## 4 Conclusions  
A Land Data Assimilation System was developed in Hungary to monitor the above-ground biomass, surface fluxes (carbon and water), and the associated root-zone soil moisture at the regional scale in quasi-real time. It was based on the SURFX model using the ISBA-A-gs photosynthesis scheme to describe the evolution of vegetation. Our study focused on the ability of the model to describe the inhomogeneity of the ecosystems. Firstly, SURFEX was run in an open-loop mode (i.e., without assimilation) for the period of 2008-2015. Secondly, the Extended Kalman Filter method was used to assimilate SPOT /VGT and PROBA-V LAI and ASCAT/Metop SWI satellite observations. The use of different satellite data at different resolutions challenges for the construction of the EKF observation operator. It was demonstrated in this study that the CDF tool can provide important, meaningful information that makes the observations more suitable for the assimilation. The comparison generally showed a good agreement between the satellite data and the simulated values for LAI and soil moisture. Both simulations were able to reproduce the variability of the amount of biomass and the soil moisture in different weather situations. The assimilation was able to correct some shortcomings of the open-loop LSM run. It was observed that the maximum of the growing phase was delayed by the simulations, probably because ISBA-A-gs underestimates the photosynthesis in spring. These discrepancies may be partly responsible for a delay of a month in the LAI seasonal cycle. The assimilation significantly reduced this delay and increases the correlation between the model and satellite GEOV1 LAI by more than 0.2-0.3. Due to the high BIAS and RMSE of LAI and WG2, their inter-annual variability was also examined through their scaled anomalies. They were also very well represented by the simulations, especially in the dry summer of 2012. The scaled anomaly can be used as a drought indicator for a period of interest. A regional LDAS system can be used operationally at HMS, and is being developed as follows:  
* The results of the assimilation depend on the quality of the data to be assimilated, although the assimilation efficiently corrected LAI high RMSE and BIAS values remained. By applying a finer resolution and assimilating more advanced products of LAI (provided by Sentinel-3/OLCI and PROBA-V) and SWI (provided by Sentinel-1 C-SAR and Metop ASCAT), more precise results can be obtained [47; 48]. New types of satellite information can be included in the assimilation system, such as FAPAR, surface albedo, or VOD (Vertical Optical Depth) in the future. * In this study, the three layers ISBA-A-gs force restore model were used. This means that a single, thick root-zone layer represents soil hydrology, causing a slow response to dry or wet conditions. The propagation of surface soil moisture in the deeper layers may be affected by the lack of vertical resolution of the model. Increasing the number of soil layers allows a more accurate representation of the vertical distribution of soil moisture and the vegetation response to the water stress [1; 18; 19]. Therefore, the multi-layer version of soil model (diffusion version, ISBA-DIF) is expected to improve the system. * It is planned that the operational non-hydrostatic AROME NWP forecasts will be corrected by using updated LAI instead of the climatic values. The updated LAI will be provided by daily assimilation of satellite LAI (Sentinel-3). Thus, LDAS should be extended to an AROME Lambert grid with finer 2.5 km resolution. * The main advantage of LDAS is that it provides a consistent state (analysis) of vegetation and soil variables (moisture and temperature) for a given location and time. This analysis can be used as a starting point for the prediction component of the system, thus implementing a Land Monitoring and Forecasting System.