To understand the spatial interpolation optimization laws of marine phytoplankton abundance, we compared three different methods of spatial interpolation: (1) inverse distance weighting (IDW), (2) radial basis functions (RBF) and (3) Ordinary Kriging (OK), using the recorded phytoplankton abundance in the Zhujiang Estuary, China, in August 2003. Firstly, exploratory spatial data analysis was used to gain a deeper understanding of the recorded phytoplankton abundance. Then the phytoplankton abundance was log-transformed. Lastly, we generated interpolation surfaces of phytoplankton biomass using the three interpolation methods. The results indicate that the phytoplankton abundance data is charactered by high dispersion, with a few outliers, and a positively skewed distribution. The log-transformation reduces the variances and skewed distribution, and effectively removes various interpolation noises in the interpolation surfaces. The accuracy of the OK is the highest, followed by the RBF, and then the IDW. The interpolation surfaces reveal that all of the three methods correctly show general trends of the phytoplankton abundance by using a series of optimization techniques. But the contours generated by the IDW always bend around the global outliers with excessively great curvature and sometimes even form closed small loops, which maybe cause some interference in identifying the general trends. The contours generated by the RBF are excessively smooth. However, it represents the general trends clearly, although many local trends are lost. The contours generated by the OK are considered the best, as the method can represent both general and local trends accurately. Thus, the OK method is more efficient than RBF and IDW in terms of accuracy and surface representation. The four semi-variance models of the OK do not affect the interpolation results, with the circular model having the best fit to the data.