Evaluating Bias-Corrected AMSR-E Soil Moisture Using in Situ Observations and Model Estimates

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Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) soil moisture data products are proven to be useful across various regions around the world. However, numerous studies have suggested that validation and bias-correction at the local scale is important to use them with confidence for numerical weather prediction, land surface energy, and water balance assessment, including infiltration and drainage. Here we investigate the cumulative distribution function (CDF) to correct AMSR-E surface soil moisture data using field observations of soil moisture for 37 sites from Nebraska’s Automated Weather Data Network (AWDN) and model-simulated soil moisture from southwestern Idaho. We explore the scaling of AMSR-E data using simulated soil moisture from three hydrological models, Variable Infiltration Capacity (VIC), Noah Land Surface Model (Noah LSM) and Robinson Hubbard 1-D (RH1D) models. We hypothesize that these calibrated hydrological models can substitute for field observations to represent continuous surface of soil moisture fields over space and time when used in conjunction with daily AMSR-E data. Our results suggest that it is necessary to have the AMSR-E data bias-corrected based on either field observations or model estimates. The magnitude of values for corrected AMSR-E soil moisture and observed soil moisture showed better correlation for the growing seasons between 2003 and 2005. It is also shown that well-calibrated hydrological models can be useful to provide correction for the AMSR-E product thereby adding value to the AMSR-E soil moisture datasets.