WindSat Radio-Frequency Interference Signature and Its Identification Over Greenland and Antarctic

Zhao, Juan; Zou, Xiaolei; Weng, Fuzhong

doi:10.1109/tgrs.2012.2230634

Cited by 39 publications

(17 citation statements)

References 13 publications

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“…The double principal component analysis method (DPCA) developed by Zhao et al (2013) has been used to identify RFI in the WindSat data over Greenland and Antarctic. This method takes advantage of the decorrelation for RFI signals and the correlation characteristics of radiation data in different channels for natural surfaces including snow cover.…”

Section: Dpca Methodsmentioning

confidence: 99%

“…There are many possible sources of RFI, including radar, air traffic control, cell phones, garage door remote controls, GPS signals on highways, defense tracking, vehicle speed detection for law enforcement, etc. (Zhao et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Zou et al (2012) also introduced the normalized PCA (NPCA) method to detect RFI in FY-3B MWRI (MicroWave Radiation Imager) data over snow-covered surfaces in winter. Zhao et al (2013) developed a double Principal Component Analysis (DPCA) method for RFI identification in WindSat data over Greenland and Antarctic where the RFI signals are distributed in coastal areas with permanent frozen ice surfaces away from the coast. However, how to identify RFI over the snowscattering surface is still an open issue.…”

Section: Introductionmentioning

confidence: 99%

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

2015

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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.

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Section: Dpca Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2015

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“…It was found that the C-and X-band AMSR-E measurements from the earth's natural thermal emission over land can be interfered by the signals from lower-frequency active microwave transmitters, including radar, air traffic control, cell phones, garage door remote controls, GPS signals on highway, defense tracking, vehicle speed detection for law enforcement, etc. (Zou et al 2012;Zhao et al 2013). Also, the X-and K-band AMSR-E measurements of the natural thermal emission over ocean could be interfered by the geostationary satellite television (TV) signals reflected off the ocean surfaces.…”

Section: Introductionmentioning

confidence: 99%

Detection of Television Frequency Interference with Satellite Microwave Imager Observations over Oceans

Zou

Tian

Weng

2014

Journal of Atmospheric and Oceanic Technology

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The geostationary satellite television (TV) signals that are broadcasted over various continents can be reflected back to space when they reach ocean surfaces. If the reflected signals are intercepted by the antenna of the microwave imager on board polar-orbiting satellites, they are mixed with the thermal emission from the earth and result in direct contamination of the satellite microwave imager measurements. This contamination is referred to as television frequency interference (TFI) and can result in erroneous retrievals of oceanic environmental parameters (e.g., sea surface temperature and sea surface wind speed) from microwave imager measurements. In this study, a principal component analysis (PCA)-based method is applied for detecting the TFI signals over oceans from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) Aqua satellite. It is found that the third principal component of the data matrix of the AMSR-E spectral difference indices from each AMSR-E swath captures the TFI contamination. The TFI-contaminated data on the AMSR-E descending node at both 10.65-and 18.7-GHz frequencies can be separated from uncontaminated data over oceanic areas near the coasts of Europe and the United States based on the intensity of the data projection onto the third principal component (PC). Compared to the earlier methods, the proposed PCA-based algorithm works well on the observations without a priori information and is thus applicable for broader user applications.

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“…Lacava et al (2013) have implemented a multi-temporal robust satellite technique approach to identify the RFI signals. In addition, the uses of principal component analysis (PCA) indices have allowed even more accurate detection of the RFI signals (Li et al, 2006;Zou et al, 2012;Zhao et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

An introduction to factor analysis for radio frequency interference detection on satellite observations

et al. 2014

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A novel radio frequency interference (RFI) detection method is introduced for satellite-borne passive microwave radiometer observations. This method is based on factor analysis, in which variability among observed and correlated variables is described in terms of factors. In the present study, this method is applied to the Tropical Rainfall Measuring Mission (TRMM)/TRMM Microwave Imager (TMI) and Aqua/Advanced Microwave Scanning Radiometer -Earth Observing System (AMSR-E) satellite measurements over the land surface to detect the RFI signals, respectively, in 10 and 6 GHz channels. The RFI detection results are compared with other traditional methods, such as spectral difference method and principal component analysis (PCA) method. It has been found that the newly proposed method is able to detect RFI signals in the C-and X-band radiometer channels as effectively as the conventional PCA method.

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WindSat Radio-Frequency Interference Signature and Its Identification Over Greenland and Antarctic

Cited by 39 publications

References 13 publications

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

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

Detection of Television Frequency Interference with Satellite Microwave Imager Observations over Oceans

An introduction to factor analysis for radio frequency interference detection on satellite observations

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