针对星载微波成像仪低频窗区通道观测数据中存在大范围无线电频率干扰(简称RFI)的情况,以微波扫描辐射计(AMSR-E)为例,首先用改进的主成分分析方法对RFI进行识别;进而探讨RFI存在对反演地表参数的影响.然后,利用经过线性拟合RFI订正后的AMSR-E观测资料,采用一维变分1D-Var方法进行地表参数反演,通过对美国地区陆地RFI订正前、后地表反演产品(地表温度及降水率)的比较,发现RFI干扰使得受影响区域反演的地表温度及降水率异常偏高,存在较大误差.因此,在使用星载微波成像仪低频窗区通道观测进行地表参数反演和资料同化前必须进行有效地RFI识别和订正,改进的主成分分析识别方法和线性拟合的RFI订正算法对陆地上观测是有效的.

<p>微波地表发射率是表征地表特征的重要参数,也是反演地表、大气参数的重要条件。相比较物理模型,其模拟计算需要若干输入参数,且相当一部分地表、植被特征参数很难从常规资料中获取,应用星载被动微波辐射计资料可以在更大空间和时间尺度范围内直接反演地表发射率。从目前常用的几种被动微波遥感反演方法(包括经验统计方法、辐射传输方程方法、指数分析方法、神经网络方法、一维变分方法等等)回顾了微波地表发射率反演的国内外研究进展及其研究中存在的问题,并对这些方法的优、缺点进行了评价。最后指出,今后应开发识别和订正直接影响卫星观测值的无线电频率干扰(RFI)算法,改善云、雨检测算法,并且加强微波波段大气辐射传输等过程的机理研究。</p>

&nbsp;为了提供更适用的地表参数反演方案，对Njoku等模拟AMSRE-E数据的多参数反演工作重新进行正向模拟和算法改进，算法用MATLAB开发实现。反演结果表明，改进后的反演方法不仅在精度上略高于原方法，而且还能反演裸露地表可实际测量的粗糙度参数，并在更大的植被含水量范围内达到较高的反演精度。

使用高级积分方程模型，模拟多个地表参数条件下的风云三号B星微波成像仪（FY-3B/MWRI）资料。基于模拟数据，分析地表微波辐射特性，利用粗糙地表发射率<em>Q_p</em>模型，建立我国西部地区裸露地表土壤湿度反演模型。将该模型用于我国西部地区4个日期（2011年10月8日、10月18日、10月28日和11月8日）的土壤湿度反演，并将反演结果用实测数据进行交叉验证。结果表明： 反演土壤湿度与实测土壤湿度的决策系数<em>R</em>²为0.604，均方根误差为0.030 5 cm³/cm³，反演模型具有较高的反演精度。

<p>&nbsp;针对对地观测卫星多传感器的特点，提出了借助MODIS地表温度产品从被动微波数据中反演地表温度的方法。即利用MODIS地表温度产品和AMSR不同通道之间的亮度温度，建立地表温度的反演方程。该方法克服了以往需要测量同步数据的困难，为不同传感器之间的参数反演相互校正和综合利用多传感器的数据提供实际应用和理论依据。文中以MODIS地表温度产品作为评价标准，对方法进行检验，其平均误差为2~3℃。另外，微波的发射率是土壤水分反演的关键参数，在对微波地表温度反演的基础上，进一步对发射率进行了研究。</p>

<p>微波遥感能穿透云层，甚至可穿透一定程度的雨区，可以弥补热红外遥感的不足。发展基于被动微波遥感的地表温度反演算法可以全天候地为相关领域提供数据服务。根据前人研究，该文从方法论的角度将已有的反演方法分为统计模型法、物理模型法和神经网络算法3类，分析了每种方法的优缺点，并探讨了未来微波遥感反演地表温度的发展方向，以期为进一步研究提供参考。</p>

<p>热红外遥感反演地表温度已取得丰硕的成果,某些反演算法精度可达到1 K以内。然而在非晴空条件下,热红外遥感的应用受到很大限制,甚至无能为力。而被动微波遥感受大气干扰小,可穿透云层获取地表辐射信息,具有全天候、多极化及高时间分辨率等特点,在地表温度反演中具有独特的优越性。被动微波反演地表温度已经成为被动微波遥感技术应用研究的主要问题之一。系统阐述了微波热辐射机理、地表温度反演模型、反演算法及应用现状,分析了目前被动微波地表温度反演研究中存在的主要问题与技术难点,为后续相关研究提供参考。</p>

雪深(snow depth,SD)和雪水当量(snow water equivalent,SWE)是气候水文研究中的重要参数,在雪灾监测中尤为重要。首先,简要介绍了被动微波遥感SD和SWE反演算法的物理基础——积雪微波辐射传输模型,分析了不同微波频段、不同特点的积雪微波辐射和散射特性。然后,根据前人的研究从数学角度将反演算法分为线性亮温梯度法和基于先验知识法,总结了2类算法的优势和局限性: 线性亮温梯度法相对简单、速度快,一般只适用于特定的研究区; 先验知识法需要获取研究区的样本数据,并反复训练才能达到较好的精度,但对样本的独立性及其均值差异显著性的要求较高。最后,重点介绍了我国风云三号微波成像仪(FY-3 MWRI)的全球SD和SWE反演算法和针对中国区域的改进算法,并对未来的研究热点进行了展望。

ABSTRACT A spectral difference method is used to quantify the magnitude and extent of radio-frequency interference (RFI) observed over the United States in the Aqua AMSR-E radiometer channels. A survey using data from the AMSR-E instrument launched in May 2002 shows the interference to be widespread in the C-band (6.9 GHz) channels. The RFI is located mostly, but not always, near large highly populated urban areas. The locations of interference are persistent in time, but the magnitudes show temporal and directional variability. Strong and moderate RFI can be identified relatively easily using an RFI index derived from the spectral difference between the 6.9- and 10.7-GHz channels. Weak RFI is difficult to distinguish, however, from natural geophysical variability. These findings have implications for future microwave sensing at C-band, particularly over land areas. An innovative concept for radiometer system design is also discussed as a possible mitigation approach.

Radio-frequency interference (RFI) in the spaceborne multichannel radiometer data of WindSat and the Advanced Microwave Scanning Radiometer-EOS is currently being detected using a spectral difference technique. Such a technique does not explicitly utilize multichannel correlations of radiometer data, which are key information in separating RFI from natural radiations. Furthermore, it is not optimal for radiometer data observed over ocean regions due to the inherent large natural variability of spectral difference over ocean. In this paper, we first analyzed multivariate WindSat and Scanning Multichannel Microwave Radiometer (SMMR) data in terms of channel correlation, information content, and principal components of WindSat and SMMR data. Then two methods based on channel correlation were developed for RFI detection over land and ocean. Over land, we extended the spectral difference technique using principal component analysis (PCA) of RFI indices, which integrates statistics of target emission/scattering characteristics (through RFI indices) and multivariate correlation of radiometer data into a single statistical framework of PCA. Over ocean, channel regression of X-band can account for nearly all of the natural variations in the WindSat data. Therefore, we use a channel regression-based model difference technique to directly predict RFI-free brightness temperature, and therefore RFI intensity. Although model difference technique is most desirable, it is more difficult to apply over land due to heterogeneity of land surfaces. Both methods improve our knowledge of RFI signatures in terms of channel correlations and explore potential RFI mitigation, and thus provide risk reductions for future satellite passive microwave missions such as the NPOESS Conical Scanning Microwave Imager/Sounder. The new RFI algorithms are effective in detecting RFI in the C- and X-band Windsat radiometer channels over land and ocean.

Radio-frequency interference (RFI) is an increasingly serious problem for passive and active microwave sensing of the Earth. To satisfy their measurement objectives, many spaceborne passive sensors must operate in unprotected bands, and future sensors may also need to operate in unprotected bands. Data from these sensors are likely to be increasingly contaminated by RFI as the spectrum becomes more crowded. In a previous paper we reported on a preliminary investigation of RFI observed over the United States in the 6.9-GHz channels of the Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System Aqua satellite. Here, we extend the analysis to an investigation of RFI in the 6.9- and 10.7-GHz AMSR-E channels over the global land domain and for a one-year observation period. The spatial and temporal characteristics of the RFI are examined by the use of spectral indices. The observed RFI at 6.9 GHz is most densely concentrated in the United States, Japan, and the Middle East, and is sparser in Europe, while at 10.7 GHz the RFI is concentrated mostly in England, Italy, and Japan. Classification of RFI using means and standard deviations of the spectral indices is effective in identifying strong RFI. In many cases, however, it is difficult, using these indices, to distinguish weak RFI from natural geophysical variability. Geophysical retrievals using RFI-filtered data may therefore contain residual errors due to weak RFI. More robust radiometer designs and continued efforts to protect spectrum allocations will be needed in future to ensure the viability of spaceborne passive microwave sensing.

Radio-frequency interference (RFI) is increasingly a severe problem for present and future microwave satellite missions. RFI at C- and X-bands can contaminate remotely sensed measurements, as experienced with the Advanced Microwave Scanning Radiometer (AMSR-E) and the WindSat sensor. In this work, the multitemporal Robust Satellite Techniques approach has been implemented on C-band AMSR-E data in order to identify areas systematically affected by different levels of RFI, trying to discriminate them from natural geophysical variability zones. To the scope, nine years of AMSR-E data have been investigated, allowing us also to better infer RFI impact on data acquired during ascending or descending passes, as well as in horizontal or vertical polarization. In detail, two analyses were carried out: one considering only measurements at C-band and another one taking into account a combination between C- and X-band measurements. The results of this study will be shown and discussed in this paper.

Radio-frequency interference (RFI) in the spaceborne multichannel radiometer data of WindSat and the Advanced Microwave Scanning Radiometer-EOS is currently being detected using a spectral difference technique. Such a technique does not explicitly utilize multichannel correlations of radiometer data, which are key information in separating RFI from natural radiations. Furthermore, it is not optimal for radiometer data observed over ocean regions due to the inherent large natural variability of spectral difference over ocean. In this paper, we first analyzed multivariate WindSat and Scanning Multichannel Microwave Radiometer (SMMR) data in terms of channel correlation, information content, and principal components of WindSat and SMMR data. Then two methods based on channel correlation were developed for RFI detection over land and ocean. Over land, we extended the spectral difference technique using principal component analysis (PCA) of RFI indices, which integrates statistics of target emission/scattering characteristics (through RFI indices) and multivariate correlation of radiometer data into a single statistical framework of PCA. Over ocean, channel regression of X-band can account for nearly all of the natural variations in the WindSat data. Therefore, we use a channel regression-based model difference technique to directly predict RFI-free brightness temperature, and therefore RFI intensity. Although model difference technique is most desirable, it is more difficult to apply over land due to heterogeneity of land surfaces. Both methods improve our knowledge of RFI signatures in terms of channel correlations and explore potential RFI mitigation, and thus provide risk reductions for future satellite passive microwave missions such as the NPOESS Conical Scanning Microwave Imager/Sounder. The new RFI algorithms are effective in detecting RFI in the C- and X-band Windsat radiometer channels over land and ocean.

基于2011年6月1日至16日先进微波扫描辐射计(AMSR-E)的观测资料，采用改进的主成分分析算法，对欧洲陆地区域的无线电频率干扰(RFI)进行识别和分析。研究发现影响英国和意大利的X波段RFI源主要是稳定的、持续的地面主动源，而影响欧洲其他国家的RFI则主要是反射的静止电视卫星信号对星载微波被动传感器观测的干扰。源于静止电视卫星的RFI出现位置和强度随时间周期性变化，在欧洲陆地多出现在星载微波辐射计升轨观测上，降轨观测则几乎不受其干扰。RFI出现位置和强度与星载微波辐射计扫描方位角和观测视场相对静止电视卫星的方位有关，只有当星载微波辐射计视场扫描方位角大小与该视场相当于静止卫星发射方位角大小接近时该视场易受RFI影响。