Passive microwave vegetation optical depth (VOD) has been increasingly used for global vegetation monitoring in the last decade. It has, for example, been used to monitor global changes in phenology, vegetation health, vegetation water content/iso-hydricity, and biomass in time and space. Compared to optical-based satellite vegetation data, VOD has a higher temporal frequency. The higher revisit times of the wide-swath satellite sensors, the independence of solar illumination and the limited sensitivity to cloud cover can strongly increase the data coverage in some areas, although at the cost of higher retrieval errors and lower spatial resolutions. Therefore, VOD has recently been used as proxy for optical-based leaf area index (LAI) in regional data assimilation studies using land surface models (LSMs). The studies showed that VOD assimilation can improve both carbon-related and water-related land surface variables, like gross primary production (GPP), evapotranspiration (ET), or root zone soil moisture. Current studies in the literature only consider the effect of biomass or LAI on VOD. However, it is well known that VOD is mainly sensitive to absolute vegetation moisture content. In the last few years, the effect of relative vegetation moisture content variations on VOD has been coming more into focus. We therefore assimilated VOD into the Noah-MP LSM using a novel approach that takes not only dry biomass variations, but also vegetation moisture content variations into account. This is accomplished with an empirical model of VOD as a function of dynamically simulated LAI, soil moisture, and vapor pressure deficit as an observation operator. We evaluate this novel approach by assimilating X-band VOD from the VOD Climate Archive into the Noah-MP LSM over Europe in the years 2002 to 2010 at a 0.25° model resolution. The results are compared to an assimilation of X-band VOD using an approach from the existing literature, and to an assimilation of optical LAI from the Copernicus Global Land Service (CGLS), focusing on the improvements made in the representation of vegetation-related state variables and fluxes in the resulting dataset, especially GPP and ET.