Likelihood parameter estimation for calibrating a soil moisture model using radar bakscatter

a r t i c l e i n f o Land surface model parameter estimation can be performed using soil moisture information provided by synthetic aperture radar imagery. The presence of speckle necessitates aggregating backscatter measurements over large (N 100 m × 100 m) land areas in order to derive reliable soil moisture information from imagery, and a model calibrated to such aggregated information can only provide 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. The method is demonstrated using the NOAH land surface model and Advanced Integral Equation Method (AIEM) 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 × 100 m NOAH and AIEM 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 10% volumetric water content by the same models calibrated directly to soil moisture information derived from synthetic aperture radar imagery at the same scales. Results suggest that much of this improvement is due to increased ability to simultaneously estimate NOAH parameters and AIEM surface roughness parameters. A standardized target set of accuracy and precision requirements governing the reporting of soil moisture distribution for watershed-scale applications is outlined by Moran et al. (2004). Such information would optimally be available at a fine spatial resolution of between 10 and 100 m, with coverage areas at the watershed scale or larger. Because many management decisions are made on a day-today basis, product delivery requirements are stringent and delivery would optimally be available immediately upon request. Difficulties arise in using field-based measurements of soil moisture to meet these requirements since soil water distributions are highly heterogeneous in nature (Mohanty et al., 2002) and field measurements offer only single-point information. Alternatively, satellite-based synthetic aperture radar (SAR) observation systems can be used to make soil moisture measurements continuously over large areas; radar can penetrate the land surface to a depth dependent on wavelength, and SAR imagery can …