This letter analyzes the impact of the accuracy of estimation of the data covariance matrix in synthetic aperture radar tomography of vegetated areas. The characterization of volumetric areas requires a robust estimation of the covariance matrix, which is usually performed by means of an averaging operation of neighboring pixels. In this letter, a different approach, based on the use of local and nonlocal (NL) neighborhoods of pixels, is evaluated. The analysis considers the quality of the reconstructed vertical profile obtained using both single and fully polarimetric multibaseline (MB) data. In the case of fully polarimetric data, two procedures for obtaining the vertical reconstruction are analyzed: the sum of Kronecker product decomposition of the covariance matrix and a procedure based on the full-rank estimation of a 3-D coherence matrix. The analysis of the impact of NL technique on the robust estimation of covariance matrix and on the capability of separating the interfering signals is performed using simulated and P-band real MB data.