We propose a bio-optical inversion model that retrieves the absorption contributions of phytoplankton and colored detrital matter(CDM),as well as the phytoplankton size classes(PSCs),from total minus water absorption spectra.The model is based on three-component separation of phytoplankton size structure and a genetic algorithm.The model performance was tested on two independent datasets(the NASA bio-Optical Marine Algorithm Dataset(NOMAD) and the northern South China Sea(NSCS) dataset).The relationships between the estimated and measured values were strongly linear,especially for aCDM(412),and the Root Mean Square Error(RMSE) of the CDM exponential slope(SCDM) was relatively low.Next,the inversion model was directly applied to in-situ total minus water absorption spectra determined by an underwater meter during a cruise in September 2008,to retrieve the phytoplankton size structure in the seawater.By comparing the measured and retrieved chlorophyll a concentrations,we demonstrated that total and size-specific chlorophyll a concentrations could be retrieved by the model with relatively high accuracy.Finally,we applied the bio-optical inversion model to investigate changes in phytoplankton size structure induced by an anti-cyclonic eddy in the NSCS.
A previously developed model was modified to derive three phytoplankton size classes (micro-, nano-, and pico-phytoplankton) from the overall chlorophyll-a concentration, assuming that each class has a specific absorption coefficient. The modified model performed well using in-situ data from the northern South China Sea, and the results were reliable and accurate. The relative errors of the size-fractioned chlorophyll-a concentration for each size class were: micro-:21%, nano-:41%, pico-:26%, and nano+pico:23%. The model was then applied on ocean color remote sensing data to examine the distribution and variation of phytoplankton size classes in northern South China Sea on a large scale.
We examined regional empirical equations for estimating the surface concentration of particulate organic carbon （POC） in the South China Sea. These algorithms are based on the direct relationships between POC and the blue-to-green band ratios of spectral remotely sensed reflectance, Rrs（λB）/Rrs（555）. The best error statistics among the considered formulas were produced using the power function POC （rag/ m3）=262.173 [Rrs（443）/Rrs（555）]^-0.940. This formula resulted in a small mean bias of approximately -2.52%, a normalized root mean square error of 31.1%, and a determination coefficient of 0.91. This regional empirical equation is different to the results of similar studies in other oceanic regions. Our validation results suggest that our regional empirical formula performs better than the global algorithm, in the South China Sea. The feasibility of this band ratio algorithm is primarily due to the relationship between POC and the green-to- blue ratio of the particle absorption coefficient. Colored dissolved organic matter can be an important source of noise in the band ratio formula. Finally, we applied the empirical algorithm to investigate POC changes in the southwest of Luzon Strait.
