Detection and Flagging of Radio Frequency Interference Contamination in SMAP Ocean Observations

Abstract Starting in late 2023, sea surface salinity estimates derived from measurements made by the SMAP L-band radiometer have been increasingly contaminated by radio frequency interference (RFI). We study various methods of identifying RFI-affected SMAP measurements over the ocean during level 2 processing. The most reliable RFI detection techniques are the chi-squared of the maximum likelihood estimator of the salinity retrieval algorithm, the difference in observed brightness temperature between forward and backward looking parts of the swaths, and the value of the observed fourth Stokes parameter. These detection methods do not rely on any external ancillary salinity data. We demonstrate that the number of false alarms in the RFI detection can be reduced by checking for spatial clusters of cells that are affected. Additionally, the detection rate can be improved by flagging neighbors of a cell in which RFI has been detected. We test and evaluate the RFI detection algorithm in various case studies and assess missed detection and false alarm rates. Significance Statement Starting in late 2023, brightness temperature measurements acquired by spaceborne L-band radiometers have been increasingly affected by contamination from radio frequency interference (RFI), which results in erroneous retrieval of sea surface salinity. The RFI is caused mainly by military operations within areas of geopolitical conflicts. The most strongly affected regions include the Bay of Bengal, Gulf of Thailand, Mediterranean Sea, northern Europe, Black Sea, Caspian Sea, Arabian Sea, and the South China Sea. The purpose of our study is the development and evaluation of an algorithm that is able to detect and remove these RFI-contaminated measurements with a minimal number of false alarms.