Accurate channel state information is crucial for directional beamforming in reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. In realistic scenarios, the components of RIS are susceptible to coverage by small particles, leading to blockage of certain elements, which increases the difficulty of channel estimation. In such cases, we propose a two-stage channel training scheme based on Kronecker decomposition to achieve joint RIS blockage and channel estimation. Specifically, we utilize the regularization parameter to recover the sparse blockage coefficients effectively. Additionally, we introduce a denoising algorithm to accelerate the convergence rate of sparse recovery and enhance estimation accuracy. Simulation results show that our proposed method outperforms the existing algorithms regarding RIS blockage and channel estimation accuracy.