
Detection of Television Frequency Interference with Satellite Microwave Imager Observations over Oceans
Author(s) -
Xiaolei Zou,
Xiaoxu Tian,
Fuzhong Weng
Publication year - 2014
Publication title -
journal of atmospheric and oceanic technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.774
H-Index - 124
eISSN - 1520-0426
pISSN - 0739-0572
DOI - 10.1175/jtech-d-14-00086.1
Subject(s) - remote sensing , satellite , environmental science , radiometer , geostationary orbit , microwave , principal component analysis , microwave radiometer , meteorology , geology , computer science , geography , physics , telecommunications , astronomy , artificial intelligence
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.