Results of previous soil moisture studies show scale dependence. Most of these studies are specific to a particular field, landscape, watershed or region and provide only a limited understanding of the fundamental geophysical processes contributing to soil moisture dynamics. Using data from three selected watersheds (semi-humid Oklahoma, humid Iowa, and semi-arid Arizona), we used a stream-tube approach to simulate soil water processes at the fine scale. In stream-tube approach, the horizontal spatial heterogeneity was represented by an ensemble of soil hydraulic parameters and was conceptualized as a bundle of independent parallel soil columns. A Markov Chain Monte Carlo (MCMC) algorithm was developed to derive upscaled land surface parameters for the Soil-Water-Atmosphere-Plant (SWAP) model using time series data of AMSR-E based soil moisture measurements. The behavior of profile soil moisture probability density functions (PDFs) from the satellite footprint scale (~60 km) to subsequent finer scales (~25 km, ~8 km) are described for different hydroclimatic regions. For all scales and different hydroclimatic regions, residual water content exhibited unimodal Gaussian distribution. However, saturated water content distribution showed multimodality, which apparently results from inherent sub-grid scale variability. Similarly, saturated hydraulic conductivity at all scales also showed multimodality. Gaussian PDFs of profile soil moisture demonstrate typical region-specific and scale effect on the mean and variability. With decreasing scale, profile soil moisture PDFs for all three regions showed distinct right skewed behavior and large variability. High variability is attributed to the multimodal saturated water content distribution. The narrow soil moisture PDFs at the AMSR-E footprint scale of ~60 km is due to spatial smoothing effects where decrease in natural variability of surface characteristics is observed with increasing scale. Estimation of effective soil hydraulic parameters using remotely sensed soil moisture data opens up new avenues for better accounting of water and energy cycle on the earth surface.