A Kurtosis-Based Approach to Detect RFI in SMOS Image Reconstruction Data Processor

• Published in: IEEE transactions on geoscience and remote sensing Vol. 52; no. 11; pp. 7038 - 7047
• Main Authors: Khazaal, Ali, Cabot, Francois, Anterrieu, Eric, Soldo, Yan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-11-2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Contamination; Extraterrestrial measurements; Geophysics; Image reconstruction; Imaging; Kurtosis; Missions; Oceans; Orbits; Pollution measurement; radio-frequency interference (RFI); radiometry; remote sensing; Salinity; Sea measurements; Soil moisture; synthetic aperture imaging; radio-frequency interference (RFI); remote sensing; Kurtosis; radiometry; synthetic aperture imaging
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2014.2306713

The Soil Moisture and Ocean Salinity (SMOS) mission is a European Space Agency project aimed to observe two important geophysical variables, i.e., soil moisture over land and ocean salinity by L-band microwave imaging radiometry. This work is concerned with the contamination of the SMOS data by radio-frequency interferences (RFIs), which degrades the performance of the mission. In this paper, we propose an approach that detects if a given snapshot is contaminated, or not, by RFI. This approach is based on evaluating the kurtosis of each snapshot or data set, using all interferometric measurements provided by the instrument. The obtained kurtosis is considered as an indicator on how much the snapshot is polluted by RFI, thus allowing the user to decide on whether to keep or discard it.