## 5 Conclusions  
In this study, LDAS-Monde sequential assimilation of satellite derived surface soil moisture and leaf area index, forced by ERA-5 latest atmospheric re-analysis, was applied to the CONtiguous US domain. Ref. [28] have highlighted the added value of using the ERA-5 atmospheric reanalysis to force the ISBA Land Surface Model over the CONtiguous US for the 2010-2016 period. They found that the use of ERA-5 instead of ERA-interim leads to significant improvements in the representation of the land surface variables linked to the terrestrial water cycle (e.g., surface soil moisture, river discharges, snow depth, and turbulent fluxes), but to a rather neutral impact on land surface variables linked to the vegetation cycle (e.g., evapotranspiration, carbon uptake, and leaf area index). Assimilating satellite derived observations linked to vegetation (LAI in this application) through LDAS-Monde forced by ERA-5 not only leads to a clear improvement in the representation of the vegetation cycle in ISBA, but brings further improvement on the representation of the terrestrial water cycle. The results have highlighted the stronger impact of LAI observations assimilation with respect to soil moisture assimilation. Other vegetation-related observations such as vegetation optical depth could be used, under specific circumstances, as a surrogate of LAI limiting the negative impact of the rather low temporal frequency of the LAI product. LDAS-Monde is a powerful tool to track the evolution of land surface variables and to monitor extreme events such as agricultural drought. Since LDAS-Monde analysis is more accurate than a simple model run, it can be used to initialise a forecast experiment of the land surface variables. Preliminary results suggest that its impact on forecast experiments, in particular with respect to vegetation, is positive and lasts in time. It opens the way towards applications from monitoring to forecasting land surface states. For that purpose, LDAS-Monde will be forced by other, higher spatial resolution, ECMWF atmospheric products like the high resolution forecast (HRES, current spatial resolution of \(\sim\)9 km), which also gives daily forecasts up to 10 days ahead and/or the ensemble forecast (ENS, current spatial resolution of \(\sim\)18 km), giving daily forecasts up to 15 days (46 days twice a week). [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; all the authors analysed the results, [PERSON] wrote the paper. **Funding:** This research received no external funding. Figure 12: (**a**) Correlation coefficient values between modelled LAI and C-band vegetation optical depth (VOD) over 2010–2016, (**b**) probability distribution of the correlation coefficient values over the same period. **Acknowledgments:** The authors would like to thank the Copernicus Global Land Service for providing the satellite-derived LAI products and the Vienna University of Technology (Vienna, Austria) for the Vegetation Optical Depth datasets. [PERSON] (Instituto Dom Luiz, IDL, Faculty of Sciences, University of Lisbon, Portugal) is thanked for his help processing the ERA-5 data. **Conflicts of Interest:** The authors declare no conflict of interest. **Code Availability:** LDAS-Monde is a part of the ISBA land surface model and is available as open source via the surface modelling platform called SURFEX. SURFEX can be downloaded freely a [[http://www.tumor-cnrm.fr/surtex/](http://www.tumor-cnrm.fr/surtex/)]([http://www.tumor-cnrm.fr/surtex/](http://www.tumor-cnrm.fr/surtex/)) using a CECIL-C Licence (a French equivalent to the L-GPL licence; [[http://www.ccelli.info/licenses/](http://www.ccelli.info/licenses/)]([http://www.ccelli.info/licenses/](http://www.ccelli.info/licenses/)) Licence_CecCILL-C_V1-en.txt). It is updated at a relatively low frequency (every 3 to 6 months).