【Objective】 To monitor the above -ground biomass (AGB) of grassland in Tianjun County quickly, accurately and on a large scale.【Method】 This research used the Landsat 8 OLI remote sensing image data of Tian- jun County and the biomass data of 43 sample points in the same period to establish a Normalized Differential Vegeta- tion Index (NDVI),Soil Adjusted Vegetation Index (SAVI),Modified Soil-Adjusted Vegetation Index (MSAVI), Ratio Vegetation Index (Ratio) Vegetation Index,RVI)) and a remote sensing statistical model of aboveground bio- mass in grassland by analyzing the correlation between the remote sensing vegetation index and the aboveground bio- mass of grassland.【Result】 The research showed that there was a good correlation between the remote sensing veg- etation index and the aboveground biomass of grassland in Tianjun County,but the fitting effects of different statisti- cal models were different. The multiple linear regression model established by 4 independent variables had a better fit- ting effect than the univariate linear regression model. The cubic regression model established by the remote sensing vegetation index and grassland aboveground biomass had better fitting accuracy than the univariate linear and multi- variate regression models.【Conclusion】 The linear height was y=116. 12x3 +898. 48x2 +1 672. 1x-1 003. 4,which was suitable for monitoring the aboveground biomass of grassland in Tianjun County. This study provides model sup- port and theoretical reference for the estimation of aboveground biomass of grassland in Tianjun County.