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.

Cross validations of radio-frequency interference signature in AMSR-E data using two detection methods

“…Currently, several robust RFI detection methods exist for various microwave imagers, including the spectral difference method (Li et al 2004;McKague, Puckett, and Ruf 2010;Njoku et al 2005;Wu and Weng 2011), the mean/ standard deviation method (Njoku et al 2005), and the principle component analysis (PCA) method (Li et al 2006). There are also some extended PCA techniques, including normalized PCA (NPCA) (Zou et al 2012;Zou, Tian, and Weng 2014) and double PCA (DPCA) (Feng, Zou, and Zhao 2016;Guan, Xia, and Zhang 2015;Zhao, Zou, and Weng 2013). However, the verification of all RFI detection methods remains unresolved, since there is no reliable validation data-set of the 'true' RFI signals.…”