Abstract: Background Aerodynamic roughness (z0) reflects the degree to which surface roughness elements attenuate wind erosion force, playing a critical role in soil wind erosion simulation in arid and semi-arid regions. Investigating remote sensing estimation models for surface roughness elements in grassland areas during spring and their influencing factors is of great significance for improving the parameterization accuracy of wind erosion models and promoting sustainable development in wind erosion-prone areas. Methods Based on this, this study selected the Xilingol Grassland in the Inner Mongolia Autonomous Region as the research area. Using measured z0 data and linear regression analysis, the correlations between different vegetation indices and measured z0 were analyzed. A remote sensing estimation model of spring z0 was established based on MODIS (MCD43A4) data. Furthermore, the geographical detector method was used to analyze the influencing factors of the spatial differentiation of spring z0 in the study area from 2010 to 2022. Results 1) The Simple Tillage Index (STI), calculated using the ratio of Band 6 to Band 7 in the short-wave infrared (SWIR) region of MODIS data, had the highest correlation with measured z0. The correlations of the vegetation indices, in descending order, were as follows: STI>NDTI>DFI>LAI>NDVI. 2) The STI-z0 remote sensing model (R² = 0.83) demonstrated the best fit and the highest validation accuracy (R² = 0.53, RMSE = 0.1257). 3) From 2010 to 2022, spring z0 showed a fluctuating upward trend, with a multi-year average z0 of 0.55 cm. 4) The dominant factor influencing the spatial differentiation of spring z0 in the study area from 2010 to 2022 was the Dead Fuel Index (DFI), which represents non-photosynthetic vegetation cover, followed by wind speed and vegetation type. Conclusion The findings of this study can provide data for improving the prediction accuracy of soil wind erosion models and offer a scientific basis for the long-term dynamic monitoring and prevention of soil wind erosion.