Algal blooms occur frequently in the freshwater lakes and reservoirs in China, but the characteristics, hazards, and treatment methods of different bloom-forming algae species are significantly different, consequently, a critical scientific problem that must be solved is how to distinguish the reflectance characteristics of different algal species and identify dominant species in lakes using remote sensing reflectance. Based on laboratory algae culture experiments, the research produced typical cyanobacteria species (including Microcystis aeruginosa, Pseudanabaena sp. and Aphanizomenon sp.) and green algae species (including Chlorella sp. and Scenedesmus quadricauda), which are prevalent in eutrophic lakes. Subsequently, the normalized in-situ remote sensing reflectance characteristics of different algal species were analyzed, the DI (difference index) and ADI (algae distinguish index) were constructed based on spectral differences, and then the algal species identification model was established. Validated by independent verification data set, the overall recognition accuracy and Kappa coefficient were 77.55% and 0.7178, respectively. The results of classification algorithm match well with the measured biomass data when applied to the field measured remote sensing reflectance data set of Lake Taihu. Further model tests based on the OLCI images of Lake Taihu in December 2019 and August 2020 show that the proportion of cyanobacteria in Lake Taihu is higher than that of green algae in summer and winter, during which Microcystis displayed obvious advantages and a stronger dominant position in summer than in winter. Furthermore, in terms of seasonal and geographical distributions, the dominant areas of Microcystis were in the northern and southern bays of Lake Taihu in winter, while Pseudoanabaena was in the central area of the lake, and scattered with a tiny fraction of Aphanizomenon and Scenedesmus quadricauda at the same time; compared to summer, Microcystis was dominant in most areas of Lake Taihu, Pseudoanabaena was mainly distributed in the center and south of the lake, and the other three species only scattered in a small part of the lake. The development of a remote sensing identification model of the main algal species can provide technological assistances to remote sensing monitoring of the lake environment, as well as algal bloom prediction and prevention.