An Improved Method of Multiple RFI Intensity Estimation for Synthetic Aperture Interferometric Radiometer

L-band radio frequency interferences (RFIs) seriously contaminate remote sensing data. This poses a major challenge in processing microwave radiometric data and can substantially degrade the quality of the data. There is an urgent need to estimate RFI intensity to mitigate Gibbs errors caused by RFIs along the sidelobes. The traditional CLEAN algorithm is limited in its ability to account for the interactions among multiple RFIs, which results in biased intensity estimations. In this article, the RFI brightness temperature (BT) model is modified to incorporate the interactions among multiple RFI sources. Building upon this modified model, we establish fundamental equation sets and propose a numerical solution method for calculating RFI intensity. Experiments and simulations based on soil moisture and ocean salinity (SMOS) data confirm that the method presented in this article, which we have named multiple source intensity decoupling (MUSID), outperforms the traditional CLEAN algorithm.