Exploiting the full benefits of massive multiple input multiple output (MIMO) technology can be directly affected by the efficiency of the channel state information (CSI) estimation. This paper focuses on frequency division duplexing (FDD) channel estimation and feedback. To the sparsity of the frequency selective massive MIMO channels, a two-step multiple approximate message passing algorithm is developed in the angle-time domain. The angle-time domain can efficiently capture the essential degrees of freedom. Consequently, it is capable of representing the channel with the minimum number of coefficients. The proposed technique for the channel estimation and feedback is divided into two main stages. The first stage is concerned with retrieving the positions of the nonzero time domain dominant taps, while the second stage focuses on estimating the channel coefficients at these taps through exploiting the common and individual sparsity pattern that appears in the angle-time domain using the proposed M-AMP algorithm. Simulation results demonstrate the improved performance of the proposed framework.