Identifying AMSR-E radio-frequency interference over winter land  

作　　者：Sibo ZHANG, Li GUAN 

机构地区：[1]Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science & Technology, Nanjing 210044, China

出　　处：《Frontiers of Earth Science》2015年第3期437-448,共12页地球科学前沿（英文版）

摘　　要：Satellite microwave emission mixed with signals from active sensors is referred to as radio- frequency interference （RFI）. RFI affects greatly the quality of data and retrieval products from space-bome microwave radiometry. An accurate RFI detection will not only enhance geophysical retrievals over land but also provide evidence of the much-needed protection of the microwave frequency band for satellite remote sensing technologies. It is difficult to detect RFI from space-borne microwave radiometer data over winter land, because RFI signals are usually mixed with snow in mid-high latitudes. A modified principal component analysis （PCA） method is proposed in this paper for detecting microwave low frequency RFI signals. Only three original variables, one RFI index （sensitive to RFI signal） and two scattering indices （sensitive to snow scattering）, are included in the vector for principal component analysis in this modified method instead of the nine or seven RFI index original variables used in a normal PCA algorithm. The principal component with higher correlation and contribution to the original RFI index is the RFI-related principal component. In the absence of a reliable validation data set of the ＂true＂ RFI, the consistency in the identified RFI distribution obtained from this method compared to other independent methods, such as the spectral difference method, the normalized PCA method, and the double PCA method, give confidence to the RFI signals＇ identification over land. The simple and reliable modified PCA method could successfully detect RFI not only in summer but also in winter AMSR-E data.Satellite microwave emission mixed with signals from active sensors is referred to as radio- frequency interference （RFI）. RFI affects greatly the quality of data and retrieval products from space-bome microwave radiometry. An accurate RFI detection will not only enhance geophysical retrievals over land but also provide evidence of the much-needed protection of the microwave frequency band for satellite remote sensing technologies. It is difficult to detect RFI from space-borne microwave radiometer data over winter land, because RFI signals are usually mixed with snow in mid-high latitudes. A modified principal component analysis （PCA） method is proposed in this paper for detecting microwave low frequency RFI signals. Only three original variables, one RFI index （sensitive to RFI signal） and two scattering indices （sensitive to snow scattering）, are included in the vector for principal component analysis in this modified method instead of the nine or seven RFI index original variables used in a normal PCA algorithm. The principal component with higher correlation and contribution to the original RFI index is the RFI-related principal component. In the absence of a reliable validation data set of the ＂true＂ RFI, the consistency in the identified RFI distribution obtained from this method compared to other independent methods, such as the spectral difference method, the normalized PCA method, and the double PCA method, give confidence to the RFI signals＇ identification over land. The simple and reliable modified PCA method could successfully detect RFI not only in summer but also in winter AMSR-E data.

关 键 词：microwave remote sensing radio-frequencyinterference （RFI） the Advanced Microwave ScanningRadiometer for Earth Observing System （AMSR-E） principal component analysis （PCA） 

分 类 号：TP721.1[自动化与计算机技术—检测技术与自动化装置] TN958[自动化与计算机技术—控制科学与工程]