In this paper we investigate a least square algorithm to retrieve forest parameters from interferometric, fully polarimetric radar remote sensing P-band data, based on an interferometric optimization scheme which is applied to maximize the separation of scattering phase centers related to the pertinent interferometric coherences in order to obtain most accurate parameter inversion results. A recently developed approach is especially adopted to airborne P-band data, introducing a least square minimization scheme in which synthetic interferometric coherences computed from a scattering model, which is based on a randomly oriented volume over a non-penetrable ground, are compared with three interferometric coherences measured by the P-band sensor. Through fitting of the synthetic and the measured interferometric coherences, the pertinent candidate parameters of the optimization problem can be retrieved. These parameters are the forest volume thickness, the volume extinction coefficient, the interferometric phase related to the underlying topography, and the effective ground-to-volume amplitude ratios related to the interferometric coherences. Through a weighted superposition of all the interferometric coherences provided by the fully polarimetric radar sensor these coherences can be maximized and introduced as the right-hand side of the parameter optimization problem. Experimental results obtained from P-band fully polarimetric single baseline interferometric data acquired over the amazon rain forest are shown in order to demonstrate the potential of the proposed approach. Furthermore, a (chi) 2-test is performed on the data to prove the validity of the introduced scattering model for rain forest vegetation.