Detection of radio-frequency interference signals from AMSR-E data over the United States with snow cover

Abstract

Radio Frequency Interference (RFI) causes severe contamination to passive and active microwave sensing observations and corresponding retrieval products. RFI signals should be detected and filtered before applying the microwave data to retrieval and data assimilation. It is difficult to detect RFI over land surfaces covered by snow because of the scattering effect of snow surface. The double principal component analysis (DPCA) method is adopted in this study, and its ability in identifying RFI signals in AMSR-E data over snow covered regions is investigated. Results show that the DPCA method can detect RFI signals effectively in spite of the impact of snow scattering, and the detected RFI signals persistent over time. Compared to other methods, such as PCA and normalized PCA, DPCA is more robust and suitable for operational application.

Keywords

Radio Frequency Interference (RFI) / AMSR-E / double principal component analysis (DPCA)

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