In this paper, we introduce two spatially adaptive covariance filtering methods and evaluate their effect on scatterer separation and height estimation from tomographic SAR. The first one was previously introduced for polarimetric data and uses pixel similarities based on Riemannian distances between covariance matrices. The second one is a new method extending the previous one to patch-based similarities. We show the importance of spatial adaptivity in covariance estimation by comparing the 3D reconstructions obtained with our nonlocal filters and the boxcar filter. Our experiments on simulated and L-band experimental data show the ability of the non-local filters to improve the height estimation and scatterer separation in layover areas thanks to their smoothing and edge preserving properties.