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Mineral dust is among the top contributors to global aerosol loads. Ability of non‐photosynthetic vegetation (NPV) to suppress dust emission has been widely acknowledged but a realistic representation of NPV has not been tested with regional‐to‐global scale models. In this study, we implemented a satellite‐based total vegetation data set, which included NPV, into a regional atmospheric chemistry model and conducted simulations for the year 2016 over the conterminous United States. To test the response of dust simulations to the NPV coverage, we conducted a control simulation incorporating only the photosynthetic vegetation (PV). Simulated dust emissions decrease by 10%–70% over most of the southwestern US from spring to autumn due to NPV. Reductions in dust concentrations are the largest in spring, which attenuate the overpredictions of fine soil concentrations, but accentuate the underpredictions in summer. Overall, the mean errors and correlations of annual simulations are slightly improved with NPV. NPV modulates dust emissions mainly by sheltering the surface and increasing the threshold velocity through drag partitioning. Moreover, we investigated the effect of vegetation height and addressed its uncertainties through a series of sensitivity tests. We observed that a 50% variation in predefined vegetation heights results in small changes in soil concentrations over majority of southwestern US, but causes up to 30% changes at local hotspots. This study highlights the significance of including NPV into the dust model and points out the importance of validation of total vegetation datasets as well as more realistic representation of vegetation heights and seasonality. Plain Language Summary: Severe dust‐emission events can interrupt traffic, damage infrastructure, and incur cleaning expenses locally. Dust particles that are lifted into the air by wind are also associated with global health problems and climate effects. Most of the global dust emissions come from arid or semi‐arid environments where the brown vegetation is abundant, and dust emissions are thus modulated by the presence of brown vegetation. However, most of the current atmospheric models omit brown vegetation because it cannot be detected easily similar to green vegetation. In this study, we provided a total vegetation (sum of green and brown vegetation) data set as an input to an atmospheric chemistry model, and simulated annual dust emissions over the conterminous United States. We find that the brown vegetation reduces the dust concentrations in air by above 10% over most of the southwestern US from spring to autumn. The reductions are mainly because the brown vegetation directly protects the surface from wind erosion, as well as reduces the drag on the surface such that a minimum wind speed needed to initiate dust emissions becomes higher. Meanwhile, we found that changes in vegetation height can moderately change dust emissions over certain areas. Key Points: A satellite‐based total (photosynthetic and non‐photosynthetic) vegetation data set is implemented in a dust emission modelNon‐photosynthetic vegetation reduces simulated dust concentrations by 10%–70% in most areas of the southwestern US from spring to autumnVegetation height has important local effects on simulated dust concentrations [ABSTRACT FROM AUTHOR]