A robust empirical algorithm to determine the diffuse attenuation coefficient K d at a wavelength of 490 nm (K d 490) from spectral remote sensing reflectance has been derived from the NOMAD data set and tested against the COASTLOOC data set. Together, both data sets contain more than 3,800 observations of concomitantly acquired bio-optical parameters in waters ranging from very clear to very turbid, of which about 2,300 have been used in this study. The proposed algorithm follows a power law of the form . Our algorithm shows comparable performance in both clear and turbid waters. Due to its simplicity and the public availability of the underlying in situ data, localization of the algorithm by appropriate sub-setting of the NOMAD data and/or adding other in situ data are straightforward.

Many recent models for retrieval of primary production in the sea from ocean-color data are temperature based. But previous studies in low latitudes have shown that models that include phytoplankton community structure can have improved predictive capability. In this study, we measured photosynthetic parameters from photosynthesis-irradiance (P-E) experiments, phytoplankton absorption coefficients, and phytoplankton community structure derived from algal pigments during four cruises in the northern South China Sea (NSCS). The maximum quantum yield of CO 2 (U C m ) and the chlorophyll a-normalized P-E curve light-limited slope (a B ) varied significantly with the blue-to-red ratio of phytoplankton absorption peaks (a ph (435)/a ph (676)) (p < 0.001, r 5 20.459 and 20.332, respectively). The unexplained variability could be due in part to the absorption associated with nonphotosynthetic pigments. The chlorophyll a-normalized light-saturated photosynthetic rate (P B m ) at the surface showed a unimodal distribution over the chlorophyll a range during the spring and summer, and significantly increased when Prochlorococcus was outcompeted by other picophytoplankton (p < 0.01). Almost 60% of the variance of P B m could be explained by a piecewise regression with phytoplankton absorption coefficients and pigment markers. Unlike previous studies, our data showed that changes of P B m were unrelated to the size structure of phytoplankton. Although a temperature-based approach could not effectively predict a B and P B m in the NSCS, a trophicbased approach can be used for assignment of these parameters in a regional primary production model using ocean-color data.

[1] In the present paper, we report on a method to retrieve the pigment concentration in Case I waters from ocean color. The method is derived from radiative transfer (RT) simulations and subsequent application of artificial neural network (ANN) techniques. Information on absorption and total scattering of pure seawater, colored dissolved organic matter, and marine particles are mostly taken from published measurements or parameterizations. Additionally, a new model relating the backscattering of marine particles to pigment concentration and wavelength is introduced. The such defined inherent optical properties are input to a RT code in order to generate a synthetic data set of remote sensing reflectance spectra. This synthetic data set is then used for the training of a set of ANNs with the aim to approximate the functional relationship between ocean color and pigment concentration. The different ANNs are obtained by systematic variations of input parameters, architecture, and noise level added to the training data. The performance of each individual ANN-based pigment retrieval scheme is assessed by applying it to the remote sensing reflectance spectra contained in the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Bio-optical Algorithm MiniWorkshop (SeaBAM) data set and comparing the retrieved pigment concentrations to those actually measured. The most successful ANN compares favorably with commonly used empirical pigment retrieval schemes. Compared, e.g., to the SeaWiFS algorithms OC2B and OC4, the square of the correlation coefficient r 2 is increased from 0.924 (OC2B), respectively, 0.928 (OC4) to 0.934 (ANN). The root mean square error of the retrieved log-transformed pigment concentration drops from 0.156 for OC2B, respectively, 0.151 for OC4 to 0.148 for the ANN-based pigment retrieval scheme. Furthermore, the latter shows a higher resistance against noisy input data.

This research used the profile data measured extensively in the Yellow Sea and Bohai Sea (YSBS) to explain the temporal and spatial distribution characteristics of optical properties and systematically analyzed the influencing mechanisms of the seasonal variations of optical properties in the YSBS in conjunction with synchronously measured hydrological and biogeochemical data in vertical profiles. The main conclusions obtained are as follows: the vertical variations in the optical properties in the YSBS are mainly influenced by the stratification effect, vertical mixing function, and phytoplankton growth process; and the variations of optical properties are dominated by the change of particle characteristics. The backscattering ratio can be used to discriminate particle types as a proxy of particulate bulk refractive index. In the YSBS, for waters with a backscattering ratio of less than 0.012, the variations of optical properties are dominated by the phytoplankton particles. For waters with a backscattering ratio greater than 0.012, the variations of optical properties are dominated by inorganic sediment particles. In addition, for the YSBS, the variations in optical properties of upper surface layer waters can be elucidated well by the vertical variations.