Microwave land emissivities can be related to variability in the soil and vegetation. From the observed radiances we can create possible soil and vegetative changes to recreate the microwave emission. This knowledge can help to improve soil and canopy interactions in weather prediction or climate models. In this study we introduce an optimal-estimation Bayesian retrieval of atmospheric and surface parameters. The primary focus of this study is the surface emissivity retrieved. The vegetation is considered an inhomogeneous scattering layer in its interaction between the lower soil boundary and the atmosphere above. The vegetation and soil emission combine to make the surface emissivity. Case studies using AMSU, TMI, and SSM/I were chosen to avoid desert or snow covered regions, and focus on the changing season where the depolarization due to scattering in the canopy changes most rapidly. This 1DVAR technique can be used across a wide microwave spectrum, 10 - 183 GHz, and the results will continue to improve the statistics on the variability of passive microwave land emissivity.