Characterization of Footprint-Scale Surface Soil Moisture Distribution Observed during Soil Moisture Experiments in 2004 (SMEX04) Using Gaussian Mixture Model

HR: 0830h
AN: H41A-09 [Abstracts]
TI: Characterization of Footprint-Scale Surface Soil Moisture Distribution Observed during Soil Moisture Experiments in 2004 (SMEX04) Using Gaussian Mixture Model
AU: * Ryu, D
EM: dryu@uci.edu
AF: Department of Earth System Science, University of California, Irvine, CA 92697 United States
AU: Famiglietti, J S
EM: jfamigli@uci.edu
AF: Department of Earth System Science, University of California, Irvine, CA 92697 United States
AU: Bindlish, R
EM: bindlish@hydrolab.arsusda.gov
AF: USDA ARS Hydrology and Remote Sensing Lab, 104 Bldg. 007 BARC-West, Beltsville, MD 20705 United States
AU: Cosh, M H
EM: mcosh@hydrolab.arsusda.gov
AF: USDA ARS Hydrology and Remote Sensing Lab, 104 Bldg. 007 BARC-West, Beltsville, MD 20705 United States
AU: Jackson, T J
EM: tjackson@hydrolab.arsusda.gov
AF: USDA ARS Hydrology and Remote Sensing Lab, 104 Bldg. 007 BARC-West, Beltsville, MD 20705 United States
AB: During the Soil Moisture Experiments in 2004 (SMEX04) in Arizona, isolated precipitation events over the experiment site created patched spatial patterns of surface wetness, which in turn resulted in non-Gaussian distributions of surface soil moisture contents within some satellite footprint-scale fields. Mixtures of two Gaussian distributions are applied to fit the observed distributions of ground-based soil moisture data. Each footprint-scale field is stratified into two sub-regions in order to determine the distribution mixing weights using both the antecedent precipitation index and soil texture. The stratified patterns are compared with stratified high-resolution soil moisture maps from aircraft polarimetric scanning radiometer (PSR) observations. Bayesian information criteria (BIC) from fitting a single Gaussian distribution and fitting mixtures of two Gaussian densities are compared to test the optimality of the models, and uncertainties resulting from both models are compared.
DE: 1640 Remote sensing
DE: 1800 HYDROLOGY
DE: 1833 Hydroclimatology
DE: 1866 Soil moisture
SC: Hydrology [H]
MN: 2005 Joint Assembly