| Remotely sensed radiobrightness temperature retrievals constitute a recently available and vital resource for validation of radiation transfer models, as well as to derive fields of volumetric soil moisture content for the top 5 centimeters of soil. Soil moisture has a significant impact on processes occurring at a wide range of scales, such as ecosystems, hydrologic, and land-atmosphere dynamics. It is also a key determinant in the partitioning of incoming radiation into latent, sensible, and ground heat fluxes, which must be considered for the joint modeling of water and energy budgets from the canopy to global scales. With the advent of remotely sensed radiobrightness temperature or near-surface soil moisture imagery providing spatial soil moisture information covering the entire globe, we are faced with an unprecedented opportunity to investigate the intrinsic spatial properties of soil moisture fields as well as to constrain and improve the computational model predictions for energy and water fluxes at the earth surface based on the remotely sensed information.; This dissertation is concerned with the stochastic characterization and modeling of the spatial attributes and dependence of L band microwave radiobrightness temperature and the derived near-surface soil moisture imagery retrieved from Electronically Scanned Thinned Array Radiometer (ESTAR) during the Southern Great Plains Hydrology Experiment of 1997 (SGP97). Chapter 2 initiates our research endeavors by evidencing that scale invariance of statistical moments appears to hold for the remote sensing imagery of interest from 0.8 km to 12.8 km, as reported in previous studies. This empirical finding serves as the basis for our three core research contributions. (1) In chapter 3, we introduce novel approach for modeling the spatial structure and evidenced scaling properties of radiobrightness temperature and near-surface soil moisture fields by extending the autoregressive fractionally integrated moving average (ARFIMA) time series paradigm to two dimensions. (2) Chapter 4 presents a general downscaling framework for long memory 1/f processes, which is based on the statistical characterization of scale-invariant properties of the wavelet coefficients or fluctuations from long memory 1/f processes. (3) Finally, chapters 5 and 6 propose and validate a multiscale framework for assimilation of radiobrightness temperature or near-surface soil water content into land surface models. (Abstract shortened by UMI.)... |
