## 1 Introduction  
Vegetation and soil moisture both play very important roles in land surface modeling by supporting critical functions in the global and regional water and energy cycles. Vegetation impacts soil characteristics, e.g., its water content, chemistry, and texture, that have feedback on vegetation characteristics, including productivity and structure [1]. Soil moisture represents the land storage for water and energy, effectively controlling the balance between sensible and latent heat flux at the land-atmosphere interface. Soil moisture content impacts atmospheric processes, such as cloud coverage and rainfall, thus affecting hydrological processes such as runoff and plant transpiration [2; 3]. To represent vegetation structural and functional variables, dynamic vegetation models (DVMs) have been designed [4]. In the past, DVMs and land surface models (LSMs) have been combined to improve the estimation of water, carbon, and energy variables [5; 6]. As an example, the Noah Multi-Parameterization LSM (hereinafter Noah-MP), which was developed by [PERSON] et al. [7], combines an LSM with a DVM, using a module that allocates carbon to various parts of vegetation and soil carbon pools. However, land surface models are affected by several uncertainties because of different reasons, such as inaccurate initialization, mis-specified forcing and parameters, or inadequate model physics [8]. As an alternative to model simulations, satellite observations also offer the estimations of several land surface variables over the global domain. For vegetation, satellites produce maps of the leaf area index (LAI), defined as the single-sided leaf surface area estimated over the unit ground area, and normalized difference vegetation index (NDVI), estimated by spectral reflectance measurements received in the red and near-infrared regions [9]. For example, the Moderate Resolution Imaging Spectroradiometer (MODIS), which has been receiving data in 36 spectral bands since 2000 [10], and the Advanced Very High-Resolution Radiometer (AVHRR; [11]) produce global maps of vegetation indices. Satellite observations also provide soil moisture information. For instance, the Soil Moisture Active Passive (SMAP; [12]) mission launched in 2015 provides direct sensing of soil moisture in the top 5 cm of the soil column. Satellite-based observations are affected by gaps in both spatial and temporal coverages (often because of cloud coverage or instrument malfunctioning). To address the gaps in satellite observations and reduce uncertainties in both models and observations, data assimilation (DA) can be adopted to optimally combine the information from observations and model estimates [13]. Land Data Assimilation Systems (LDASs) have been used in the past successfully for merging satellite observations into LSMs [3; 13; 14]. DA has the potential to improve model estimates of land surface variables, which is useful for different purposes, including the monitoring and forecasting of extreme hydrological events, crop water management, and ecological studies. DA can be particularly effective in data-poor regions, where ground observations are sparse or not available at all. Soil moisture has been assimilated in land surface models to generate a superior soil moisture product in numerous experiments, which used field measurements and satellite retrievals [3]. For large-scale soil moisture DA, the Ensemble Kalman Filter (EnKF) has become a method of choice [15; 16]. [PERSON] et al. [17] performed a comparison between the Extended Kalman Filter and the EnKF for soil moisture estimation in a twin experiment over the southeastern United States, and both methods provided satisfactory estimates of soil moisture. In the last decade, real satellite soil moisture observations have been used in several experiments for DA within different land surface models [18; 19; 20; 21; 22; 23]. More recent studies have applied DA techniques to vegetation observations and vegetation dynamic models. For instance, [PERSON] et al.