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A Methodology to Determine Radio-Frequency Interference in AMSR2 Observations

机译:确定AMSR2观测中的射频干扰的方法

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A study to determine radio-frequency interference (RFI) in low-frequency passive microwave observations of the Advanced Microwave Scanning Radiometer-2 (AMSR2) is performed. RFI detection methods, such as the spectral difference method, have already been applied on microwave satellite sensors. However, these methods may result in false RFI detection, particularly in zones with extreme environmental conditions. To overcome this problem, this paper proposes an approach that uses the additional 7.3-GHz channel of the AMSR2 sensor in a new RFI detection method. This method uses calculated standard errors of estimate to detect RFI contamination in 6.9- and 7.3-GHz observations. It was found that 6.9-GHz observations are mainly contaminated in the USA, India, Japan, and parts of Europe. The 7.3-GHz observations are contaminated in South America, Ukraine, the Middle East, Southeast Asia, and Russia. The fact that these channels are not affected by RFI in exactly the same regions is useful for studies that prefer C-band brightness temperature observations (e.g., soil moisture retrieval algorithms). Therefore, a decision tree approach was set up to determine RFI and to select reliable brightness temperature observations in the lowest frequency free of any man-made contamination. The result is a reduction of the total contaminated pixels in the 6.9-GHz observations of 66% for horizontal observations and even 85% for vertical observations when 7.3 and 10.7 GHz are used. By linking RFI maps with civilization maps, this paper further shows that RFI sources at the C-band frequency are mainly located in urbanized areas.
机译:进行了一项研究,以确定高级微波扫描辐射仪2(AMSR2)的低频无源微波观测中的射频干扰(RFI)。 RFI检测方法(例如光谱差方法)已经应用于微波卫星传感器。但是,这些方法可能会导致错误的RFI检测,尤其是在极端环境条件下的区域。为了克服这个问题,本文提出了一种在新的RFI检测方法中使用AMSR2传感器的额外7.3 GHz信道的方法。该方法使用计算出的估计标准误差来检测6.9 GHz和7.3 GHz观测中的RFI污染。发现6.9 GHz观测主要在美国,印度,日本和欧洲部分地区受到污染。 7.3 GHz观测值在南美,乌克兰,中东,东南亚和俄罗斯受到污染。这些通道在完全相同的区域内不受RFI影响的事实对于喜欢C波段亮度温度观测的研究(例如土壤湿度检索算法)很有用。因此,建立了一种决策树方法来确定RFI并以最低频率选择可靠的亮度温度观测值,而没有任何人为污染。结果是,当使用7.3 GHz和10.7 GHz时,在6.9 GHz观察中,水平观察的总污染像素减少了66%,在垂直观察中减少了85%。通过将RFI地图与文明地图链接起来,本文进一步表明,在C波段频率下的RFI源主要位于城市地区。

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