The quadrupole resonance (QR) technology can be used as a confirming sensor for buried plastic landmine detection by detecting the explosives (e.g., TNT and RDX) within the mine. We focus herein on the detection of TNT via the QR sensor. Since the frequency of the QR signal is located within the AM radio frequency band, the QR signal can be corrupted by strong radio frequency interferences (RFIs). Hence to detect the very weak QR signal, RFI mitigation is essential. Reference antennas, which receive RFIs only, can be used together with the main antenna, which receives both the QR signal and the RFIs, for RFI mitigation. By taking advantage of the spatial correlation of the RFIs received by the antenna array, the RFIs can be reduced significantly. However, the RFIs are usually colored both spatially and temporally and hence exploiting only the spatial diversity of the antenna array may not give the best performance. We exploit herein both the spatial and temporal correlation of the RFIs to improve the TNT detection performance. First, we consider exploiting the spatial correlation of the RFIs only and propose a maximum likelihood (ML) estimator for parameter estimation and a constant false alarm rate (CFAR) detector for TNT detection. Second, we adopt a multichannel autoregressive model to take into account the temporal correlation of the RFIs and devise a detector based on the model. Third, we take advantage of the temporal correlation by using a two-dimensional robust Capon beamformer (RCB) with the ML estimator for improved RFI mitigation. Finally, we combine the merits of all of the three aforementioned approaches for TNT detection. The effectiveness of the combined method is demonstrated using the experimental data collected by Quantum Magnetics, Inc.