrough system. A mean relative uncertainty, which was approximately twofold lower than VIIRS OC3M Chla, was obtained for VIIRS RGCI Chla (mean relative error: ~56%) over a large range (0.5-20 mg m -3). Similar spatial patterns between near-concurrent MODIS-Aqua (MODISA) normalized fluorescence line height (nFLH) and VIIRS RGCI Chla imagery indicate that VIIRS RGCI may be used as a surrogate for MODISA nFLH in the absence of a fluorescence band. The success of this newly developed data product may be partially attributed to the 20-nm bandwidth of the VIIRS 672-nm band (662-682 nm) that covers a portion of the solar stimulated fluorescence region. However, whether such observations from a simple case study can be extended to other turbid coastal or inland waters still remains to be tested.

Ocean color algorithms in optically shallow waters: limitations and improvements

Current ocean color algorithms based on remote-sensing reflectance spectra, Rrs(λ), overestimate chlorophyll a concentrations, Chl, and particulate backscattering coefficients, bbp(λ), in optically shallow oceanic waters due to increased bottom reflectance. Since such regions often contain important ecological resources and are heavily influenced by human populations, accurate estimates of Chl and bbp(λ) are essential for monitoring algal blooms (e.g. red tides), detecting sediment resuspension events and quantifying primary productivity. In this study, a large synthetic data set of 500 Rrs(λ) spectra is developed to examine limitations of ocean color algorithms for optically shallow waters and to develop alternative algorithms that can be applied to satellite (e.g. SeaWiFS and MODIS) and aircraft ocean color sensor data. Rrs(λ) spectra are simulated using a semi-analytic model for optically shallow waters. The model is parameterized with sand bottom albedo spectra, ρ(λ), using a wide range of chlorophyll a concentrations (0.03-30 mg m-3), bottom depths (2-50m) and bottom albedos (ρ(550)=0.01-0.30) to provide a robust data set that accurately represents and complements shipboard Rrs(λ) data from the Gulf of Mexico and Bahamian waters. The accuracy of a remotely-based technique developed recently from shipboard Rrs(λ) data is tested on the synthetic data for identifying waters with bottom reflectance contributions at Rrs(555) greater than 25%. Limitations and improvements regarding this method are discussed.

Binational collaboration to study Gulf of Mexico's harmful algae

Blooms of the toxic marine dinoflagellate Karenia brevis cause massive fish kills and other public health and economic problems in coastal waters throughout the Gulf of Mexico [Steidinger, 2009]. These harmful algal blooms (HABs) are a gulf-wide problem that require a synoptic observing system for better serving decision-making needs. The major nutrient sources that initiate and maintain these HABs and the possible connectivity of blooms in different locations are important questions being addressed through new collaborations between Mexican and U.S. researchers and government institutions. These efforts were originally organized under the U.S./Mexico binational partnership for the HABs Observing System (HABSOS), led by the U.S. Environmental Protection Agencys Gulf of Mexico Program (EPAGMP) and several agencies in Veracruz, Mexico, since 2006. In 2010 these efforts were expanded to include other Mexican states and institutions with the integrated assessment and management of the Gulf of Mexico Large Marine Ecosystem (GoMLME) program sponsored by the Global Environment Facility (GEF), the United Nations Industrial Development Organization (UNIDO), the Secretaria de Medio Ambiente y Recursos Naturales (SEMARNAT), and the National Oceanic and Atmospheric Administration (NOAA).