Reliably separating primary and multiple reections in a shallow water environment (i.e., 50 m to 200 m water depth) still remains a challenge. The success of previously published closed-loop surface-related multiple estimation (CL-SRME) depends heavily on the data coverage, i.e., the near-offset reconstruction. Therefore, we propose the integrated framework of CL-SRME and full-wavefield migration (FWM). Multiples recorded in the data are capable of helping inll the acquisition imprint of the FWM image. With this image as a strong constraint, we are able to reconstruct the data at near-offsets, which is essential for better primary and multiple estimation during CL-SRME. FWM applied in a non-linear way can avoid the negative inuences from the missing data, and at the same time bring in more physics between primaries and multiples. The FWM image of the top part of the subsurface is also used to back-project the information from multiples to primaries with the physical constraint of all this information belongs to the same earth model, provided that a good description of the source wavefield and a reasonable velocity model are available. The proposed integrated framework first reconstructs near-offsets via the closed-loop imaging process of FWM and then feeds the complete reconstructed data to CL-SRME for better primary and multiple estimation. A good performance is demonstrated on both 2D synthetic and field data examples in a challenging shallow water environment.