## 4 Discussion and Conclusions  
This study focused on the capability of LDAS-Monde to provide an improved reanalysis of land surface conditions through the joint assimilation of satellite-derived soil moisture and vegetation products over Burkina-Faso in western Africa. The LDAS-Monde offline system was forced by both ERA5 new atmospheric reanalysis and ERA-Interim former atmospheric reanalysis, leading to a 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) and a 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\) degree spatial resolution reanalyses. The quality of ERA5 with respect to the former ERA-Interim reanalysis was evaluated using (i) in situ measurements of precipitation and incoming solar radiation (\(\text{Sw}_{\text{in}}\)) as well as (ii) the output of LDAS-Monde. The comparison of the two atmospheric forcing yields two key results: ERA5 provides substantial improvements compared to ERA-interim for precipitation (over 2010-2016), and also for the \(\text{SW}_{\text{in}}\) variable (over 2017). Using ERA5 and ERA-Interim to force the ISBA LSM (open-loop) provides a good model first guess, e.g., on the LAI variable with an advantage to ERA5. However, comparing the open-loop with the observed LAI has highlighted the missing processes in the representation of vegetation phenology. The assimilation is able to improve the simulation of both SSM and LAI when using either ERA5 or ERA-Interim, showing that the analyses add skill to both configurations and indicating the healthy behavior of LDAS-Monde. From the analysis, important improvements in the representation of the LAI, SWI, and GPP variables were obtained with better scores for the analysis than for the model equivalent (open-loop simulation). In particular, the LAI analysis is very good at compensating for caveats, such as the model's failure in capturing leaf onset prior to the first rains for particular plant species. The LAI representation could be enhanced even more using more efficient observation and assimilation systems. For example, one single LAI observation was used within a grid cell for all types of vegetation to perform the assimilation, making the Kalman gain and the increment disaggregation rely on the vegetation type as in [37]. This procedure could be improved by performing the assimilation using disaggregated values of LAI for each individual vegetation type. Such a LAI disaggregation method was recently proposed by [83]. [PERSON] et al. [83] produced disaggregated global LAI maps available every 10 days for each vegetation type available from ECOCLIMAP-II over 1999-2015. Assimilating the disaggregated LAI could impact the analyzed LAI and other vegetation related variables, mainly evapotranspiration and GPP as emphasized in [83]. Also, LDAS-Monde analyses for the SSM variable could be improved by implementing an observation operator for the ASCAT radar (backscatter coefficients), instead of assimilating a retrieved soil moisture product. This would allow a direct assimilation of level 1 data and the use of all the information contained in these observations [84]. Based on these preliminary results, future work should focus on the use of ERA5 and on the improvement of LDAS-Monde through the direct assimilation of satellite-based soil moisture and vegetation characteristics from remote sensing. This could lead to possible applications, such as monitoring LSVs related to hydrometeorological and agrometeorological variables as well as the development of forecasting systems based on analyzed initial conditions. The ERA5 reanalysis is not only one high resolution atmospheric reanalysis (31 km), but also an ensemble reanalysis using 10 members available at 3-hourly intervals at a lower resolution (62 km). This ensemble not only provides a reanalysis, but also information on the uncertainty of the atmospheric reanalysis. Using this ensemble together with the whole available period (1979-present) would provide a long-term ensemble of LSV reanalyses driven by high quality meteorological data.