Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 14, 2010
Publication Date: September 1, 2010
Citation: Nearing, G.S., Moran, M.S., Thorp, K.R., Holifield Collins, C.D., Slack, D.C. 2010. Likelihood parameter estimation for calibrating a soil moisture using radar backscatter. Remote Sensing of Environment. 114: 2564-2574. Interpretive Summary: Mapping soil moisture over large areas is important for a variety of applications including fire, flood, and drought prediction, weather forecasting, and crop yield prediction, and a high resolution product is desirable for watershed-scale applications. Continuous and distributed soil moisture estimates can be obtained using a land surface hydrology model calibrated to soil moisture states observed by orbital radar remote sensing devices. Radar remote sensing estimates of soil moisture are corrupted by speckle which must be averaged out over large land areas, thus adversely affecting the spatial resolution of the information product. Here we develop and demonstrate a method for probabilistically accounting for speckle while simultaneously calibrating the land surface model in a way which derives a more robust information signal from the remote sensing data, thereby improving the spatial resolution of the model/image soil moisture estimation product.
Technical Abstract: Assimilating soil moisture information contained in synthetic aperture radar imagery into land surface model predictions can be done using a calibration, or parameter estimation, approach. The presence of speckle, however, necessitates aggregating backscatter measurements over large land areas in order to derive reliable soil moisture information from imagery, and a model calibrated to such aggregated information can only provide temporally continuous estimates of soil moisture at spatial resolutions required for reliable speckle accounting. A method utilizing the likelihood formulation of a probabilistic speckle model as the calibration objective function is proposed which will allow for calibrating land surface models directly to radar backscatter intensity measurements in a way which simultaneously accounts for model parameter- and speckle-induced uncertainty. Assimilation is demonstrated using the NOAH land surface model and Integral Equation Method (IEM) backscatter model calibrated to SAR imagery of an area in the Southwestern United States, and validated against in situ soil moisture measurements. At spatial resolutions finer than 100 m by 100 m square NOAH and IEM calibrated using the proposed radar intensity likelihood parameter estimation algorithm predict surface level soil moisture to within 4% volumetric water content 95% of the time, which is an improvement over a 95% prediction confidence of 7% volumetric water content by the same models calibrated directly to soil moisture information derived from synthetic aperture radar imagery at the same scales. Results demonstrate that the proposed algorithm consistently improves calibrated-model estimates of surface level soil moisture at fine spatial resolutions as compared to estimates by models calibrated to a priori-derived image information.