A methodology for retrieving land surface properties from passive microwave observations is presented. Dual polarization microwave brightness temperature data, together with a simple radiative transfer model are used to derive surface soil moisture and vegetation optical depth simultaneously, in a non linear optimization procedure using a forward modeling approach. Soil temperature is derived off-line with a common heat flow model, driven by high frequency vertical polarization microwave data and remotely sensed observations of net radiation. The methodology does not require any field observations of soil moisture or canopy biophysical properties for calibration purposes and is independent of wavelength. Remote sensing provides an excellent opportunity to monitor and gather environmental data in regions that have little or no instrumentation. Moreover, microwave technology provides a more all-weather capability than is typically afforded with visible and near infrared wavelengths. The model was developed for regional- to global-scale monitoring and related environmental applications such as surface energy balance modelling, numerical weather prediction, flood and drought forecasting, and climate change studies. However, at higher spatial resolutions, which would be possible with aircraft, especially unmanned vehicles, tactical applications may be realized as well. Retrieval results compare well with field observations of soil moisture and satellite-derived vegetation index data from optical sensors.