###### Abstract  
In this study, a Land Data Assimilation System (LDAS) is applied over the Carpathian Basin at the Hungarian Meteorological Service to monitor the above-ground biomass, surface fluxes (carbon and water), and the associated root-zone soil moisture at the regional scale (spatial resolution of 8 km \(\times\) 8 km) in quasi-real-time. In this system the SURFEX model is used, which applies the vegetation growth version of the Interactions between Soil, Biosphere and Atmosphere (ISBA-A-gs) photosynthesis scheme to describe the evolution of vegetation. SURFEX is forced using the outputs of the ALADIN numerical weather prediction model run operationally at the Hungarian Meteorological Service. First, SURFEX is run in an open-loop (i.e., no assimilation) mode for the period 2008-2015. Secondly, the Extended Kalman Filter (EKF) method is used to assimilate Leaf Area Index (LAI) Spot/Vegetation (until May 2014) and PROBA-V (from June 2014) and Soil Water Index (SWI) ASCAT/Metop satellite measurements. The benefit of LDAS is proved over the whole country and to a selected site in West Hungary (Hegyhatsai). It is demonstrated that the EKF can provide useful information both in wet and dry seasons as well. It is shown that the data assimilation is efficient to describe the inter-annual variability of biomass and soil moisture values. The vegetation development and the water and carbon fluxes vary from season to season and LDAS is a capable tool to monitor the variability of these parameters. land surface modeling; data assimilation; satellite data; LAI; SWI +
Footnote †: : 2021 July 2021  
parameters and the soil moisture [3; 4]. An extended Kalman Filter (EKF) allows assimilation of both conventional (screen-level) and non-conventional (satellite) observations to provide surface analysis. Furthermore, to exploit the advantage of the satellite data, EKF, 2D-Var [5], and Ensemble Kalman Filter [6] methods were investigated to generate soil moisture analysis. Since few in-situ measurements are available and they are not representative, it is important to know how to assimilate satellite data. Several studies have discussed the possibility of the assimilation of remote sensing observations into LSMs [7; 8; 9; 10; 11; 12; 13] and in NWP models [14; 15; 16; 17]. Combining Earth observations (EOs) and LSMs through land data assimilation systems (LDAS) can improve the initial land surface conditions, this can benefit the weather forecasts, including temperature and precipitation predictions [18; 19; 20]. Various articles have assessed different versions of EKF to assimilate either satellite measurements for soil moisture [10; 11; 12], LAI [13], or both [1; 2; 3; 4; 5; 6; 7], in LSMs; [11] demonstrated useful increments generated from AMSR-E (Advanced Microwave Scanning Radiometer) soil moisture remote sense observations in the total soil layer. Besides, EKF and the simplified version of EKF were compared, in which the background errors were kept constant. Despite the fact that they produced similar soil analyses, EKF was recommended for future work. It was also presented that it is necessary to remove the seasonal bias between the satellite observation and the model soil moisture by using a Cumulative Distribution Function (CDF) matching technique. The SURFace EXternalisee (SURFEX) LSM model applies Interaction between Soil, Biosphere, and Atmosphere (ISBA), a scheme which simulates these processes [21]. It is an externalised surface model capable of being coupled to any atmospheric numerical weather prediction (NWP) model. The benefit of joint assimilation of SSM and LAI by using the multi-patch, multi-layer diffusion version of SURFEX ISBA model (LDAS-Monde), was pointed out in [9; 22]. The evolution of the above-ground biomass changes in a lagged response to the modified soil moisture conditions. The model sensitivity to the observed SSM decreases from the top to the deep layers and there was almost no impact from 60 cm downwards, and the assimilation is more efficient in summer and autumn than in winter and spring.