A novel reconstruction algorithm for the improvement of SMOS brightness temperatures

RFI (Radio Frequency Interference) sources, Sun signal and even land-sea transitions may generate large sidelobes that corrupt SMOS brightness temperature products and therefore, the quality of the soil moisture and sea surface salinity retrievals. This work focuses on the reduction of this Gibbs-like contamination in brightness temperature scenes using an alternative image reconstruction approach. This technique is based on sampling the signal at the nodal points, that is, at those points at which the oscillating interference causes the minimum distortion to the geophysical signal. The method has been successfully tested using ocean views significantly reducing general ripples and sidelobes in the contaminated brightness temperature images. In addition, it reduces the standard deviation of the difference between the SMOS measurements and the theoretical model by 1K, which is crucial to improve the quality of the sea surface salinity retrievals.