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Due to the external environment and the buoyancy of cyanobacteria, the inhomogeneous vertical distribution of phytoplankton in eutrophic lakes affects remote sensing reflectance (<i>R_rs</i>) and the inversion of surface chlorophyll-<i>a</i> concentration (Chl<i>a</i>). In this study, vertical profiles of Chl<i>a</i>(z) (where z is the water depth) and field <i>R_rs</i> (<i>R_rs_F</i>) were collected and utilized to retrieve the vertical profiles of Chl<i>a</i> in Lake Chaohu in China. Chl<i>a</i>(z) was categorized into vertically uniform (Type 1: N = 166) and vertically non-uniform (Type 2: N = 58) types. Based on the validation of the atmospheric correction performance of the Geostationary Ocean Color Imager (GOCI), a Chl<i>a</i>(z) inversion model was developed for Lake Chaohu from 2011 to 2020 using GOCI <i>R_rs</i> data (<i>R_rs_G</i>). (1) Five functions of non-uniform Chl<i>a</i>(z) were compared, and the best result was found for Chl<i>a</i>(<i>z</i>) = <i>a × exp(b × z) + c</i> (R² = 0.98, RMSE = 38.15 μg/L). (2) A decision tree of Chl<i>a</i>(z) was established with the alternative floating algae index (AFAI_Rrs), the fluorescence line height (FLH), and wind speed (WIN), where the overall accuracy was 89% and the Kappa coefficient was 0.79. The Chl<i>a</i>(z) inversion model for Type 1 was established using the empirical relationship between Chl<i>a</i> (z = surface) and AFAI_Rrs (R² = 0.58, RMSE = 10.17 μg/L). For Type 2, multivariate regression models were established to estimate the structural parameters of Chl<i>a</i>(z) combined with <i>R_rs_G</i> and environmental parameters (R² = 0.75, RMSE = 72.80 μg/L). (3) There are obvious spatial variations in Chl<i>a</i>(z), especially from the water surface to a depth of 0.1 m; the largest diurnal variations were observed at 12:16 and 13:16 local time. The Chl<i>a</i>(z) inversion method can determine Chl<i>a</i> in different layers of each pixel, which is important for the scientific assessment of phytoplankton biomass and lake carbon and can provide vertical information for the short-term prediction of algal blooms (and the generation of corresponding warnings) in lake management.