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In this paper, we study the radar retrieval of soil moisture as well as canopy parameters in a range of boreal forests. The retrieval is formulated as an optimization problem where the difference between data and prediction of a forward scattering model is minimized. The forward model is a discrete scatterer radar model, and the optimization algorithm is a global optimization scheme known as simulated annealing. The inversion method is first applied to synthetic data assuming hypothetical allometric relationships to make the retrieval possible by reducing the number of unknown vegetation parameters. The inversion algorithm is then validated using the data acquired with the National Aeronautics and Space Administration (NASA)/Jet Propulsion Laboratory (JPL) Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) in June 2010 in central Canada boreal forests in support of the prelaunch calibration and validation activities of NASA's Soil Moisture Active and Passive (SMAP) mission. The inversion results for synthetic data show that the absolute retrieval error in soil moisture and relative retrieval error in canopy height are small, while the relative output error in trunk density could be large. The inversion results for actual field data show a great accuracy in soil moisture retrieval for Old Jack Pine and Young Jack Pine forests but show large retrieval errors for many of the radar pixels in the Old Black Spruce site. This paper shows that L-band radar is capable of retrieving surface soil moisture in forests with a high biomass where the forest structure allows soil moisture information to be carried by scattering mechanisms.