The mapping of grassland biomass is of fundamental importance for estimating carbon budgets and the optimal use of grassland resources.The High Accuracy Surface Model for Grassland Biomass Simulation（HASM-GB） model was developed to estimate grassland aboveground biomass in Inner Mongolia

China.The ground truth biomass data and Normalized Difference V egetation Index（NDVI） were used to predict maximum growing season biomass maps using the HASM-GB model.To evaluate the performance of HASM-GB

it was compared with three other methods: Satellite-Based Regression Model（REG）

Ordinary Kriging（OK）

and Regression Kriging（RK）.As expected from theory

HASM-GB generally performs better than REG

OK

and RK

with a lower estimation bias

mean absolute error

root mean square error and higher correlation coefficient for measured and simulated values.From the predicted grassland biomass maps

the aspatial method vegetation index-biomass relationship technique was directly used with the variable NDVI to estimate biomass

and the precision of the results depend largely on how closely the primary and secondary variables are related.The spatial variation of the biomass produced by this method is very similar to the spatial variation of NDVI

so the simulation result is sensitive to errors in NDVI data.The OK method cannot factor information regarding vegetation index.However

HASM-GB can consider both the spatial structure of the measured biomass values and the NDVI data affecting local spatial trends

and it also had a higher precision of interpolation than RK.Consequently

HASM-GB is shown to be relatively effective for simulating spatial patterns of grassland biomass.