Microwave radiometry is the primary means of soil moisture remote sensing and capable of observation soil moisture all-time, all-weather. But the imaging method of traditional microwave radiometry has complex structure and low resolution, which limited largely the appli- cation in regional soil moisture remote sensing. In this paper, we propose a new inversion method for microwave radiation interferometry imaging based on compressed sensing (CS), by mining the sparse and compressible information of the microwave radiation image. The proposed method can break through the limit of the inherent spatial resolution of the imaging system which based on the Nyquist sampling, achieving the spatial resolution image closely obtained by the expensive large aperture imaging system, and reducing the complexity and the cost of the hardware of the imaging system configuration. Due to the complexity of microwave radiation image scene, it is difficult to sparsely represent the microwave radiation image by single orthogonal basis, so we make use of the sparse representation in mixed orthogonal basis and the constraint conditions of total variation regularization of microwave radiation image, and establish the optimal recon- struction model of microwave radiation image. We solve the optimal solution of reconstruction model by adopting the alternating direction method (ADM), achieve high resolution microwave radiation image reconstruction from low-dimensional measurements to high -dimensional data, and solve the contradiction between the high resolution and the complexity of the system, and establish evaluation methods of spatial resolution. The simulation results show that, compared with orthogonal the matching pursuit (OMP) algorithm, the proposed algorithm can achieve higher spatial resolution.