Since the computational load of Two-Dimensional Direction-Of-Arrival (2D-DOA) estimation using sparse reconstruction is high, a 2D-DOA estimation algorithm based on sparse representation of reduced covariance matrix was proposed. Firstly, the manifold vector matrix redundant dictionary was constructed by using space angle, which maps the azimuth angle and pitch angle from two-dimensional space to one-dimensional space. Consequently, the length of the dictionary and the computational complexity were greatly reduced, and the pitch angle and the azimuth angle could be automatically matched. Secondly, the sampled covariance matrix sparse representation model was improved to reduce its model dimension. Then, constraint residual confidence intervals were obtained by the residual constraint characteristics of the sparse reconstruction of the covariance matrix to avoid the choice of regularization parameters. Finally, the 2D-DOA estimation was realized via convex optimization package. Simulation results show that the incident angle can be accurately estimated when selected covariance matrix column reaches a threshold (the number is 3 in the presence of 2 incident signals). As compared with the feature vector method based on space angle, the estimation accuracy of the proposed method is higher when the Signal-to-Noise Ratio (SNR) is relatively small (<5dB), and is slightly lower under small number of snapshots (<100); both methods have similar estimation accuracy with small angle intervals.