A reconstruction method was developed for hyperspectral remote sensing reflectance (Rrs) data in the visible domain (400–700 nm) based on in situ observations. A total of 2,647 Rrs spectra were collected over a wide variety of water environments including open ocean, coastal and inland waters. Ten schemes with different band numbers (6 to 15) were tested based on a nonlinear model. It was found that the accuracy of the reconstruction increased with the increase of input band numbers. Eight of these schemes met the accuracy criterion with the mean absolute error (MAE) and mean relative error (MRE) values between reconstructed and in situ Rrs less than 0.00025 sr−1 and 5%, respectively. We chose the eight-band scheme for further evaluation because of its decent performance. The results revealed that the parameterization derived by the eight-band scheme was efficient for restoring Rrs spectra from different water bodies. In contrast to the previous studies that used a linear model with 15 spectral bands, the nonlinear model with the eight-band scheme yielded a comparable reconstruction performance. The MAE and MRE values were generally less than 0.00016 sr−1 and 3% respectively; much lower than the uncertainties in satellite-derived Rrs products. Furthermore, a preliminary experiment of this method on the data from the Hyperspectral Imager for the Coastal Ocean (HICO) showed high potential in the future applications for reconstructing Rrs spectra from space-borne optical sensors. Overall, the eight-band scheme with our nonlinear model was proven to be optimal for hyperspectral Rrs reconstruction in the visible domain.