Abstract

A simplified generalized radio frequency interference (RFI) detection method and principal component analysis (PCA) method are utilized to detect and attribute the sources of C-band RFI in AMSR2 L1 brightness temperature data over land during 1–16 July 2017. The results show that the consistency between the two methods provides confidence that RFI may be reliably detected using either of the methods, and the only difference is that the scope of the RFI-contaminated area identified by the former algorithm is larger in some areas than that using the latter method. Strong RFI signals at 6.925 GHz are mainly distributed in the United States, Japan, India, Brazil, and some parts of Europe; meanwhile, RFI signals at 7.3 GHz are mainly distributed in Latin America, Asia, Southern Europe, and Africa. However, no obvious 7.3 GHz RFI appears in the United States or India, indicating that the 7.3 GHz channels mitigate the effects of the C-band RFI in these regions. The RFI signals whose position does not vary with the Earth azimuth of the observations generally come from stable, continuous sources of active ground-based microwave radiation, while the RFI signals which are observed only in some directions on a kind of scanning orbit (ascending/descending) mostly arise from reflected geostationary satellite signals.

Highlights

  • Parameters such as soil moisture, surface temperature, and the rate of surface precipitation are important meteorological factors [1,2,3,4]

  • The following conclusions are drawn: (1) The radio frequency interference (RFI) contamination of the C-band channel over land in summer identified by the generalized RFI detection method is basically the same as that by the principal component analysis (PCA) method

  • The identified location, intensity, and temporal variation characteristics of the RFI contamination are almost the same. This shows that the generalized RFI detection method and PCA algorithm are effective for identifying RFI over land

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Summary

Introduction

Parameters such as soil moisture, surface temperature, and the rate of surface precipitation are important meteorological factors [1,2,3,4]. Low-frequency microwave channels are occupied by various active and passive remote sensing instruments, such as communication satellites, weather and military radars, GPS signals, mobile phones, and so on. This means that, on top of the actual surface thermal radiation, spaceborne microwave radiometers receive signals emitted by active sensors or reflected by the surface, which are collectively referred to as radio frequency interference (RFI) [14,15,16]. The characteristics of RFI observed by AMSR2 (Advanced Microwave Scanning Radiometer 2) in C-band channels are analyzed using a simplified generalized RFI detection method and the principal component analysis (PCA) method. Remote Sens. 2019, 11, 1228 is important for improving the utilization of satellite-borne microwave data in land surface process models and data assimilation

AMSR2 Data
Generalized RFI Detection Method
PCA Method
RFI Distribution Detected by the Generalized RFI Detection Method
RFI Source Analysis
Conclusions and Future Work
Full Text
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