A method utilizing the adaptive strong tracking unscented Kalman filter (ASTUKF) was proposed to address the issue of divergent identification results caused by state model errors and time-varying noise resulting from changes in road environments during the terrain parameters identification of distributed drive agricultural vehicles. Compared with the traditional internal combustion engine agricultural vehicles, distributed drive agricultural vehicles can directly obtain state information of the driving wheel. And combining the μ-s model which contained adhesion coefficient and limit slip ratio, a state function and a measurement function of unscented Kalman filter (UKF) identification algorithm were established. At the same time, strong tracking filter (STF) and adaptive filter (AF) were introduced into the identification algorithm to improve identification accuracy and robustness against the changing environment, and singular value decomposition (SVD) was used to solve the problem of non-positive definite matrix in iterative process. The simulation test showed that under the condition of abrupt noise environment, the identification result of ASTUKF can quickly converge to target value, which was not affected by abrupt noise. Mean absolute errors (MAE) of the adhesion coefficient estimation results of each driving wheel were 0.0144, 0.0267, 0.0144 and 0.0267, respectively, and MAE of the limit slip ratio estimation results were 0.0025, 0.0028, 0.0025 and 0.0028, respectively. The real vehicle test showed that the 95% confidence interval of average identification result of ASTUKF can match the measured value on test road of cultivated and uncultivated road. The identification results of adhesion coefficient of the whole vehicle were 0.4061 (uncultivated road) and 0.3991 (cultivated road), and the identification results of limit slip ratio were 0.1484 (uncultivated road) and 0.3600 (cultivated road), which can provide a theoretical reference for the operation parameter perception of distributed electric agricultural vehicles.