Source localization in the presence of reverberation and a noisy environment is still a challenging research problem and has various signal processing applications. This paper proposes a novel far-field source localization method using the acoustic intensity vector in the spherical harmonics domain. The mathematical model for the sound pressure captured by the spherical microphone array (SMA) is first developed in the spherical harmonics domain. Subsequently, the acoustic intensity vector is derived from the spherical harmonics decomposition of the pressure and acoustic velocity. As the acoustic velocity efficiently preserves the directional information, the intensity vector also contains directional and energy information. The dependency of location on the intensity vector is further explored. Since the intensity vector in the azimuth and elevation plane varies with the location, a unified convolutional neural network (CNN) model is selected to map the intensity features to the locations in reverberant and noisy conditions. Extensive simulations and experiments are conducted both on simulated and real speech data for evaluating the performance of the proposed localization method. The results show a significant improvement in localization accuracy and mean square error (MSE) compared with the state-of-art methods.