Soil moisture is an important component of the water and energy balance of the Earth's surface, and as such, is essential to many Earth science disciplines. Regular and accurate estimates of soil moisture are an important input to the study of climate change, numerical weather prediction models, drought forecasts, and agricultural predictions. Soil moisture has been identified as a parameter of significant potential for improving the accuracy of large-scale land surface-atmosphere interaction models. However, accurate estimates of surface soil moisture are often difficult to make, especially at large spatial scales. Soil moisture is a highly variable land surface parameter, and while point measurements are usually accurate, they are typically representative only of the immediate sample location, and simple averaging of point values, in order to obtain large-area spatial means, often leads to substantial errors. In addition, ground sampling is labor intensive and costly, and consequently highly impractical for long-term and/or large-scale monitoring. Since satellite remote sensing observations are already a spatially averaged value, they are ideally suited for easuring land surface parameters such as soil moisture. Passive microwave remote sensing presents the greatest potential for providing regular spatially representative estimates of surface soil moisture at global scales. But, while the optimum wavelength for soil moisture sensing is in the L-band (1.4 GHz or λ = 21 cm), such a sensor has yet to be deployed operationally. However, new and highly improved microwave retrieval techniques are being developed that maximize the information that can be obtained from less optimum sensors, such as C-band and even X-band. Progress from one such study is presented, along with preliminary results of some validation studies and plans to develop a 20-year retrospective global database of surface soil moisture. This data product will be made available to the general public through the Goddard Space Flight Center Distributed Active Archive Center (DAAC). Improved real-time estimates of surface soil moisture should greatly improve the performance of real-time models, while the development of historical data sets will provide necessary information for simulation and validation of long-term climate and global change studies.