Chlorophyll-a concentration is an essential indicator for estimating phytoplankton biomass, and continuous wavelet transforms is an essential multi-scale spectral analysis method. In this study, taking western Guangdong and Pearl River Estuary as the study area, ten mother wavelet functions are selected based on the water surface hyperspectral and measured chlorophyll-a concentration data to perform continuous wavelet transformation on hyperspectral reflectance data. The partial least square regression (PLSR) method was used to develop the chlorophyll-a concentration inversion model. Besides, the influence of different wavelet transformation coefficients on the modeling results was analyzed and compared. The results showed that the correlation between wavelet coefficients after various wavelet transformations and measured chlorophyll-a concentration is higher than that between the original spectrum and measured chlorophyll-a concentration. Besides, the results showed that the inversion accuracy varies greatly with models based on different wavelet coefficients. The partial least squares regression(PLSR) model based on sym6 wavelet coefficients has the best accuracy (determination coefficient R² is 0.732, root-mean-square error is 6.457 μg/L, relative percent deviation is 2.600). Compared with the traditional inversion method based on spectral characteristics, it performs better and provides a wavelet-based optimization selection in the construction of the chlorophyll-a concentration model of case Ⅱ water in the future.