Submitted to: Water Resources Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/25/2008
Publication Date: 8/14/2008
Citation: Choi, M., Jacobs, J.M., Bosch, D.D. 2008. Remote Sensing Observatory Validation of Surface Soil Moisture Using Advanced Microwave Scanning Radiometer E, Common Land Model, and Ground Based Data: Case Study in SMEX03 Little River Region, Georgia, U.S.. Water Resources Research. 44:WO8421, doi:10,1029/2006WR005578. Interpretive Summary: Soil moisture content is a critical soil property which can affect many geophysical processes in the environment including flooding, drought, climate, and plant/soil interactions. Knowledge of the amount of water existing in the soil is critical to understanding many fundamental environmental processes. Soil moisture measurements interpreted from satellite collected data as well as estimates from a large-scale computer simulation model were compared to data collected from a ground-based network in South-central Georgia. The comparison indicated the simulation model provided estimates of ground-based soil moisture within reasonable error ranges and accurately predicted the extremes of the observed soil moisture. While the satellite measurements accurately represented soil moisture trends, they did not estimate the full range of the observed soil moisture or its' true variability. This study indicates that the satellite estimates and the computer simulation model may be useful tools for estimating soil moisture in regions where ground-based data are not available.
Technical Abstract: Optimal soil moisture estimation may be characterized by inter-comparisons among remotely sensed measurements, ground-based measurements, and land surface models. In this study, we compared soil moisture from Advanced Microwave Scanning Radiometer E (AMSR-E), ground-based measurements, and Soil-Vegetation-Atmosphere Transfer (SVAT) model (Common Land Model) at SMEX03 Little River region, GA. The comparison results showed that there is a good agreement among different soil moisture products for short and long periods (i.e., highest Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were 0.054 and 0.059 [m3/m3], respectively). The Common Land Model reasonably replicated soil moisture patterns in dry down and wetting after rainfall though it had modest wet biases as compared to AMSR-E and ground data. While the AMSR-E average soil moisture agreed well with the other data sources, it had extremely low temporal variability, especially during the growing season from May to October. Overall, both Common Land Model and AMSR-E had complementary strengths, low MAE and RMSE errors for CLM and low biases for AMSR-E.