Data Assimilation for Improving Soil Moisture Estimation at Hillslope Scales: Experiments with Synthetic SMAP Radar Data
In a series of synthetic experiments we test the hypothesis that data assimilation algorithms can be employed to improve soil moisture estimation at spatial scales of hillslopes (e.g.100–102 m). We use the Ensemble Kalman Filter (EnKF) to update an ensemble of hillslope-scale soil moisture fields simulated by a physically-based ecohydrology model with synthetic SMAP radar observations. For sparse vegetation, assimilation of the synthetic observations substantially reduces estimation error in near-surface soil moisture (e.g. top 5 cm), relative to the synthetic true soil moisture conditions. Key components of our data assimilation system are: (1) explicit representation of the impact of hillslope-scale topography on microwave observation, and (2) a Latin Hypercube-based soil parameter generator that preserves the correlation between soil properties and improves the reproducibility of soil moisture ensemble statistics.
Flores, Alejandro N.; Entekhabi, Dara; and Bras, Rafael L.. (2012). "Data Assimilation for Improving Soil Moisture Estimation at Hillslope Scales: Experiments with Synthetic SMAP Radar Data". IAHS-AISH Publication, 352308-311.