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Geosciences 2016, 6(2), 19;

Sequential Ensembles Tolerant to Synthetic Aperture Radar (SAR) Soil Moisture Retrieval Errors

Environmental Engineering, Politecnico di Milano, Leonardo da Vinci 32, Milano 20133, Italy
Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede 7500AE, The Netherlands
Academic Editors: Kevin Tansey, Ruiliang Pu and Jesus Martinez-Frias
Received: 11 January 2016 / Revised: 17 March 2016 / Accepted: 19 March 2016 / Published: 6 April 2016
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Due to complicated and undefined systematic errors in satellite observation, data assimilation integrating model states with satellite observations is more complicated than field measurements-based data assimilation at a local scale. In the case of Synthetic Aperture Radar (SAR) soil moisture, the systematic errors arising from uncertainties in roughness conditions are significant and unavoidable, but current satellite bias correction methods do not resolve the problems very well. Thus, apart from the bias correction process of satellite observation, it is important to assess the inherent capability of satellite data assimilation in such sub-optimal but more realistic observational error conditions. To this end, time-evolving sequential ensembles of the Ensemble Kalman Filter (EnKF) is compared with stationary ensemble of the Ensemble Optimal Interpolation (EnOI) scheme that does not evolve the ensembles over time. As the sensitivity analysis demonstrated that the surface roughness is more sensitive to the SAR retrievals than measurement errors, it is a scope of this study to monitor how data assimilation alters the effects of roughness on SAR soil moisture retrievals. In results, two data assimilation schemes all provided intermediate values between SAR overestimation, and model underestimation. However, under the same SAR observational error conditions, the sequential ensembles approached a calibrated model showing the lowest Root Mean Square Error (RMSE), while the stationary ensemble converged towards the SAR observations exhibiting the highest RMSE. As compared to stationary ensembles, sequential ensembles have a better tolerance to SAR retrieval errors. Such inherent nature of EnKF suggests an operational merit as a satellite data assimilation system, due to the limitation of bias correction methods currently available. View Full-Text
Keywords: Ensemble Kalman Filter (EnKF); satellite data assimilation; ensemble evolution; SAR soil moisture Ensemble Kalman Filter (EnKF); satellite data assimilation; ensemble evolution; SAR soil moisture

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Lee, J.H. Sequential Ensembles Tolerant to Synthetic Aperture Radar (SAR) Soil Moisture Retrieval Errors. Geosciences 2016, 6, 19.

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