Land surface radiation is an important parameter with regard to energy cycling and global water- heat balance. Estimating land surface radiation is a significant part of global climate change research. Series of remote sensing data have been used in the calculation of each parameter of the land surface radiation budget. The MODIS data are widely used, but the schemes are complicated. How to estimate land surface radiation easily and accurately is always an appropriate subject of debate. The GLASS data were published by Peking University in 2012 and can be directly used in global change research. Firstly, GLASS data (including albedo data, land surface emissivity data and downward shortwave radiation data), MODIS land surface temperature data and ground observed data were employed to develop a modified parameterization scheme of net radiation based on the research published by Bisht et al. in 2005. The maximum net radiation over eight continuous days in the middle of each month in 2010 was calculated using the scheme presented in this paper. Because the values that were obtained at the time of the satellite passing by were instantaneous, the instantaneous values were translated into the daily maximum data using the modified sinusoidal model and validated using ground observed data. There was a good agreement between ground observed data and the parameterization results and the average bias was 27.21 W? m~(-2). The results showed that the scheme presented in this paper was an effective model to calculate land surface radiation over China. Our scheme required less input data. It can be applied to calculate land surface radiation at the scale of a large region. The spatial pattern change over China in 2010 was also analyzed. The results show that the land surface radiation changes significantly with the seasons and there are differences between different places at the same time. Generally, the value of net radiation increases in the first eight months and decreases in the last four months. The maximum net radiation gradually decreases from south to north. The probable reason is the different characters of the underlying surface, including land cover and geographic location.