The variation in tire pressure markedly alters the mechanical characteristics of tires, particularly in off-road vehicles equipped with distributed drive systems. Crucial to the formulation of a tire inflation and deflation control strategy is the precise determination of the road adhesion coefficient. Addressing this issue, the current study introduces an Extended Kalman Filter (EKF) estimation method for road adhesion coefficient of four wheel drive (4WD) vehicles based on tire pressure changes. By incorporating a tire pressure influence factor, a revised tire model that accounts for pressure changes was developed using the Dugoff tire model as a foundation. Utilizing the extended Kalman filter, this model facilitates real-time estimation of the road adhesion coefficient and enhances the precision of the estimate. Experimental validation confirms the efficacy of the algorithm. Findings indicate that, in comparison to the conventional Dugoff tire model, the method presented herein exhibits superior estimation precision during tire inflation and deflation scenarios, accommodates the intricate and mutable conditions of tire pressure, and provides accurate assessments of the road adhesion coefficient.