###### Abstract
It was shown that the assimilation worked effectively, but the impact of the assimilation on the vegetation phenology and the water and carbon fluxes varied from season to season. Besides, the global LDAS-Monde model can detect, monitor, and forecast extreme weather, such as drought, or heat-wave events, which were performed by computing anomalies. A wide range of satellite-based biophysical products are provided by the Copernicus Land Service, which had been built in the framework of the FP7 Geoland2 project ([[http://www.gmes-geoland.info](http://www.gmes-geoland.info)]([http://www.gmes-geoland.info](http://www.gmes-geoland.info)), accessed on 21 July 2021). The developments were continued in the ImagineS (Implementation of Multi-scale AGricultural INdictators Exploiting Sentinels) project to support the operations of the global component of the Copernicus Land service, preparing for the use of the Sentinel data in an operational context ([[http://fp7-imagines.eu/](http://fp7-imagines.eu/)]([http://fp7-imagines.eu/](http://fp7-imagines.eu/)), accessed on 21 July 2021). Both projects aimed to organise a qualified production network, to set up user-driven biophysical products describing the vegetation and the energy and water budgets. Within the framework of these projects, several studies demonstrated the added values of regional or global LDAS, to monitor crop/fodder production together with carbon and water fluxes [7; 9]. The aim of the Hungarian Meteorological Service in both projects was to adopt, develop and validate the regional LDAS and quasi-real-time modeling of biomass, soil moisture, natural carbon dioxide, and water vapor fluxes for the territory of Hungary, using satellite measurements. Vegetation monitoring is an important task in Hungary. As the inter-annual variability of the vegetation is quite high in Hungary (considering its dry continental climate), and since the proportion of irrigated areas is rather small (1-2%, [[https://data.worldbank.org/indicator/AG.LND.IRIG.AG.ZS?locations=HU](https://data.worldbank.org/indicator/AG.LND.IRIG.AG.ZS?locations=HU)]([https://data.worldbank.org/indicator/AG.LND.IRIG.AG.ZS?locations=HU](https://data.worldbank.org/indicator/AG.LND.IRIG.AG.ZS?locations=HU)), accessed on 21 July 2021), the drought exposure of agriculture is very high. In addition, agriculture accounts for a significant share of the national economy. Therefore, it is important to estimate the evolution of the vegetation using numerical methods (e.g., LDAS). The main objective of this study is to assess the performance of the system established in Geoland2 and ImagineS projects over the Carpathian Basin, i.e., how SURFEX can provide the above-ground biomass, soil moisture, water and carbon fluxes, and how LDAS captures the benefits of the assimilation of the remote sensing observations and improves the biomass as well as the soil moisture response under different climatic conditions. In this paper, evaluations of the model runs against satellite data, and in-situ measurements are shown. The paper is structured in the following way. In Section 2 the SURFEX model and the ISBA-A-gs scheme are introduced, and then the applied data and assimilation methods are described. The seasonal-based CDF technique is also explained. In Section 3, the results are presented: the impact of assimilation and the seasonal cycle of LAI, soil moisture, carbon and water fluxes, the inter-annual variability of LAI and soil moisture through the drought monitoring. Finally, the main conclusions and outlooks are summarised in Section 4.