Water quality estimation in fresh and marine water systems with in situ above-water spectroscopy requires measurement of the volume reflectance (rhov) of water bodies. However, the above-water radiometric measurements include surface reflection (Lr) as a significant component along with volume reflection. The Lr carries no information on water quality, and hence it is considered as a major source of error in in situ above-water spectroscopy. Currently, there are no methods to directly measure Lr. The common method to estimate Lr assumes a constant water surface reflectance (rhos) of 2%, and then subtracts the Lr thus calculated from the above-water radiance measurements to obtain the volume reflection (Lv). The problem with this method is that the amount of rhos varies with environmental conditions. Therefore, a methodology was developed in this study for direct measurement of water volume reflectance above water at nadir view geometry. Other objectives of this study were to analyze the contribution of Lr to the total water reflectance under various environmental conditions in a controlled setup and to develop an artificial neural network (ANN) model to estimate rhos from environmental conditions. The results showed that Lr contributed 20-54% of total upwelling radiance from water at nadir. The rhos was highly variable with environmental conditions. Using sun altitude, wind speed, diffuse lighting, and wavelength as inputs, the ANN model was able to accurately simulate rhos, with a low root mean square error of 0.003. A sensitivity analysis with the ANN model indicated that sun altitude and diffuse light had the highest influence on rhos, contributing to over 82% of predictability of the ANN model. Therefore, the ANN modeling framework can be an accurate tool for estimating surface reflectance in applications that require volume reflectance of water.