Review on software identification methods for radio frequency interference of spaceborne microwave radiometer

Compared to visible and infrared spectrum used by meteorological satellite instruments, microwave remote sensing is not affected easily by atmosphere because of its strong penetration capability of the cloud, rain and atmosphere, and also its all weather and all day monitoring capability.Moreover, microwave sensors are very sensitive to changes of vegetation characteristics, soil moisture and snow parameters.So microwave measurements have been widely used in land and sea surface parameters monitoring and retrieving.However, in recent years, due to the increasing conflict between scientific and commercial users of the radio spectrum, these lower frequency bands observations of spaceborne microwave imagers which recieves passively earth-atmosphere system thermal radiations are more and more affected by the signals from lower frequency active microwave transmitters. Man-made RFI originates from transmitters on surface, airborne, and spaceborne platforms and contaminates remote measurements of the Earth's scattering and emission by adding spurious noise of unpredictable characteristics, resulting in too higher brightness temperatures than nature surface within the field of view. Such a phenomenon of satellite measured passive microwave thermal emission being mixed with the signals from the active sensors is referred to as radio frequency interference (RFI). The interferences of the radiance measurements from the meteorological satellite radiometric instruments with TV signals reflected off the ocean surface are known as television frequency interference (TFI). If not properly identified and rejected, this RFI or TFI contamination problem would introduce errors in the product retrieval and data assimilation, and could significantly reduce the science value of existing and future passive microwave missions.Several common software RFI identifying algorithms for spaceborne microwave radiometer are systematically reviewed in this paper, such as spectral difference method, mean and standard deviation method, model difference method, principal component analysis method, et al., as well as some new methods, standard error of estimate method, ratio of spectral differences. The main advantages and limitations along with the application scope of each method are also discussed.At the same time, the TFI distribution characteristics, as well as detection and correction method to interference from reflected stationary communication and television satellite downlink signal are introduced in detail.