## 1 Introduction
[24] performed an experiment to assess the impact of satellite-based LAI DA by applying an EnKF technique to the Noah-MP LSM over the Continental U.S. (CONUS). The results showed that LAI DA improves the estimation of key water budget terms (i.e., soil moisture, evapotranspiration (ET), terrestrial water storage, and streamflow) and carbon fluxes (i.e., gross primary production and net ecosystem exchange) when validated with ground-based reference datasets, but the improvement in surface soil moisture was still limited. In another study, [PERSON] et al. [25] assimilated Global Land Surface Satellite (GLASS) LAI data using an Ensemble Adjustment Kalman Filter technique over the global domain. The results demonstrated that the DA reduced the bias in LAI (from 5 m\({}^{2}\)/m\({}^{2}\) to \(\pm\)1 m\({}^{2}\)/m\({}^{2}\)). Two past studies proposed synthetic experiments to assimilate LAI alone within Noah-MP using an EnKF [26] and direct insertion (DI) [9]. The results showed an improvement in a subset of water and vegetation variables and a reduction in the impact of large biases in the model input precipitation. However, the improvement in soil moisture was still limited. Past work that jointly assimilated vegetation and water variables has shown some promise. As an example, [PERSON] et al. [27] assimilated LAI and surface soil moisture observations within the CERES-Wheat model for winter wheat yield estimation, using a particle filter algorithm across the Guan Zhong Plain, China. Another study jointly assimilated LAI and soil moisture from Sentinel-1 and Sentinel-2 data into a crop model for winter wheat yield estimation in Heng Shui city, China. Although promising, these studies focused on regions of limited spatial extension [28]. A study by [PERSON] et al. [8] investigated the joint assimilation of surface soil moisture and LAI within the Land Data Assimilation System LDAS-Monde over the Euro-Mediterranean Region. They adopted a deterministic ensemble square root filter. This joint assimilation was shown to moderately improve ET and highly improve river discharges. LDAS-Monde was developed for performing data assimilation using the ISBA (Interaction Sol-Biosphere-Atmosphere) land surface model, initially developed for France and then extended to the global domain [29]. ISBA interfaces with an atmospheric and a hydrological model but simulates above-ground plant biomass and LAI with a simplistic plant growth model (ISBA-A-gs) [30]. In another recent work, [PERSON] et al. [31] performed a joint assimilation of satellite-derived LAI and soil water index into the ISBA model to monitor the impact of extreme events (heat waves and droughts), using the Simplified Extended Kalman Filter. This work stems from the studies discussed above and further investigates the potential of jointly assimilating satellite LAI and soil moisture products. Specifically, for the first time, it jointly assimilates GLASS LAI and SMAP soil moisture within the Noah-MP model over the CONUS domain. Noah-MP uses a more sophisticated dynamic vegetation model scheme compared to other similar models used in the literature (e.g., ISBA) [31]. As past work showed that LAI assimilation alone could not improve the estimation of soil moisture (specifically surface soil moisture), the main goal of this work is to improve our estimation and understanding of soil moisture storages in addition to analyzing the impact on vegetation variables.