Information about the distribution of surface soil moisture can greatly benefit the management of agriculture and natural resource. However, direct measurement of soil moisture over larger areas can be impractical and expensive, which has led scientists to develop satellite based remote sensing techniques for soil moisture assessments. Retrieving soil moisture from radar satellite imagery often associated with the collection and use of ancillary field data on surface roughness. However, field data that is meant to characterize surface roughness is often unreliable, is expensive to collect and is nearly impossible to acquire for large scale applications. These issues represent barriers to the adoption and of radar data for mapping soil moisture over large areas.The research presented in the dissertation is aimed at the development of an operational soil moisture assessment system based solely on radar satellite data and a radar model, eliminating the field data requirements altogether. The research is directed towards a so-called equation-based solution of the problem as an alternative to the approach that requires the use of extensive field-data sets on surface roughness. This approach is based on the concept that if the number of equations are equal to the number of unknowns, then explicit solutions of all unknowns are possible. My research derived the necessary equations to solve for soil moisture and surface roughness. The derivation of the equations and how to use them to estimate soil moisture without using ancillary field data was demonstrated by my research. Validation results showed that the equation-based method that was developed is capable of providing more precise estimates of surface soil moisture than that of ancillary field-data supported method.