## 1 Introduction  
An accurate representation of land surface variables (LSVs), such as soil moisture or vegetation cover, is critical in climate science as well as environmental monitoring and prediction (e.g., in order to cope with drought, flood, or other extreme events). To that end, land surface models (LSMs) have been widely used to simulate and predict the Earth's water storage energy budgets over a broad range of time scales [1; 2; 3; 4]. For instance, the AMMA (African Monsoon Multidisciplinary Analysis) Land Surface Model Intercomparison Project (ALMIP) used a set of LSMs forced in offline mode by a combination of satellite products and high quality in situ measurements in order to better apprehend LSV processes and their representation [5; 6]. These LSMs are intended to reproduce LSVs, such as surface and root zone soil moisture (SSM and RZSM, respectively), vegetation biomass, and leaf area index (LAI), together with surface energy fluxes and streamflow simulations. Over the last two decades, much progress has been made on the degree to which realistic land surface initialization contributes to the skill and performance of subseasonal land-related predictability as documented by [7; 8] in the Global Land-Atmosphere Coupling Experiment (GLACE). LSMs have subsequently benefited from the growing development of observational networks. Unfortunately, those are not evenly spaced and data sparse regions remain very difficult to model with accuracy. This is the case of West Africa ([5; 9]), where LSVs are of primary importance, as emphasized by many studies, see, e.g., [10; 11]. Representation of LSVs by LSMs can be improved through the dynamical integration of observations [12; 13], and remote sensing observations are particularly useful in this context as they are now unrestrictedly available at a global scale with high spatial resolution and with long-term records. Land data assimilation systems (LDASs) combine LSMs with satellite observations in order to produce reanalyses of LSVs. Several LDASs now exist, amongst them are the Global Land Data Assimilation System (GLDAS, [1]), the Carbon Cycle Data Assimilation System (CCDAS, [14]), the Coupled Land Vegetation LDAS (CLVLDAS, [15; 16; 17]), the U.S. National Climate Assessment LDAS (NCA-LDAS, reference [18]) as well as LDAS-Monde [19] to name a few. More recently, soil moisture (SM) data from the Soil Moisture Operational Product System (SMOPS) has been assimilated in the Noah model [20]. Those systems either optimize process parameters (e.g., CCDAS), state variables (e.g., GLDAS, NCA-LDAS, LDAS-Monde), or both (e.g., CLVLDAS). Only few studies have considered the integration of multiple remote sensing measurements [17; 19] and even less have had a specific focus over West Africa (e.g., [21]). In this context, the present study aims to evaluate reanalyses of LSVs obtained with LDAS-Monde [19] over Burkina Faso in West Africa (domain shown in Figure 1). This country exhibits three distinctive climates: Sahel, Sudan-Sahel, and Sudan-Guinea that cover most part of West Africa, making Burkina Faso an area of interest for such study. A southward gradient characterizes rainfall distribution across the country: Mean annual precipitation decreases from more than 1100 mm in the South to approximately 300 mm in the North [22]. Daily mean temperature fluctuates between 21 and 34 \({}^{\circ}\)C (17 and 37 \({}^{\circ}\)C) during the rainy season (dry season) across Burkina Faso [23]. LDAS-Monde [19] is based on the CO\({}_{2}\)-responsive version of the Interactions between Soil, Biosphere, and Atmosphere (ISBA) LSM [24; 25; 26; 27] available through the SURFX (SURFace EXternalisee; [28]) modelling system of Meteo-France. The reanalysis is performed by assimilating jointly satellite-derived SSM and LAI using a simplified extended Kalman filter (SEKF).