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Abstract:
Data assimilation techniques allow for optimally merging remote sensing observations in ecohydrological models, guiding them for improving land surface flux predictions. Nowadays freely available remote sensing products, like those of Sentinel 1 radar, Landsat 8 and MODIS sensors, allow for monitoring land surface variables (e.g., soil moisture, the normalized difference vegetation index, NDVI, and land surface temperature, LST) at unprecedented high spatial and time resolutions, appropriate for heterogeneous ecosystems. An assimilation approach that assimilates radar backscatter (from Sentinel 1), grass and tree NDVI (from Landsat 8) and LST (from MODIS) observations in a coupled vegetation dynamic-land surface model is proposed. It is based on the Ensemble Kalman filter (EnKF), and it is not limited to assimilate remote sensing data for model predictions, but it uses assimilated data for dynamically updating key model parameters (the ENKFdc approach), the saturated hydraulic conductivity, and the grass and tree maintenance respiration coefficients, which are highly sensitive parameters of soil water balance and biomass budget models, respectively. The proposed EnKFdc assimilation approach facilitated good predictions of soil moisture and LAI in an heterogeneous ecosystem in Sardinia, for 5 years period with contrasting hydrometeorological (dry vs wet) conditions. The use of ENKFdc also enabled us to predict evapotranspiration and carbon flux well, with errors less than 4% and 15%, respectively, although the initial model conditions were extremely biased.
