Red tides in the Gulf of Mexico: Where, when, and why?

[1] Independent data from the Gulf of Mexico are used to develop and test the hypothesis that the same sequence of physical and ecological events each year allows the toxic dinoflagellate Karenia brevis to become dominant. A phosphorus-rich nutrient supply initiates phytoplankton succession, once deposition events of Saharan iron-rich dust allow Trichodesmium blooms to utilize ubiquitous dissolved nitrogen gas within otherwise nitrogen-poor sea water. They and the co-occurring K. brevis are positioned within the bottom Ekman layers, as a consequence of their similar diel vertical migration patterns on the middle shelf. Upon onshore upwelling of these near-bottom seed populations to CDOM-rich surface waters of coastal regions, light-inhibition of the small red tide of ~1 ug chl l(-1) of ichthytoxic K. brevis is alleviated. Thence, dead fish serve as a supplementary nutrient source, yielding large, self-shaded red tides of ~10 ug chl l(-1). The source of phosphorus is mainly of fossil origin off west Florida, where past nutrient additions from the eutrophied Lake Okeechobee had minimal impact. In contrast, the P-sources are of mainly anthropogenic origin off Texas, since both the nutrient loadings of Mississippi River and the spatial extent of the downstream red tides have increased over the last 100 years. During the past century and particularly within the last decade, previously cryptic Karenia spp. have caused toxic red tides in similar coastal habitats of other western boundary currents off Japan, China, New Zealand, Australia, and South Africa, downstream of the Gobi, Simpson, Great Western, and Kalahari Deserts, in a global response to both desertification and eutrophication.

Retrieval of chlorophyll from remote-sensing reflectance in the china seas.

The East China Sea is a typical case 2 water environment, where concentrations of phytoplankton pigments, suspended matter, and chromophoric dissolved organic matter (CDOM) are all higher than those in the open oceans, because of the discharge from the Yangtze River and the Yellow River. By using a hyperspectral semianalytical model, we simulated a set of remote-sensing reflectance for a variety of chlorophyll, suspended matter, and CDOM concentrations. From this simulated data set, a new algorithm for the retrieval of chlorophyll concentration from remote-sensing reflectance is proposed. For this method, we took into account the 682-nm spectral channel in addition to the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) channels. When this algorithm was applied to a field data set, the chlorophyll concentrations retrieved through the new algorithm were consistent with field measurements to within a small error of 18%, in contrast with that of 147% between the SeaWiFS ocean chlorophyll 2 algorithm and the in situ observation.

Atmospheric Correction of SeaWiFS Imagery: Assessment of the Use of Alternative Bands.

Spatial inhomogeneity, or speckling, frequently occurs in Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data products such as water-leaving radiance and chlorophyll concentration. We have found that this effect may be caused by high-altitude aerosols or thin cirrus clouds or even by digitization errors. For the scenes evaluated, whitecaps were ruled out as a likely cause of these errors. We tried to avoid using the 765-nm band, which is affected by O(2) absorption and is more sensitive to digitization errors, by instead using the 670-nm band in the atmospheric correction and found that speckling for either cloud-free areas or cloud-adjacent areas was significantly reduced.

Band-ratio or spectral-curvature algorithms for satellite remote sensing?

For the retrieval of chlorophyll concentrations or the total absorption coefficients of oceanic waters based on water color, there are algorithms that use either band ratios or spectral curvatures of remote-sensing reflectance or water leaving radiance. We show that band-ratio algorithms have the potential to be applied to a wider dynamic range of oceanic waters, whereas spectral-curvature algorithms show stable performance as long as the data set falls within the appropriate range.

Hyperspectral remote sensing for shallow waters. 2. Deriving bottom depths and water properties by optimization.

In earlier studies of passive remote sensing of shallow-water bathymetry, bottom depths were usually derived by empirical regression. This approach provides rapid data processing, but it requires knowledge of a few true depths for the regression parameters to be determined, and it cannot reveal in-water constituents. In this study a newly developed hyperspectral, remote-sensing reflectance model for shallow water is applied to data from computer simulations and field measurements. In the process, a remote-sensing reflectance spectrum is modeled by a set of values of absorption, backscattering, bottom albedo, and bottom depth; then it is compared with the spectrum from measurements. The difference between the two spectral curves is minimized by adjusting the model values in a predictor-corrector scheme. No information in addition to the measured reflectance is required. When the difference reaches a minimum, or the set of variables is optimized, absorption coefficients and bottom depths along with other properties are derived simultaneously. For computer-simulated data at a wind speed of 5 m/s the retrieval error was 5.3% for depths ranging from 2.0 to 20.0 m and 7.0% for total absorption coefficients at 440 nm ranging from 0.04 to 0.24 m(-1). At a wind speed of 10 m/s the errors were 5.1% for depth and 6.3% for total absorption at 440 nm. For field data with depths ranging from 0.8 to 25.0 m the difference was 10.9% (R2 = 0.96, N = 37) between inversion-derived and field-measured depth values and just 8.1% (N = 33) for depths greater than 2.0 m. These results suggest that the model and the method used in this study, which do not require in situ calibration measurements, perform very well in retrieving in-water optical properties and bottom depths from above-surface hyperspectral measurements.

Satellite-sensor calibration verification with the cloud-shadow method.

An atmospheric-correction method appropriate for high-spatial-resolution sensors that uses cloud-shaded pixels together with pixels in a neighboring region of similar optical properties is described. This cloud-shadow method uses the difference between the total radiance values observed at the sensor for these two regions, thus removing the nearly identical atmospheric radiance contributions to the two signals (e.g., path radiance and Fresnel-reflected skylight). What remains is largely due to solar photons backscattered from beneath the sea to dominate the residual signal. Normalization by the direct solar irradiance reaching the sea surface and correction for some second-order effects provides the remote-sensing reflectance of the ocean at the location of the neighbor region, providing a known ground target spectrum for use in testing the calibration of the sensor. A similar approach may be useful for land targets if horizontal homogeneity of scene reflectance exists about the shadow. Monte Carlo calculations have been used to correct for adjacency effects and to estimate the differences in the skylight reaching the shadowed and neighbor pixels.

Hyperspectral remote sensing for shallow waters. I. A semianalytical model.

For analytical or semianalytical retrieval of shallow-water bathymetry and/or optical properties of the water column from remote sensing, the contribution to the remotely sensed signal from the water column has to be separated from that of the bottom. The mathematical separation involves three diffuse attenuation coefficients: one for the downwelling irradiance (K(d)), one for the upwelling radiance of the water column (K(u)(C)), and one for the upwelling radiance from bottom reflection (K(u)(B)). Because of the differences in photon origination and path lengths, these three coefficients in general are not equal, although their equality has been assumed in many previous studies. By use of the Hydrolight radiative-transfer numerical model with a particle phase function typical of coastal waters, the remote-sensing reflectance above (R(rs)) and below (r(rs)) the surface is calculated for various combinations of optical properties, bottom albedos, bottom depths, and solar zenith angles. A semianalytical (SA) model for r(rs) of shallow waters is then developed, in which the diffuse attenuation coefficients are explicitly expressed as functions of in-water absorption (a) and backscattering (b(b)). For remote-sensing inversion, parameters connecting R(rs) and r(rs) are also derived. It is found that r(rs) values determined by the SA model agree well with the exact values computed by Hydrolight (~3% error), even for Hydrolight r(rs) values calculated with different particle phase functions. The Hydrolight calculations included b(b)/a values as high as 1.5 to simulate high-turbidity situations that are occasionally found in coastal regions.

Method to derive ocean absorption coefficients from remote-sensing reflectance.

A method to derive in-water absorption coefficients from total remote-sensing reflectance (ratio of the upwelling radiance to the downwelling irradiance above the surface) analytically is presented. For measurements made in the Gulf of Mexico and Monterey Bay, with concentrations of chlorophyll-a ranging from 0.07 to 50 mg/m(3), comparisons are made for the total absorption coefficients derived with the suggested method and those derived with diffuse attenuation coefficients. For these coastal to open-ocean waters, including regions of upwelling and the Loop Current, the results are as follows: at 440 nm the difference between the two methods is 13.0% (r(2) = 0.96) for total absorption coefficients ranging from 0.02 to 2.0 m(-1); at 488 nm the difference is 14.5% (r(2) = 0.97); and at 550 nm the difference is 13.6% (r(2) = 0.96). The results indicate that the method presented works very well for retrieval of in-water absorption coefficients exclusively from remotely measured signals, and that this method has a wide range of potential applications in oceanic remote sensing.

Estimating primary production at depth from remote sensing.

By use of a common primary-production model and identical photosynthetic parameters, four different methods were used to calculate quanta (Q) and primary production (P) at depth for a study of high-latitude North Atlantic waters. The differences among the four methods relate to the use of pigment information in the upper water column. Methods 1 and 2 use pigment biomass (B) as an input and a subtropical, empirical relation between K(d) (diffuse attenuation coefficient) and B to estimate Q at depth. Method 1 uses measured B, but Method 2 uses B derived from the Coastal Zone Color Scanner (subtropical algorithm) as inputs. Methods 3 and 4 use the phytoplankton absorption coefficient (a(ph)) instead of B as input, and Method B uses empirically derived a(ph)(440) and K(d) values, and Method 4 uses analytically derived a(ph)(440) and a (total absorption coefficient) values based on the same remote measurements as Method 2. When the calculated and the measured values of Q(z) and P(z) were compared, Method 4 provided the closest results [for P(z), r(2) = 0.95 (n = 24), and for Q(z), r(2) = 0.92 (n = 11)]. Method 1 yielded the worst results [for P(z), r(2) = 0.56 and for Q(z), r(2) = 0.81]. These results indicate that one of the greatest uncertainties in the remote estimation of P can come from a potential mismatch of the pigment-specific absorption coefficient (a(ph)*), which is needed implicitly in current models or algorithms based on B. We point out that this potential mismatch can be avoided if we arrange the models or algorithms so that they are based on the pigment absorption coefficient (a(ph)). Thus, except for the accuracy of the photosynthetic parameters and the above-surface light intensity, the accuracy of the remote estimation of P depends on how accurately a(ph) can be estimated, but not how accurately B can be estimated. Also, methods to derive a(ph) empirically and analytically from remotely sensed data are introduced. Curiously, combined application of subtropical algorithms for both B and K(d) to subarctic waters apparently compensates to some extent for effects that are due to their similar and implicit pigment-specific absorption coefficients for the calculation of Q(z).

Monte Carlo simulation of the atmospheric point-spread function with an application to correction for the adjacency effect.

Monte Carlo techniques are used to simulate atmospheric point-spread functions (PSF's) that are appropriate for the viewing geometries typical of the Airborne Visible-Infrared Imaging Spectrometer (AVIRIS). A model sensor is located at an altitude of 20 km and views a Lambertian surface through a horizontally homogeneous and vertically stratified atmosphere. Simulations show the effects on the PSF of variation of the aerosol phase function, the aerosol optical thickness, the sensor viewing angle, and the wavelength. An algorithm that uses the PSF to correct high-contrast images for adjacency effects is developed and applied to an AVIRIS image of Big Pine Key in the Florida Keys. A method to approximate the atmospheric PSF's without the need to resort to a Monte Carlo simulation is described. Correction of the AVIRIS image through the use of the approximated PSF is consistent with a previous correction. Error analysis is difficult and scene dependent; however, the correction algorithm is shown to be capable of indicating regions of high-contrast images in which conventional estimates of surface-leaving radiance are likely to be unreliable due to adjacency effects.

Model for the interpretation of hyperspectral remote-sensing reflectance.

Remote-sensing reflectance is easier to interpret for the open ocean than for coastal regions because the optical signals are highly coupled to the phytoplankton (e.g., chlorophyll) concentrations. For estuarine or coastal waters, variable terrigenous colored dissolved organic matter (CDOM), suspended sediments, and bottom reflectance, all factors that do not covary with the pigment concentration, confound data interpretation. In this research, remote-sensing reflectance models are suggested for coastal waters, to which contributions that are due to bottom reflectance, CDOM fluorescence, and water Raman scattering are included. Through the use of two parameters to model the combination of the backscattering coefficient and the Q factor, excellent agreement was achieved between the measured and modeled remote-sensing reflectance for waters from the West Florida Shelf to the Mississippi River plume. These waters cover a range of chlorophyll of 0.2-40 mg/m(3) and gelbstoff absorption at 440 nm from 0.02-0.4 m(-1). Data with a spectral resolution of 10 nm or better, which is consistent with that provided by the airborne visible and infrared imaging spectrometer (AVIRIS) and spacecraft spectrometers, were used in the model evaluation.