Snow grain size is a key parameter not only to affect the energy budget of the global or local region but also characterizing the status of snow vapor transport and temperature gradient. It is significant to monitor and estimate the snow grain size in large area for global or local climate change and water resource management. Recently, remote sensing technology has become a useful tool for snow grain size monitoring and estimating. In the present paper, the estimate models were built based on simulating the snow surface spectral reflectance curve in visible-infrared region and the sensitive bands and snow indices for snow grain size were selected. These models help estimate snow grain size by hyperspectral remote sensing. Through validating with ground true data, the results show that these models have higher explorative accuracy using 1 030, 1 260 nm and normalized difference snow index (460 and 1 030 nm). In addition, the correlation slopes of estimated and observed valves are 1.37, 0.61 and 0.62, respectively. R2 are 0.82, 0.86 and 0.93 and RMSE are 55.65, 50.83 and 35.91 μm, respectively. The result can provide a scientific basis for snow grain size monitoring and estimating.