Identifying AMSR-E radio-frequency interference over winter land
Identifying AMSR-E radio-frequency interference over winter land
复制标题
DOI：
10.1007/s11707-014-0476-1
复制
发表时间：
2015-02
影响因子：
2
通讯作者：
Sibo Zhang;L. Guan
中科院分区：
文献类型：
--
作者：
Sibo Zhang;L. Guan
卫星微波辐射与有源传感器的信号相混合，称为射频干扰。RFI对星载微波辐射测量数据和反演产品的质量有很大影响。准确的射频干扰探测不仅将加强陆地上的地球物理反演，而且还将提供证据，证明急需对卫星遥感技术的微波频带进行保护。在中高纬度地区，RFI信号通常与积雪混在一起，这给利用星载微波辐射计数据检测冬季陆地RFI带来了困难。提出了一种改进的主元分析（PCA）方法，用于微波低频RFI信号的检测。在主成分分析中，该方法只包含三个原始变量，即一个RFI指标（对RFI信号敏感）和两个散射指标（对雪散射敏感），而不是通常PCA算法中使用的九个或七个RFI指标原始变量。对原RFI指数相关性和贡献度较高的主成分为RFI相关主成分。在缺乏可靠的“真实”RFI验证数据集的情况下，从该方法获得的所识别的RFI分布的一致性与其他独立的方法（例如，谱差方法、归一化PCA方法和双PCA方法）相比，为RFI信号在陆地上的识别提供了置信度。该方法简单、可靠，不仅可以成功地检测夏季的RFI，而且可以成功地检测冬季的AMSR-E数据。
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-borne 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.