Soil hydraulic properties (hydraulic conductivity, water retention) are by far the most important land surface parameters to govern the partitioning of soil moisture between infiltration and evaporation fluxes at a range of spatial scales. Proper evaluation of the water balance near the land-atmosphere boundary depends strongly on appropriate characterization of soil hydraulic parameters under field conditions and at the appropriate process scale. In recent years we have adopted a multi-facet approach to this problem including: (1) a bottom-up approach, where larger-scale effective parameters are calculated by aggregating point-scale insitu hydraulic property measurements, (2) a top-down approach, where effective soil hydraulic parameters are estimated by inverse modeling using remotely sensed soil moisture measurements, and (3) an artificial neural network approach, where effective soil hydraulic parameters were estimated by exploiting the correlations with soil texture, topographic attributes, and vegetation characteristics at multiple spatial resolutions. Summary of several numerical and experimental results using these various effective soil hydraulic parameter estimation approaches including some comparisons between the approaches will be presented for different hydro-climatic regions with space- and air-borne remote sensing and other ground based platforms.