The presented study focuses on the experimental system identification for the longitudinal aircraft model of a micro fixed-wing unmanned airplane in ground effect flights and the longitudinal dynamic stability analysis. The primary contribution is modifying the traditional longitudinal aerodynamic models (i.e. lift coefficient \({C}_{L}\) , drag coefficient \({C}_{D}\) and pitching moment coefficient \({C}_{m}\) ) to incorporate the influence of altitude variation in the ground effect and utilize experimental flight data to identify the model parameters. In this study, we incorporate the relative altitude as an additional state variable. This extends the ordinary Taylor expansion of the longitudinal aerodynamic coefficient model to include the “height derivatives” (i.e. \({C}_{Lh}\) , \({C}_{Dh}\) and \({C}_{mh}\) [4]). A genetic optimization algorithm is applied to the flight data and executes the estimation of the model derivatives. After filling the aircraft state space model, the primary observation was that the Phugoid mode becomes unstable, and the Short Period Mode turns out to be less stable in the ground effect flight. Meanwhile, an unconventional convergent and non-oscillatory mode shows the aircraft’s tendency to maintain its nominal flight speed and altitude. It should be noted that different aircraft configurations and designs could lead to different values of height derivatives, which will consequently lead to different ground effect dynamic behaviour. For the illustration, a root locus analysis with respect to each height derivative is performed. The single impact of each height derivative is observed so that it provides insights into the aircraft design considerations.