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。

Land surface temperature (LST) is an essential parameter in such fields of research as climate, hydrology and ecology, and it plays a significant role in the understanding of the water and energy balance of the Earth's surface. Because the heterogeneity of the underlying surface is most likely a main source of the uncertainties of the satellite derived LST, this paper aims to evaluate the accuracy of the FY-2C derived LST over the heterogeneous area of Maqu County in the source region of the Yellow River and subsequently to provide solid basis for the future development of the LST inversion algorithm and product. MODIS LST product (MOD11B1) was primarily conducted to verify the FY-2C derived LST over the study area. In addition to the MODIS data, soil temperature measurements from 20 soil samples of the study area were also implemented to validate the FY-2C derived LST. The results indicate that a significant correlation exists between the two datasets, with the coefficient of correlation, varying from 0.72 to 0.95, root mean square error(RMSE) ranging from 0.44 to 3.87 K, and the average RMSE being 1.90 K. The FY-2C derived LST exhibits a consistent variation with the measured soil temperature, and the coefficient of correlation reaches 0.69.

以3S技术为支持,采用TM数据、应用单窗算法对1989-2010年间北京市五环内地表温度（ land surface temperature,LST）进行反演,并采用同步的MODIS 温度产品对反演结果进行对比验证。在此基础上,利用距平值分析和剖面线分析等方法揭示了20多a来北京市地表温度的时空分异总体特征；基于归一化水汽指数（ normalized difference moisture index,NDMI）和归一化建筑指数（ normalized difference building index,NDBI）分析了研究区内地表温度的时空变化特征,定量揭示并阐述了地表温度和NDMI及NDBI的相关关系。研究结果表明：反演温度可较为真实地反映研究区地表热量的空间差异；北京地表温度时空变化特征明显,同一时段、不同土地覆盖类型的地表温度差异较为显著,热岛效应明显；地表温度和NDBI呈显著的正相关,与NDMI则呈显著的负相关,NDMI和NDBI在表征地表热特征方面均是较好的指标。该结果为监测北京地表温度变化及其时空分布特征提供了依据。

针对对地观测卫星多传感器的特点,提出了借助MOD IS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MOD IS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MOD IS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。

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) affects greatly the quality of the data and retrieval products from space-borne microwave radiometry. Analysis of the Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) Aqua satellite observations reveals very strong and widespread RFI contaminations on the C- and X-band data. Fortunately, the strong and moderate RFI signals can be easily identified using an index on observed brightness temperature spectrum. It is the weak RFI that is difficult to be separated from the nature surface emission. In this study, a new algorithm is proposed for RFI detection and correction. The simulated brightness temperature is used as a background signal ( B ) and a departure of the observation from the background ( O-B ) is utilized for detection of RFI. It is found that the O-B departure can result from either a natural event (e.g., precipitation or flooding) or an RFI signal. A separation between the nature event and RFI is further realized based on the scattering index (SI). A positive SI index and low brightness temperatures at high frequencies indicate precipitation. In the RFI correction, a relationship between AMSR-E measurements at 10.65 GHz and those at 18.7 or 6.925 GHz is first developed using the AMSR-E training data sets under RFI-free conditions. Contamination of AMSR-E measurements at 10.65 GHz is then predicted from the RFI-free measurements at 18.7 or 6.925 GHz using this relationship. It is shown that AMSR-E measurements with the RFI-correction algorithm have better agreement with simulations in a variety of surface conditions.

The MicroWave Radiation Imager(MWRI) onboard the FengYun(FY)-3B satellite has five frequencies at 10.65,18.7,23.8,36.5,and 89.0 GHz,each having dual channels at vertical and horizontal polarization states,respectively.It is found that radiofrequency interference(RFI) is present in MWRI data over land.The RFI signals are,in general,detectable from a spectral difference method and a principal component analysis(PCA) method.In particular,the PCA method is applied to derive RFI signals from natural radiations by using the characteristics of natural radiation measurements having all-channel correlations.In the area where data have a higher projection onto the first principle component(PC) mode,RFI is,in general,present.However,both the spectral and PCA methods cannot detect RFI reliably over frozen grounds and scattering surfaces,where the brightness temperature difference between 10.65 and 18.7 GHz is large.Thus,detection is improved through the use of normalized PCA.The new RFI detection algorithm is now working reliably for MWRI applications.It is found that RFI at 10.65 GHz distributes widely over Europe and Japan,and is less popular over the United States and China.

A detection of radio-frequency interference (RFI) in the space-borne microwave radiometer data is difficult under snow and sea ice-covered conditions. The existing methods such as a spectral difference technique or a principal component analysis (PCA) of RFI indices produce many false RFI signals near the boundary of Greenland and Antarctic ice sheets. In this paper, a double PCA (DPCA) method is developed for RFI detection over Greenland and Antarctic regions. It is shown that the new DPCA method is effective in detecting RFI signals in the C- and X-band radiometer channels of WindSat while removing the false RFI signals over Greenland and Antarctic. It also worked well in other snow-free or snow-rich regions such as winter data over the United States. The proposed DPCA can be applied to satellite radiometer data orbit-by-orbit or granule-by-granule and is thus applicable in an operational environment for fast processing and data dissemination.

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。

There are spread radio-frequency interference (RFI) problems of the low-frequency observations of the spaceborne microwave imagers, e.g. AMSR-E (Advanced Microwave Scanning Radiometer). A modified principal component analysis method was used to identify RFI and then its influence on the retrieval of surface parameters was studied. Next, the AMSR-E data corrected by linear fitting correction of RFI were used to retrieve the surface parameters through 1D-Var method. By comparing the retrieved products (e.g., land surface temperature and surface rainfall rate) before and after RFI correction over the United States, it was found that retrieved land surface temperature and surface rainfall rate which were interfered by RFI were abnormally high and with large deviations. Therefore, it was necessary to effectively identify and correct RFI prior to low-frequency observations with spaceborne microwave imagers to retrieve land surface parameters or assimilate these observations. And the modified principal component analyzing RFI identification method and linear fitting RFI correction algorithm were effective for the observations over land.

A 1-D variational system has been developed to process spaceborne measurements. It is an iterative physical inversion system that finds a consistent geophysical solution to fit all radiometric measurements simultaneously. One of the particularities of the system is its applicability in cloudy and precipitating conditions. Although valid, in principle, for all sensors for which the radiative transfer model applies, it has only been tested for passive microwave sensors to date. The Microwave Integrated Retrieval System (MiRS) inverts the radiative transfer equation by finding radiometrically appropriate profiles of temperature, moisture, liquid cloud, and hydrometeors, as well as the surface emissivity spectrum and skin temperature. The inclusion of the emissivity spectrum in the state vector makes the system applicable globally, with the only differences between land, ocean, sea ice, and snow backgrounds residing in the covariance matrix chosen to spectrally constrain the emissivity. Similarly, the inclusion of the cloud and hydrometeor parameters within the inverted state vector makes the assimilation/inversion of cloudy and rainy radiances possible, and therefore, it provides an all-weather capability to the system. Furthermore, MiRS is highly flexible, and it could be used as a retrieval tool (independent of numerical weather prediction) or as an assimilation system when combined with a forecast field used as a first guess and/or background. In the MiRS, the fundamental products are inverted first and then are interpreted into secondary or derived products such as sea ice concentration, snow water equivalent (based on the retrieved emissivity) rainfall rate, total precipitable water, integrated cloud liquid amount, and ice water path (based on the retrieved atmospheric and hydrometeor products). The MiRS system was implemented operationally at the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 for the NOAA-18 satellite. Since then, it has been extended to run for NOAA-19, Metop-A, and DMSP-F16 and F18 SSMI/S. This paper gives an overview of the system and presents brief results of the assessment effort for all fundamental and derived products.

Microwave Radiation Imager (MWRI) onboard the FengYun (FY)-3A satellite observes the Earth atmosphere at 10.65, 18.7, 23.8, 36.5 and 89.0 GHz with each having dual polarization. Its calibration system is uniquely designed with its main reflector viewing both cold and hot calibration targets. Two quasi-optical reflectors are used to reflect the radiation from hot load and cold space to the main reflector. However, some radiation loss in the beam transmission path must be taken into account in the calibration process. The loss factor in hot load transmission path is derived using the antenna pattern data measured on ground and satellite data observing over Amazon forest where the scene temperature is steady and close to hot target. The instrument non-linearity factors at different channels are also evaluated over a wide range of brightness temperatures and compared with the results from the ground vacuum test. After a cross-calibration with Windsat data, atmospheric products are derived from MWRI brightness temperatures and display reasonable accuracy and meet the overall requirements.