Kendall L. Carder

Ocean color chlorophyll algorithms for SeaWiFS

A large data set containing coincident in situ chlorophyll and remote sensing reflectance measurements was used to evaluate the accuracy, precision, and suitability of a wide variety of ocean color chlorophyll algorithms for use by SeaWiFS (Sea-viewing Wide Field-of-view Sensor). The radiance-chlorophyll data were assembled from various sources during the SeaWiFS Bio-optical Algorithm Mini-Workshop (SeaBAM) and is composed of 919 stations encompassing chlorophyll concentrations between 0.019 and 32.79 μg L−1. Most of the observations are from Case I nonpolar waters, and ∼20 observations are from more turbid coastal waters. A variety of statistical and graphical criteria were used to evaluate the performances of 2 semianalytic and 15 empirical chlorophyll/pigment algorithms subjected to the SeaBAM data. The empirical algorithms generally performed better than the semianalytic. Cubic polynomial formulations were generally superior to other kinds of equations. Empirical algorithms with increasing complexity (number of coefficients and wavebands), were calibrated to the SeaBAM data, and evaluated to illustrate the relative merits of different formulations. The ocean chlorophyll 2 algorithm (OC2), a modified cubic polynomial (MCP) function which uses Rrs490/Rrs555, well simulates the sigmoidal pattern evident between log-transformed radiance ratios and chlorophyll, and has been chosen as the at-launch SeaWiFS operational chlorophyll a algorithm. Improved performance was obtained using the ocean chlorophyll 4 algorithm (OC4), a four-band (443, 490, 510, 555 nm), maximum band ratio formulation. This maximum band ratio (MBR) is a new approach in empirical ocean color algorithms and has the potential advantage of maintaining the highest possible satellite sensor signal: noise ratio over a 3-orders-of-magnitude range in chlorophyll concentration.

Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters.

For open ocean and coastal waters, a multiband quasi-analytical algorithm is developed to retrieve absorption and backscattering coefficients, as well as absorption coefficients of phytoplankton pigments and gelbstoff. This algorithm is based on remote-sensing reflectance models derived from the radiative transfer equation, and values of total absorption and backscattering coefficients are analytically calculated from values of remote-sensing reflectance. In the calculation of total absorption coefficient, no spectral models for pigment and gelbstoff absorption coefficients are used. Actually those absorption coefficients are spectrally decomposed from the derived total absorption coefficient in a separate calculation. The algorithm is easy to understand and simple to implement. It can be applied to data from past and current satellite sensors, as well as to data from hyperspectral sensors. There are only limited empirical relationships involved in the algorithm, and they are for less important properties, which implies that the concept and details of the algorithm could be applied to many data for oceanic observations. The algorithm is applied to simulated data and field data, both non-case1, to test its performance, and the results are quite promising. More independent tests with field-measured data are desired to validate and improve this algorithm.

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.

Semianalytic Moderate-Resolution Imaging Spectrometer Algorithms for Chlorophyll A and Absorption with Bio-Optical Domains Based on Nitrate-Depletion Temperatures

This paper describes algorithms for retrieval of chlorophyll a concentration and phytoplankton and gelbstoff absorption coefficients for the Moderate-Resolution Imaging Spectrometer (MODIS) or sensors with similar spectral channels. The algorithms are based on a semianalytical, bio-optical model of remote sensing reflectance, Rrs(λ). The Rrs(λ) model has two free variables, the absorption coefficient due to phytoplankton at 675 nm, aϕ(675), and the absorption coefficient due to gelbstoff at 400 nm, ag(400). The Rrs model has several parameters that are fixed or can be specified based on the region and season of the MODIS scene. These control the spectral shapes of the optical constituents of the model. Rrs(λi) values from the MODIS data processing system are placed into the model, the model is inverted, and aϕ(675), ag(400), and chlorophyll a are computed. The algorithm also derives the total absorption coefficients a(λi) and the phytoplankton absorption coefficients aϕ(λi) at the visible MODIS wavelengths. MODIS algorithms are parameterized for three different bio-optical domains: (1) high photoprotective pigment to chlorophyll ratio and low self-shading, which for brevity, we designate as “unpackaged”; (2) low photoprotective pigment to chlorophyll ratio and high self-shading, which we designate as “packaged”; and (3) a transitional or global-average type. These domains can be identified from space by comparing sea-surface temperature to nitrogen-depletion temperatures for each domain. Algorithm errors of more than 45% are reduced to errors of less than 30% with this approach, with the greatest effect occurring at the eastern and polar boundaries of the basins.

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.

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.

Reflectance model for quantifying chlorophyll a in the presence of productivity degradation products

Marine colored dissolved organic matter (CDOM) (also gilvin or yellow substance) absorbs light at an exponentially decreasing rate as a function of wavelength. From 410 nm to about 640 nm, particulate phytoplankton degradation products including pheopigments, detritus, and bacteria have absorption curves that are similar in shape to that of CDOM. In coastal areas and areas downstream from upwelling regions, these constituents of seawater often absorb much more light than do living phytoplankton, leading to errors in satellite-derived chlorophyll estimates as high as 133%. Proposed NASA sensors for the 1990s will have spectral channels as low as 412 nm, permitting the development of algorithms that can separate the absorption effects of CDOM and other phytoplankton degradation products from those due to biologically viable pigments. A reflectance model has been developed to estimate chlorophyll a concentrations in the presence of CDOM, pheopigments, detritus, and bacteria. Nomograms and lookup tables have been generated to describe the effects of different mixtures of chlorophyll a and these degradation products on the R(412) : R(443) and R(443) : R(565) remote-sensing reflectance or irradiance reflectance ratios. These are used to simulate the accuracy of potential ocean color satellite algorithms, assuming that atmospheric effects have been removed. For the California Current upwelling and offshore regions, with chlorophyll a ≤ 1.3 mg m−3, the average error for chlorophyll a retrievals derived from irradiance reflectance data for degradation product-rich areas was reduced from ±61% to ±23% by application of an algorithm using two reflectance ratios (R(412) : R(443) and R(443) : R(565)) rather than the commonly used algorithm applying a single reflectance ratio (R(443) : R(565)).

Atmospheric Correction of SeaWiFS Imagery over Turbid Coastal Waters: A Practical Method

Abstract The current SeaWiFS algorithms frequently yield negative water-leaving radiance values in turbid Case II waters primarily because the water-column reflectance interferes with the atmospheric correction based on the 765-nm and 865-nm spectral bands. Here we present a simple, practical method to separate the water-column reflectance from the total reflectance at 765 nm and 865 nm. Assuming the type of aerosol does not vary much over relatively small spatial scales (∼50–100 km), we first define the aerosol type over less turbid waters. We then transfer it to the turbid area by using a “nearest neighbor” method. While the aerosol type is fixed, the concentration can vary. This way, both the aerosol reflectance and the water-column reflectance at 765 nm and 865 nm may be derived. The default NASA atmospheric correction scheme subsequently is used to obtain the aerosol scattering components at shorter wavelengths. This simple method was tested under various atmospheric conditions over the Gulf of Mexico, and it proved effective in reducing the errors of both the water-leaving radiance and the chlorophyll concentration estimates. In addition, in areas where the default NASA algorithms created a mask due to atmospheric correction failure, water-leaving radiance and chlorophyll concentrations were recovered. This method, in comparison with field data and other turbid water algorithms, was tested for the Gulf of Maine and turbid, posthurricane Gulf of Mexico waters. In the Gulf of Maine it provided more accurate retrievals with fewer failures of the atmospheric correction algorithms. In the Gulf of Mexico it provided far fewer pixels with atmospheric failure than the other methods, did not overestimate chlorophyll as severely, and provided fewer negative water-leaving radiance values. Background Since the launch of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) aboard the SeaStar satellite in August 1997, global ocean color data are available to the science community on a regular basis. SeaWiFS is superior to the original Coastal Zone Color Scanner (CZCS, Hovis et al., 1980 ). It has much higher radiometric sensitivity (10 bits versus 8 bits) and additional spectral bands to aid in atmospheric correction and bio-optical applications. Specifications called for uncertainties less than ±5% in retrieved water-leaving radiance and less than ±35% in chlorophyll a concentration ([chl a]) in Case I waters ( Hooker et al., 1992 ; “Case I” defined in Morel and Prieur, 1977 ). SeaWiFS, however, generally fails to deliver such fidelity in turbid or shallow coastal waters (Case II waters). Turbid water constituents (suspended sediments, bubbles, etc., or bottom reflection) can contribute significant amounts of radiance to the atmospheric correction bands (765 nm and 865 nm). This will induce large errors when applying the standard atmospheric correction scheme since in that scheme the water-leaving radiance is assumed to be negligible in the near-infrared (IR) part of the spectrum (Gordon and Wang, 1994) . Also, in turbid Case II waters, the current band ratio bio-optical algorithm does not work well, simply because there are often other constituents (e.g., colored dissolved organic matter, or CDOM) whose optical properties may not covary with phytoplankton pigment concentrations Morel and Prieur 1977 , Carder et al. 1991 , Carder et al. 1999 , Muller-Karger et al. 1991 .

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.

Euphotic zone depth: Its derivation and implication to ocean‐color remote sensing

[1] Euphotic zone depth, z1%, reflects the depth where photosynthetic available radiation (PAR) is 1% of its surface value. The value of z1% is a measure of water clarity, which is an important parameter regarding ecosystems. Based on the Case-1 water assumption, z1% can be estimated empirically from the remotely derived concentration of chlorophyll-a ([Chl]), commonly retrieved by employing band ratios of remote sensing reflectance (Rrs). Recently, a model based on water’s inherent optical properties (IOPs) has been developed to describe the vertical attenuation of visible solar radiation. Since IOPs can be nearanalytically calculated from Rrs, so too can z1%. In this study, for measurements made over three different regions and at different seasons (z1% were in a range of 4.3–82.0 m with [Chl] ranging from 0.07 to 49.4 mg/m 3 ), z1% calculated from Rrs was compared with z1% from in situ measured PAR profiles. It is found that the z1% values calculated via Rrs-derived IOPs are, on average, within � 14% of the measured values, and similar results were obtained for depths of 10% and 50% of surface PAR. In comparison, however, the error was � 33% when z1% is calculated via Rrs-derived [Chl]. Further, the importance of deriving euphotic zone depth from satellite ocean-color remote sensing is discussed.