The relationship between water surface reflectance and total depth integrated algae biomass can be very complex as different kinds of algal vertical distributions can occur. For this reason, effectively identifying the algae vertical profiles is fundamental to estimate algal biomass. Gaussian profiles are the most typical algae vertical profiles which occur in most environmental conditions (including external and internal system). In this research, a back propagation (BP) neural network was established to estimate Gaussian distribution parameters of the vertical structure h and σ by wave bands R_rs(469), R_rs(555), R_rs(645) and chlorophyll-a concentration band C_Chl.a(0). The BP neural network was trained by using 3000 simulated datasets (radiative transfer simulation based on in-situ measured data by HydroLight), and verified by another 200 groups of simulated data and measured data. The correlation coefficient between estimated and measured h and σ were 0.97 and 0.95, while the relative errors were 13.20% and 12.36%, respectively. The relative error of h and σ was mostly less than 30%. This indicated that it is a good effectiveness of BP neural networks to estimate the vertical distribution parameters and able to explore the three dimensional algal distribution in Lake Chaohu, thereby providing a significant theoretical basis for remote sensing estimation of algal biomass.