Abstract: Abstract: The rapid and precise estimation model of herbage yield using remote sensing technology is of great significance to maintain grassland ecological balance, reasonably manage the yield of animal husbandry and determine carrying capacity. Taking the Tianshan pastoral area of Xinjiang into account, the vegetation index NDVI (normalized difference vegetation index), the vegetation index EVI (enhanced vegetation index), the soil conditioning index SAVI (soil adjusted vegetation index) and the difference index DVI (soil adjusted vegetation index) were calculated using the MODIS/MOD13Q1 data with 250 m spatial resolution. And the 4 indices and their different combinations were used to establish a regression model related to total herbage yield (fresh weight) of grassland for each subregion divided by herbage use type. Then the leave-one-out cross validation method was used to evaluate the precision of the model and the difference in simulation of different vegetation indices in the same pastoral area so as to obtain the pasture herbage yield - optimal vegetation index estimation model in the cases of different pasture use types. So, spatial distribution and difference of the herbage yield (fresh weight) in Tianshan mountainous area could be estimated. The results showed that estimation precision using nonlinear regression equation with respect to single vegetation index is higher than that of the linear regression equation. There is a nonlinear relationship between the herbage yield and the vegetation index derived from remote sensing image. Secondly, the precision of grass yield estimation model based on the combination of multiple vegetation indices is higher than that of the model based on one vegetation index in that the use of the combination of complementary information from different vegetation indices is preferred to improve the estimation precision of grass yield with the remote sensing technology. Additionally, the 4 indices including NDVI, EVI, DVI and SAVI were correlated to the herbage yield of pasture and could be used to estimate the herbage yield. However, there is a difference in estimation precision for different vegetation indices, the maximum of RMSE (root mean square error) when fitting with quadratic polynomial and SAVI occurs in the Subregion I, which is 5 857 kg/hm2, and the minimum of RMSE when fitting with quadratic polynomial and NDVI occurs in the Subregion III, which is 616.487 kg/hm2. It is noteworthy that both DVI and SAVI considering environment factors especially like vegetation cover circumstance in the study area are most suitable for herbage yield monitoring. The work can provide significant suggestion for the estimation of total production of herbage in Tianshan Mountains.