Using unmanned aerial vehicle (UAV) near-ground remote sensing technology to estimate grassland biomass is a popular method at present. However, the inversion model types, variables and algorithms are quite different. Modeling variables such as band reflectance and vegetation index were obtained by UAV multispectral images combined with the actual survey data of ground samples in Xilingol. The prediction models of grassland aboveground biomass constructed by eight most commonly used parametric and non-parametric methods were constructed and compared. The accuracy and modeling variables of different models were evaluated in order to optimize the best prediction model. The results showed that among the eight models, the accuracy of the parametric model was relatively low, and the nonparametric model had higher accuracy. The multivariable generalized linear model in the parametric model was better than the linear, logarithmic and exponential models. Among the nonparametric model, the model determination coefficients R² of K nearest neighbor, Support vector machine, XGBoost and Random forests were all greater than 0.7 and the random forest model was relatively more robust and had the least number of model variables. Among the modeling variables, the normalized difference vegetation index and red band reflectance played important roles in biomass estimation. In summary, the random forests model is more suitable for UAV near-ground remote sensing technology to estimate grassland biomass in grasslands of Xilingol, Inner Mongolia. But the hyperparameter tuning and algorithm optimization, as well as vegetation multi-source variable selection and other aspects need more in-depth researches.