Cyanobacteria is the dominant algae species in inland eutrophic water bodies, and the phycocyanin ( PC) is its unique pigment which can be used as an indicator of its presence. Therefore, the retrieval of PC concentration by remote sensing is of great significance to early warning of cyanobacteria bloom. In this paper, the Random Forest retrieval Model for estimating PC concentration based on the sentinel 3 A-OLCI bands was developed using in situ data collected from Taihu Lake, Dianchi Lake andHongzehu Lake. The results of the importance analysis of input variables in random forest demonstrated that the seventh band ( 674 nm) , the eighth band ( 665 nm) and the ninth band ( 620 nm) have significant impact on the PC estimation. The accuracy assessment showed that the Mean Absolute Percentage Error ( MAPE) of this PC retrieval model is only 34. 86% with the Root Mean Square Error ( RMSE) of38. 67 μg/L. The comparison between the mode developed by this paper and other models, i. e., Simis semi-analytic algorithm and PCI exponential model was extensively conducted, and it was found that compared with other two models, the MAPE was improved by 85. 65% and 15. 65% respectively, and the RMSE was improved by 26. 08 μg/L and 19. 86 μg/L respectively. The atmospheric correction accuracy was further analyzed using the in situ samples and synchronous satellite image, and the result showed that the Management Uint Mathematical Model ( MUMM) method can be successfully used for the OLCI image. The atmospheric corrected spectral curves are consistent with the measured spectral curves, and the MAPEs of 8 bands are all less than 30% at the wavelength range between 560 and 779 nm. The random forest model developed for estimating PC concentration in this paper can be successfully applied to Sentinel 3 A-OLCI images, which provides a newalgorithm for remote estimation of phycocyanin concentration in inland lake.