Abstract: To accurately estimate grassland biomass is of significance for the reasonable management of regional stock-raising and the evaluation of ecological benefit. Various vegetation indices and regression functions have been used in estimating grassland biomass by remote sensing data. Based on the field survey and MODIS data, and adopting the significant remote sensing-based vegetation indices including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Modified Soil Adjusted Vegetation Index (MSAVI), three regression models (linear, power, and exponential functions) for each paired grassland biomass and vegetation index in Keerqinzuoyihou County, Inner Mongolia were established and compared. The results showed that grassland biomass could be well simulated by linear, power, and exponential functions, and exponential function performed best. Three vegetation indices (NDVI, EVI and MSAVI) had significant positive correlation with grassland biomass, and were suitable for the successful quantification of grassland biomass based on MODIS. MSAVI functioned most efficient with above-ground biomass (R²＝0.900), and the simulation of total biomass was more effective than that of above-ground grassland biomass by using linear function with NDVI and EVI.

Key words: Soil moisture, Wheat with specialized end-uses, Photosynthetic characteristics, Yield