Abstract: 【Objective】 Therefore, the monitoring of dynamic changes in natural grasslands is important for both ecology and agriculture. 【Method】 This study focused on the impact of changes in plant species on the inversion accuracy.Spectral data for two mountain steppe plots were collected using a spectrometer over three years and concurrently, supporting data were obtained. 【Result】 The results showed that when the physical model PROSAIL was used to invert the mountain steppe LAI, the inversion error was very large for a single solution.Adding random noise and averaging multiple solutions when solving significantly improved the LAI inversion accuracy.The coefficient of determination (R²) for the LAI inversion for most cost functions was between 0.54 and 0.55, root mean square error (RMSE) was between 0.23 and 0.25, and normalized root mean square error (NRMSE) was between 17 and 19.In nine cost functions from different statistical types, the inversion accuracy of the commonly used RMSE cost function was relatively low.The 427 samples obtained were divided into four groups according to the number of species.The inversion results indicated that a greater number of species corresponded to an increasing RMSE, decreasing R², and poorer LAI inversion accuracy, although the precision decrease was not uniform.Groups with up to two species and groups with up to three species had the greatest difference in inversion accuracy. 【Conclusion】 When using physical model to invert LAI of natural grassland, the precision of inversion obtained by different cost functions is different.As the number of vegetation species increases, the precision of inversion decreases.