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In this study, a global land data assimilation system (LDAS-Monde) is applied\udover Europe and the Mediterranean basin to increase monitoring accuracy for\udland surface variables. LDAS-Monde is able to ingest information from\udsatellite-derived surface soil moisture (SSM) and leaf area index (LAI)\udobservations to constrain the interactions between\udsoil–biosphere–atmosphere (ISBA, Interactions between Soil, Biosphere and Atmosphere) land\udsurface model (LSM) coupled with the CNRM (Centre National de Recherches\udMétéorologiques) version of the Total Runoff Integrating Pathways\ud(ISBA-CTRIP) continental hydrological system. It makes use of the\udCO<sub>2</sub>-responsive version of ISBA which models leaf-scale physiological\udprocesses and plant growth. Transfer of water and heat in the soil rely on\uda multilayer diffusion scheme. SSM and LAI observations are assimilated using\uda simplified extended Kalman filter (SEKF), which uses finite differences\udfrom perturbed simulations to generate flow dependence between the\udobservations and the model control variables. The latter include LAI and\udseven layers of soil (from 1 to 100 cm depth). A sensitivity test of the\udJacobians over 2000–2012 exhibits effects related to both depth and season.\udIt also suggests that observations of both LAI and SSM have an impact on the\uddifferent control variables. From the assimilation of SSM, the LDAS is more\udeffective in modifying soil moisture (SM) from the top layers of soil, as\udmodel sensitivity to SSM decreases with depth and has almost no impact from\ud60 cm downwards. From the assimilation of LAI, a strong impact on LAI\uditself is found. The LAI assimilation impact is more pronounced in SM layers\udthat contain the highest fraction of roots (from 10 to 60 cm). The\udassimilation is more efficient in summer and autumn than in winter and\udspring. Results shows that the LDAS works well constraining the model to the\udobservations and that stronger corrections are applied to LAI than to SM.\udA comprehensive evaluation of the assimilation impact is conducted using\ud(i) agricultural statistics over France, (ii) river discharge observations,\ud(iii) satellite-derived estimates of land evapotranspiration from the Global\udLand Evaporation Amsterdam Model (GLEAM) project and (iv) spatially gridded\udobservation-based estimates of upscaled gross primary production and\udevapotranspiration from the FLUXNET network. Comparisons with those four\uddatasets highlight neutral to highly positive improvement

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Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area