This paper presents a general reduced-rank channel model and a corresponding low-complexity estimation scheme for wideband spatial-correlated multiple-input multiple-output (MIMO) systems. We focus on orthogonal frequency division multiplex (OFDM) based systems. The proposed reduced-rank model is useful for many post-channel-estimation applications such as channel state information (CSI) feedback, precoder design and user/channel selection. Our work is an extension of an earlier investigation on narrowband MIMO channels. Like the narrowband case, the proposed wide channel estimator also offer the advantage of rendering both channel coefficients and mean angle of departure (AoD) simultaneously. BY exploiting the time, frequency, and spatial correlations of the channel and with continuous-type pilot symbols, we found that, even with as high as a compression ratio of 1\%, our channel estimator is capable of maintaining an acceptable mean squared errors (MSE) in highly correlated environments. Both mathematical analysis and computer simulation, based on some industry-approved standard channel models, indicate that our algorithm outperform the conventional least-square estimator within most range of interest.