Abstract:
A 3-D Ensemble Kalman Filter (EnKF) technique is used to assimilate the current coarse scale Advanced Microwave Scanning Radiometer-Earth observing System (AMSR-E) satellite derived soil moisture data into the Noah land surface model to estimate the fine scale surface soil moisture over the Little River Experimental Watershed (LREW), Georgia, USA. We derived the AMSR-E satellite soil moisture from the AMSR-E observed brightness temperature at 10.7 GHz frequency channel with Horizontal polarization using a single frequency single polarization emission model (Land surface Microwave Emission Model (LSMEM)), prior to assimilating them into the Noah land surface model. It is found that the Ensemble Kalman Filter assimilation algorithm improved the fine scale soil moisture estimation compared to the model simulated soil moisture without data assimilation, when we validated the results with the local point observations. The 3-D filter allows retaining fine scale soil moisture variability and properly accounts for the scale mismatch between observations and model predictions, as opposed to 1-D approaches which may suffer from spatial scale depending bias. The assimilated soil moisture affects the model estimate of other water and energy budget variables.