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New opportunities are arising in Earth observation with the use of multibaseline (MB) SAR being able to combine data stacks for an improved imaging and characterization of the observed scene. Prior to any MB coherent processing, it is necessary to correct the data stack for the phase contributions due to residual non-compensated platform motions (typical in airborne SAR applications) and/or atmospheric propagation delays (e.g. in spaceborne SAR), which in general are different from track to track. The classical calibration methods tackle the MB phase calibration problem by means of the detection of point-like scatterers. However, the effectiveness of these techniques is scenario-dependent, as both the number and spatial distribution of such scatterers varies. Especially in natural scenarios, the MB phase calibration is particularly challenging given the volumetric nature of the scattering. Moreover, a limited number of baselines can impair the detection performance, which in turn results into a degraded calibration quality. In this work we propose a new calibration method based on the minimization of the entropy applied on the vertical profile of the backscattered power to improve the performance. This allows to potentially exploit the MB SAR signal independently of the imaged scattering mechanism. With particular reference to SAR Tomography applications, the proposed method has been tested under controlled conditions by processing a simulated dataset of a forest stand. Performance results are shown in this presentation. The calibration accuracy has been also evaluated with an airborne L-Band dataset acquired by the DLR E-SAR platform over the forest site of Traunstein (Germany), and comparisons with the classical calibration techniques have been carried out.