Chesapeake Bay Data — College of Marine Science

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CHLOR_A: chlorophyll-a concentration (in mg/m^3) in the surface ocean layer estimated with the most updated calibration and algorithms in the SeaDAS processing software (default product). Over most open ocean waters it is fairly accurate (40-50% RMS uncertainty without significant bias see McClain et al. 2004 for more details). Over clear, shallow waters (< 30 m) or over very turbid coastal waters or river plumes, it is often overestimated as other components (colored dissolved organic matter, suspended sediments, ocean bottom) interfere with the algorithm (e.g., Hu, 2008).

CI: MODIS Color Index derived from reflectance data after correction for gaseous absorption, molecular scattering, and sun glint effects. It is basically the reflectance at 555 nm, referenced against linear baseline between 469 and 645 nm (Hu, 2011). CI has the advantage over the “standard” NASA chlor_a or other products because it is nearly immune to sun glint contamination, and therefore increases the data coverage for subtropical and tropical oceans. This is particular useful for cruise planning and for feature tracking. The MODIS standard product MOD35 (Ackerman et al., 2010) is used to discriminate clouds from water, and a cloudmask (grey color) is overlaid on the image.

EFAI: Enhanced Floating Algae Index (in reflectance units) to detect ocean surface features such as Sargassum, green macroalgae, and cyanobacteria. EFAI is nearly identical to FAI, except that one of the spectral bands to construct the background is 667 nm instead of 645 nm. Thus, it is more sensitive than FAI to detect subtle ocean surface features, but it saturates under sun glint, clouds, or thick aerosols (i.e., less coverage than FAI). See FAI description for more algorithm details.

ERGB: Enhanced Red-Green-Blue composite image from normalized water-leaving radiance at 547 nm (R), 488 nm (G), and 443 nm (B). The latter three are standard data products of the SeaDAS processing. The image is used to identify various ocean water types, from shallow (bright), sediment-rich (brownish), phytoplankton-rich (darkish), CDOM-rich (colored dissolved organic matter) (darkish), to blue waters (bluish) (see Hu et al., 2004 & 2005 for using this type of image to identify dark waters).

FAI: Floating Algae Index (in reflectance units) to detect ocean surface features such as Sargassum, green macroalgae, and cyanobacteria. FAI is derived as the reflectance at 859 nm (after correction for gaseous absorption and molecular scattering), referenced against a linear baseline between 645 nm and 1240 nm (Hu, 2009). FAI has been used to study Qingdao’s green tides (Ulva prolifera blooms) (Hu et al., 2010a), cyanobacteria blooms in Taihu Lake (Hu et al., 2010b), and to detect Trichodesmium blooms in coastal waters (Hu et al., 2010c). Be very cautious to interpret the tiny surface features – they are very often small clouds.

FLH: Fluorescence Line Height (in reflectance units), derived using the spectral reflectance (after correction of the gaseous absorption and molecular scattering effects) and the Letelier and Abott (1996) baseline subtraction algorithm. It measures the solar stimulated chlorophyll-a fluorescence. In sediment-poor waters (< 5 ug/L) it can be used as a better proxy than the “standard” chlor_a product to represent surface chlorophyll-a concentrations (Hu et al., 2005), as FLH is much less sensitive to CDOM interference (McKee et al., 2007; Gilerson et al., 2007).

NFLH: Normalized Fluorescence Line Height (in mW cm^-2 um^-1 sr^-1), a standard data product from the SeaDAS processing software using the Letelier and Abott (1996) baseline subtraction algorithm. It is a measure of the solar stimulated chlorophyll-a fluorescence. In sediment-poor waters (< 5 ug/L) it can be used as a better proxy than the “standard” chlor_a product to represent surface chlorophyll-a concentrations (Hu et al., 2005), as FLH is much less sensitive to CDOM interference (McKee et al., 2007; Gilerson et al., 2007). "Normalize" means that the data are normalized against input solar light and partially corrected for the effect of sediment 'contamination'.

RGB: Red-Green-Blue composite image showing clouds, ocean, and land. The calibrated radiance is first converted to reflectance, and then corrected for gaseous absorption and molecular (Rayleigh) scattering effects (software credit: Liam Gumley, Jacques Descloitres, and Jeffrey Schmaltz of the University of Wisconsin). The resulting reflectance in the three MODIS bands (645 nm: R; 555 nm: G; 469 nm: B) is stretched to 0-255 to obtain the RGB image.

SST4: Sea Surface Temperature (in Degree C) estimated with the SeaDAS processing software (default product) using a multi-channel non-linear regression algorithm (Brown and Minnett, 1999). The difference from the "sst" image is that sst4 is derived from the 4-um channels instead of the 10-um channel, and therefore has higher sensitivity (image is sharper than sst). For most ocean waters RMS uncertainty is often within 0.5 degree, for coastal waters in the Gulf of Mexico RMS uncertainty is less than 1 degree (Hu et al., 2009). Most errors come from cloud contaminations. The MODIS standard product MOD35 (Ackerman et al., 2010) is used to discriminate clouds from water, and a cloudmask (grey color) is overlaid on the image.

SST: Sea Surface Temperature (in Degree C) estimated using the SeaDAS processing software (default product) with a multi-channel non-linear regression algorithm (Brown and Minnett, 1999). For most ocean waters RMS uncertainty is often within 0.5 degree, for coastal waters in the Gulf of Mexico RMS uncertainty is less than 1 degree (Hu et al., 2009). Most errors come from cloud contaminations. The MODIS standard product MOD35 (Ackerman et al., 2010) is used to discriminate clouds from water, and a cloudmask (grey color) is overlaid on the image.