Velocity model building and imaging for land surveys are particularly challenging due to the complexities of the near surface model and strong noise. In this abstract, we design a model building workflow for land seismic data that incorporates dynamic matching fullwaveform inversion (DMFWI). DMFWI employs an objective function that uses multi-dimensional local cross correlations which minimize the impact of amplitudes and gives reliable results even in the presence of strong noise. We present the results of improved imaging for onshore surveys in Mexico. We also explore full-waveform inversion (FWI) for imaging and utilize the high-resolution FWI velocity model to estimate reflectivity. The FWI images show enhanced features and provide an alternative image for poor S/N land datasets.