Data assimilation techniques allow researchers to optimally merge remote sensing observations in ecohydrological models, guiding them for improving land surface fluxes predictions. Presently, freely available remote sensing products, such as those of Sentinel 1 radar, Landsat 8 sensors, and Sentinel 2 sensors, allow the monitoring of land surface variables (e.g., radar backscatter for soil moisture and the normalized difference vegetation index (NDVI) and for leaf area index (LAI)) at unprecedentedly high spatial and time resolutions, appropriate for heterogeneous ecosystems, typical of semiarid ecosystems characterized by contrasting vegetation components (grass and trees) competing for water use. A multiscale assimilation approach that assimilates radar backscatter and grass and tree NDVI 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 assimilating remote sensing data for model predictions, but it uses assimilated data for dynamically updating key model parameters (the ENKFdc approach), including saturated hydraulic conductivity and 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, grass, and tree LAI in a heterogeneous ecosystem in Sardinia for a 3-year period with contrasting hydrometeorological (dry vs. wet) conditions. Contrary to the EnKF-based approach, the proposed EnKFdc approach performed well for the full range of hydrometeorological conditions and parameters, even assuming extremely biased model conditions with very high or low parameter values compared with the calibrated (“true”) values. The EnKFdc approach is crucial for soil moisture and LAI predictions in winter and spring, key seasons for water resources management in Mediterranean water-limited ecosystems. The use of ENKFdc also enabled us to predict evapotranspiration and carbon flux well, with errors of less than 4% and 15%, respectively; such results were obtained even with extremely biased initial model conditions.
<p>In the presence of uncertain initial conditions and model parameters coupled land surface model (LSM)- vegetation dynamic model (VDM) performance can be significantly improved by the assimilation of periodic observations of certain state variables, such as the soil moisture and normalized difference vegetation index (NDVI) as observed from satellite remote platforms.</p><p>The possibility to merge grass and tree NDVI observations and radar data with the model optimally for providing robust predictions of soil moisture and grass and tree leaf area index (LAI) in heterogenous ecosystems is demonstrated. We propose an assimilation approach that assimilates backscatter data from radar and NDVI from optical sensors through the Ensemble Kalman filter (EnKF) and provides a physics-based update of soil moisture and grass and tree LAI predicted by VDM. We used Sentinel 1 radar data for soil moisture, and Landsat 8 and Sentinel 2 optical data for NDVI.</p><p>This approach, as with other common assimilation approaches, may fail when key model parameters, e.g. the saturated hydraulic conductivity of LSM and the maintenance respiration coefficient (m<sub>a</sub>) of VDM, are estimated poorly. This leads to biased model errors producing a violation of a main assumption (model errors with zero mean) of the EnKF. For overcoming this model bias an innovative assimilation approach was developed, which accepts this violation in the early model run-times and dynamically calibrates all the components of the model parameter ensembles as a function of the persistent bias in root zone soil moisture and LAI, allowing to remove the model bias, restore the fidelity to the EnKF requirements and reduce the model uncertainty.</p><p>The proposed multiscale assimilation approach was tested in a Sardinian field site, a typical Mediterranean ecosystem characterized by strong heterogeneity of the vegetation and water limited conditions. The site is also a case study of the SWATCH European Research Project, and in this field a micrometeorological eddy-covariance based tower is operating from 2003.</p><p>The effective impact of the proposed assimilation approach on the soil water budget, evapotranspiration and CO<sub>2</sub> uptake predictions in the heterogenous ecosystem is demonstrated.</p>
<p><em><span lang="EN-US">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 Sentinel 2 sensors, allow for monitoring land surface variables (e.g., radar backscatter for soil moisture and the normalized difference vegetation index, NDVI, for leaf area index, LAI) at unprecedented high spatial and time resolutions, appropriate for heterogeneous ecosystems, typical of semi-arid ecosystems characterized by contrasting vegetation components (grass and trees) competing for water use. An assimilation approach that assimilates radar backscatter and grass and tree NDVI 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 in an heterogeneous ecosystem in Sardinia, for 5 years period with contrasting hydrometeorological (dry vs wet) conditions. Contrary to the EnKF-based approach, the proposed EnKFdc approach performed well for the full range of hydrometeorological conditions and parameters, even assuming extremely biased model conditions with very high or low parameter values compared to the calibrated (&#8220;true&#8221;) values. The EnKFdc approach is crucial for soil moisture and LAI predictions in winter and spring, key seasons for water resources management in Mediterranean water-limited ecosystems. 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.</span></em></p>
<p>In the presence of uncertain initial conditions and model parameters coupled land surface model (LSM)- vegetation dynamic model (VDM) performance can be significantly improved by the assimilation of periodic observations of certain state variables, such as the soil moisture and normalized difference vegetation index (NDVI) as observed from satellite remote platforms.</p><p>The possibility to merge grass and tree NDVI observations and radar data with the model optimally for providing robust predictions of soil moisture and grass and tree leaf area index (LAI) in heterogenous ecosystems is demonstrated. We propose an assimilation approach that assimilates backscatter data from radar and NDVI from optical sensors through the Ensemble Kalman filter (EnKF) and provides a physics-based update of soil moisture and grass and tree LAI predicted by VDM. We used Sentinel 1 radar data for soil moisture, and Landsat 8 and Sentinel 2 optical data for NDVI. Soil moisture is predicted by the LSM, while the VDM predicts the LAI, which is strictly related to NDVI, through a field-estimated empirical relationship.</p><p>This approach, as with other common assimilation approaches, may fail when key model parameters, e.g. the saturated hydraulic conductivity of LSM and the maintenance respiration coefficient (m<sub>a</sub>) of VDM, are estimated poorly. This leads to biased model errors producing a violation of a main assumption (model errors with zero mean) of the EnKF. For overcoming this model bias an innovative assimilation approach was developed, which accepts this violation in the early model run-times and dynamically calibrates all the components of the model parameter ensembles as a function of the persistent bias in soil moisture and LAI predictions, allowing to remove the model bias, restore the fidelity to the EnKF requirements and reduce the model uncertainty.</p><p>The proposed multiscale assimilation approach was tested in a Sardinian field site, a typical Mediterranean ecosystem characterized by strong heterogeneity of the vegetation and water limited conditions. The site is also a case study of the SWATCH European Research Project, and in this field a micrometeorological eddy-covariance based tower is operating from 2003.</p><p>The positive impact of the proposed assimilation approach on the soil water budget, evapotranspiration and CO<sub>2</sub> uptake predictions in the heterogenous ecosystem is demonstrated finally.&#160;</p>
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