Near-surface site characterization is of great significance in the fields of geotechnical engineering and resource exploration. In this paper, we propose a near-surface site characterization method based on the joint iterative analysis of first-arrival and surface-wave data (JIAFS). The proposed method combines the advantages of first-arrival traveltime tomography (FATT) and multichannel analysis of surface waves (MASW). First, the 1D S-wave velocity ( v S ) models obtained by MASW are interpolated to construct the pseudo-2D v S model. According to the available geological survey information and borehole data, the initial Poisson’s ratio ( σ ) model is estimated. Based on the estimated σ model, the pseudo-2D v S model is converted to a referenced P-wave velocity ( v P ) model which is utilized to constrain the progress of FATT. This helps FATT overcome the inherent defect that it cannot effectively identify velocity-inversion interfaces and low-velocity zones. On the other hand, the v P model obtained by FATT can provide a favorable priori information to improve the reliability of the results of MASW. Then, the v P and v S models obtained by constrained FATT and MASW are used to update the σ model. In addition, the v P and v S models are also used as initial models in the next iterative analysis. Finally, through the iteration of this process, the two inversion methods can make use of their own advantages to improve each other, so we can establish accurate near-surface v P , v S and σ models under complex geological conditions. A velocity model including low-velocity zone is established for synthetic model test to analyze and verify the advantage of JIAFS. The v P , v S and σ models obtained by JIAFS can accurately identify the low-velocity zone and match the true models well. In addition, the proposed method is applied to the field seismic data acquired for oil and gas exploration in Northwest China. Compared with the results of individual inversions and borehole data, JIAFS can establish more reliable 3D v P , v S and σ models by interpolating the 2D inversion results, which reveals further details and enhances the geological interpretation significantly.