Abstract. 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/•g 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. IntroductionThe influence of phytoplankton on the color of seawater has been studied for several decades. It is well understood that chlorophyll a, the primary photosynthetic pigment in phytoplankton, absorbs relatively more blue and red light than green, and the spectrum of backscattered sunlight or color of ocean water progressively shifts from deep blue to green as the concentration of phytoplankton increases [e.g.

Abstract. A nonlinear statistical method for the inversion of ocean color spectra is used to determine three inherent optical properties (IOPs), the absorption coefficients for phytoplankton and dissolved and detrital materials, and the backscattering coefficient due to particulates. The inherent optical property inversion model assumes that (1) the relationship between remotesensing reflectance and backscattering and absorption is well known, (2) the optical coefficients for pure water are known, and (3) the spectral shapes of the specific absorption coefficients for phytoplankton and dissolved and detrital materials and the specific backscattering coefficient for particulates are known. This leaves the magnitudes for the three unknown coefficients to be determined. A sensitivity analysis is conducted to determine the best IOP model configuration for the Sargasso Sea using existing bio-optical models. The optical and biogeochemical measurements used were collected as part of the Bermuda Bio-Optics Project and the U.

No abstract

An extensive database of -400 in situ particulate absorption spectra [a,(X)] is analyzed to assess the potential of using ocean color imagers to examine variability in the structure of the near-surface ocean planktonic ecosystem. This application of a,(X) data is appropriate, as particulate absorption variations are the dominant source of ocean color variation and are attributable to changes in the phytoplankton community structure. Empirical orthogonal function (EOF') analyses are used to estimate the contribution of each statistical mode to the total variance. The EOF analyses showed that > 99% of the variance found in the a,(X) data set can be simply attributed to the total amount of particulate material. When this source of variability is removed, two significant modes of variability can be identified which comprise 79 and 18% of the normalized variance. These modes are interpreted as representing the relative contribution of chlorophyll-containing biomass and detrital materials, verifying the use of two-component phytoplankton-detritus models to partition a,(X). Only a small amount of the total a,(X) variability (~0.5% of the total) can be attributed to absorption features caused by accessory pigment groups. Thus, variability in a,(X) is almost entirely associated with the quantity of the absorbing materials rather than their spectral quality (or normalized spectral shape). These results suggest that remotely sensed ocean color spectra will reflect only three statistically significant components: the total amount of particulate material, the relative amounts of chlorophyll-containing biomass, and detrital materials. For most typical conditions it is unlikely that robust global algorithms for determining particular phytoplankton groups can be developed from remotely sensed ocean color spectra.