A model for estimating the contributions of phytoplankton and nonalgal particles to the total particulate absorption coefficient was developed based on their separate spectral relationships,and a constrained nonlinear optimization code was used to realize the spectral decomposition. The spectral absorption of total particulate matter including phytoplankton and nonalgal particles was measured using the filter-pad method during two cruises in autumn in Northern South China Sea.Using the dataset collected iN₂004,the spectral relationships of particle absorption coefficients were examined and the results showed that the phytoplankton absorption coefficients at various wavebands could be well expressed by a_ph (443) as the second-order quadratic equations;and the nonalgal particle absorption (a_NAP(λ)) could be successfully modeled with the simple exponential function.Based on these spectral relationships,we developed this partition model.The model was tested using the independently measured absorption by phytoplankton and nonalgal materials which were obtained iN₂005 from the same area.The test results showed that the computed spectral absorption coefficients of phytoplankton and nonalgal particles were consistent with in situ measurement.Good correlations were found between the computed phytoplankton absorption coefficient and the measured value,with the determination coefficients (r2) being higher than 0.97 and slopes being around 1.0;and the RMSE values could be controlled within 17% over the main absorption wavebands such as 443,490 and 683 nm.Compared with the other two existing models from Bricaud et al.and Oubelkheir et al.,this method shows many advantages for local applications.Moreover,this model does not need any information about pigment concentrations and the selected spectral bands are consistent with the ocean color satellite sensor.This method could also be used in the total absorption coefficient decomposition which provides much insight into the phytoplankton absorption retrieval from in situ measurement and ocean color remote sensing data.