## 1 Introduction
For that purpose, the most recent SURFX_v8.1 Offline Data Assimilation (SODA) implementation has been utilized considering a long-term period (January 2001 to June 2018) along with the joint assimilation of both satellite-derived SSM (from 2007, [29]) and LAI (GEOV2, from 2001, [[http://land.copernicus.eu/global/](http://land.copernicus.eu/global/)]([http://land.copernicus.eu/global/](http://land.copernicus.eu/global/)), last access November 2018). The quality of LSV reanalyses depends on the quality of LSMs and observations used, but also on the quality of atmospheric forcings used by LSMs. Numerous improvements were made in the generation of long-term (1979-onwards) global atmospheric reanalyses, leading to more advancements in land surface modeling fields and their applications (e.g., water resources monitoring [2; 30; 31]. In line with those improvements, NASA's (National Aeronautics and Space Administration) Modern Era Retrospective analysis for Research and Applications (MERRA; [32], and MERRA2; [33]) as well as ECMWF's (European Centre for Medium-Range Weather Forecasts) Interim reanalysis (ERA-Interim; [34]) were the most investigated. We take advantage of the recent development of ERA5, which was released in 2017 as the fifth generation of ECMWF global atmospheric reanalyses. At the time of this study, a time-slice of the ERA5 database was available from 2001 within 3 months of real time. ERA5 brings extensive changes compared to ERA-Interim, including higher spatial and temporal resolutions as well as a generally improved representation of, e.g., precipitation and incoming solar radiation (SW\({}_{\rm in}\)) [4; 35; 36]. The performance of ERA5 and ERA-Interim precipitation and SW\({}_{\rm in}\) is first investigated using in situ measurements over Burkina Faso (BF) before studying the quality of LDAS-Monde renalysis of LSVs forced by either ERA5 or ERA-interim. Section 2 provides (i) a description of LDAS-Monde, including details on the CO\({}_{2}\)-responsive version of the ISBA LSM and the data assimilation system, (ii) information on the atmospheric reanalyses used to force the system, the assimilated remotely sensed observations along with the in-situ and satellite datasets used to assess the sensitivity of the results to the atmospheric reanalysis, and (iii) the experimental set up and the evaluation strategies. Section 3 presents the results of a performance assessment of the reanalyses against in situ measurements, the assimilation impact of the  
Figure 1: Averaged (**a**) surface soil moisture from ASCAT (obtained after rescaling to model climatology, see Section 2.2.3). (**b**) GEOV2 leaf area index (for pixels covered by more than 90% of vegetation) from the Copernicus Global Land Service project averaged at 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) spatial resolution over the domain centred on Burkina Faso (latitudes from 9.0\({}^{\circ}\)N to 16.0\({}^{\circ}\)N and longitudes from 7.0\({}^{\circ}\)W to 3.0\({}^{\circ}\)E). The symbols, SH (Sahel), SS (Sudan-Sahel), and SG (Sudan-Guinea), represent the three agroclimatic regions across Burkina Faso (BF). assimilated variables as well as independent satellite datasets, and Section 4 provides perspectives and future directions.