微波月亮-人类对月球的全新视角——中国“嫦娥一号”卫星微波探测仪若干探测结果

中国“嫦娥一号”探月卫星自2007年10月24日成功发射并于同年11月7日进入其工作轨道。在轨工作一年多,完成了全部使命,期间获取了大量的科学数据。其中“嫦娥一号”月球微波探测仪(Chang’e-1 Lunar Microwave Sounder-CELMS)已多次覆盖全月表面,首次获取了全月微波亮温分布数据,创建了“微波月亮”(Microwave Moon-MicM)。“微波月亮”的建立为月球科学研究、宇宙科学研究、月球资源研究及应用、未来月球基地的建立等带来了全新的信息,与“可见月亮”、“红外月亮”及其它相关探测结果(如X、γ谱仪,中子谱仪)、地基探测及未来月球轨道上观测和就位探测等多方信息的融合、分析,将大大提升人类对太空、月球及宇宙起源、生命起源等问题的认识和研究水平,在人类探月活动中具有里程碑意义。
在“嫦娥一号”卫星微波探测仪绕月探测之前,从来没有从月球轨道对全月球进行微波探测的活动。很多涉及月球微波特征研究,如月表微波亮温分布、月壤厚度及氦-3资源量分布信息、涉及月球历史等的研究多数是靠Apollo、Luna的落月点实测数据为依据,加上其它探测(如光学等)结果融合分析并逻辑延伸而得来的,因此其结果存在相当的多解或不确定性,这就使我们对月球微波辐射特性的真实情况了解很少,甚至可能有偏差。着重讨论“微波月亮”的含义,其相关信息内涵,几种特征区、点的分析等。根据微波探测仪的数据,获得了全月月壤厚度分布、氦-3资源量评估、全月亮温及其变化规律等研究结果,得出了一些与现今其它研究结果不同的结论。     

L波段微波辐射计冷空定标源亮温分析

欧空局SMOS任务的塔克拉玛干沙漠地面定标实验,采用L波段微波辐射计测量塔克拉玛干沙漠的土壤亮温。此辐射计的定标采用冷空作为低温定标源。由于冷空的情况比较复杂,在L波段,其亮温受到诸多因素的影响。根据日地关系、太阳与天线的位置关系,建立太阳辐射模型,得出了任意时刻太阳进入天线视场范围内的影响亮温值,并简要分析了月球、其它星球、中性氢原子分立谱发射和热源连续谱发射对辐射亮温的影响,为选取冷空作为低温定标源提供了理论及数据参考。     

基于Gradient Boosting算法的ERMS辐射数据预测

影响核辐射监测站点辐射监测HPIC剂量率实时数据准确性的组成因素多且复杂,如自然因素的降雨、温湿度、风向及太阳辐射等,客观因素的设备异常及放射性状况等;以致在实际应用中发现辐射监测状态异常时,很难分析出是什么原因导致的监测数据偏离.结合ERMS海量历史辐射序列监测数据,深入挖掘降雨、温湿度、气压、风向、太阳辐射天顶方向电子量及周边各站点辐射数值等特征因子集,基于Gradient Boosting算法（简称GB算法）建立起HPIC剂量率辐射数据的在线预测模型,有效融合自然特征因子,降低了自然因子对HPIC剂量率辐射监测数值异常的分析及判读的干扰作用,提高了对ERMS辐射异常发现的辅助判断能力及维保效率.     

月球表面微波主被动遥感的建模模拟与反演

探测月球和其它外星球是我国空间遥感与深空探测的又一轮新课题。研究地球遥感中星载被动遥感辐射计、主动遥感高分辨率合成孔径雷达等如何应用于月球和其它外星球的探测是一项十分有意义的工作。了解月球表层的月壤与月岩的物质状态及其分布,对于月球资源的科学认识、以及未来月球探测、登月与月球开发,以及其它外星球探测均具有十分重要的意义。先讨论微波被动遥感月球表面辐射的模拟和由微波辐射反演月壤厚度。由月球表面数字高程和月壤厚度实测点数据建立月球表面高度与月壤厚度的一种对应关系,构造整个月球表面月壤厚度的试验性分布。根据克莱门汀的紫外可见光光学数据,计算整个月球表面月壤中FeO+TiO2含量分布,给出整个月球表面月壤介电常数分布。由月球表层温度的观测结果以及月壤的导热特性,给出月尘层与月壤层温度随纬度分布的经验公式。在这些条件基础上,建立月尘、月壤、月岩3层微波热辐射模型。由起伏逸散定理,模拟计算月壤低耗散介质层多通道辐射亮度温度。以此辐射亮度温度模拟加随机噪声为理论观测值,按3层模型提出月壤层厚度反演方法。由于高频通道穿透深度小,由高频通道的辐射亮度温度按照两层尘-月壤微波热辐射模型反演月尘层与月壤层的物理温度。并以此为已知参数,由穿透深度较大的低频通道的辐射亮度温度反演月壤层厚度,对于反演的相对误差也进行了讨论。在研究微波主动遥感方面,提出低空飞行全极化L波段雷达窄脉冲探测月壤层厚与层结构的建议。此时采用一层具有上下随机粗糙界面的有耗 介质层月壤层模型,在下垫月岩粗糙界面上有一层随机分布的碎石散射体。推导了包含面散射、体散射,以及面体相互作用7种散射机制的全极化脉冲波Mueller矩阵解。以月壤特征参数(月壤层厚、FeO+TiO2金属含量、介电常数,界面粗糙度、碎石分布等)为函数,用时域Mueller矩阵解数值模拟来验证方法可行性。L波段窄脉冲极化回波波形能用于反演或估算月壤厚度与分层结构。     

基于3种机器学习法的太阳辐射模拟研究

定量模拟太阳辐射对认识黄土高原区气候变化至关重要,现有研究表明机器学习可以很好地模拟太阳辐射,但不同的机器学习法在不同区域模拟精度不同,为了实现黄土高原区太阳辐射数据的最优模拟,从而为农作物模型、水文模型以及气候变化模型提供精度更高的太阳辐射数据。基于随机森林（RF,Random Forest）、人工神经网络（ANN,Artificial Neural Network）和支持向量机（SVM,Support Vector Machine）3种机器学习法来模拟黄土高原地区的太阳辐射并对这3种算法进行比较研究,选取了2003~2009年14个辐射站点和2010~2016年10个辐射站点的实测数据和对应参数气压、云量、云光学厚度、臭氧、可降水水汽以及DEM、坡度、坡向作为模型的训练数据,随机选取2010~2016年4个辐射站点的太阳辐射实测数据对模拟结果进行验证。验证结果表明：RF模型在黄土高原及周边地区的模拟效果最优,平均偏差(MBE)为-0.17 MJ·m^-2,均方根误差(RMSE)为1.48 MJ·m^-2,拟合优度达到0.96。研究结果表明:RF模型与气象数据及遥感数据结合能够有效解决黄土高原无辐射观测区的太阳辐射模拟问题,对区域太阳辐射的研究具有重要意义。     

嫦娥二号工程月球辐射噪声影响研究

月球辐射噪声是月球探测中影响测控通信性能的重要因素,将直接影响地面测控系统的数据接收和测距测速性能.本文在借鉴国外相关领域研究的基础上,首次探讨了月球噪声对测控通信的影响机理和测试方法,建立了理论模型并进行了仿真分析.在嫦娥二号工程中,利用国内目前最大口径的18m测控系统对月球辐射噪声进行了长时间的测量,通过对试验数据和理论计算结果的对比分析,得到了月球辐射噪声在S频段和X频段对18m测控系统的影响规律,为我国后续月球与深空探测的测控与通信链路性能的设计提供了更精确的参考依据.     

空间微波环境对嫦娥一号微波探测仪在轨定标影响分析

嫦娥一号（CE-1）卫星利用微波探测仪（Chang'e Lunar Microwave Sounder-CELMS）对月球探测在国际上尚属首次。CELMS星上定标时采用冷空作为其低温定标源。当定标天线指向冷空获取宇宙背景辐射亮温时,宇宙中一些离月球距离较近的星体,如太阳、地球以及一些对月张角较大且辐射强度较高的射电源,如银心、天鹅座、金牛座等在某些时段均可进入其波束范围,是影响CELMS准确定标的主要因素。从分析CE-1的轨道入手,结合冷空星体的分布,研究了各星体相对于CELMS定标天线波束的入射角,进而得到了空间微波环境对CELMS探测结果的影响：太阳辐射对冷空天线的影响最大,其次是地球和银心,所有这些影响综合可达10 K量级。
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基于AMSR-E的全球陆表被动微波发射率数据集

地表微波发射率表征了地物向外发射微波辐射的能力,星载被动微波发射率估算可在宏观、大尺度上对陆表微波辐射进行整体表达,是被动微波地表参数定量反演中重要基础数据,也是在大尺度上获取陆表微波辐射特征的一种途径。本数据集利用搭载在Aqua卫星上的高级微波扫描辐射计(AMSR-E)和中分辨率成像光谱仪(MODIS)的同步观测特点,采用MODIS的地表温度和大气水汽产品数据作为输入,基于考虑大气影响的发射率估算模型,生产了全球晴空条件下AMSR-E传感器运行期间(2002年6月~2011年10月)的陆表多通道双极化微波瞬时发射率。通过产品低频无线电信号影响、数据间比对、分布统计、不同地表覆盖条件的发射率特征、频率依赖和相关性研究等开展验证性分析,结果表明:瞬时发射率的动态大、细节表达丰富,月内日变化标准差在0.02以内,其时空变化、频率依赖和相关性等符合微波理论分析和自然物理过程理解。此套数据集还包括AMSR-E全生命周期的全球陆表逐日、侯、旬、半月及月产品,可用于开展星载被动微波遥感模拟、陆面模型以及陆表温度、积雪、大气降水/水汽/可降水量等反演研究。     

尖端导体电晕放电试验研究

电晕放电是高压电力工程及航空航天领域研究的重点及难点问题;为研究尖端导体电晕放电的辐射特点,设计了电晕放电试验模拟系统;利用测试系统及天线在微波暗室内进行了不同测试距离、不同放电电压及不同尖端导体长度条件下的试验;通过对试验数据的分析可知,电晕放电辐射信号呈衰减振荡模式;电晕放电脉冲重复率随着充电电压的增大而增加;天线接收的辐射信号能量与测试距离成反比;随着充电电压的增加,信号辐射功率呈增加趋势;试验研究结果为输变电工程、航空航天装备的电磁环境分析提供参考。     

车载多频率微波辐射计与观测数据应用

地基微波辐射计是被动微波遥感研究中进行地面实验的核心仪器。了解辐射计的观测原理以及实验方法对实验的设计和数据的分析具有重要意义。2007年遥感科学国家重点实验室购置了车载多频率微波辐射计,自仪器投入使用以来,获取了多种典型地表的微波辐射测量数据。基于车载多波段微波辐射计开展的地面观测实验为被动微波遥感地表参数反演算法的发展、辐射传输模型的验证等研究提供了重要的数据支持。详细介绍了车载多频率微波辐射计的配置、观测原理和定标方法,并结合地面实验,介绍了利用车载多频率微波辐射计进行实验的方法及数据的处理与应用。该研究对地基微波辐射计观测实验的设计与实施以及对辐射计数据的分析和使用有重要的参考价值。     

A novel ground-based experimental observation method for determining multiband microwave radiative properties of forest litter

The layer of litter covering the forest floor attenuates microwave radiation coming from soil. In satellite remote-sensing data, this reduces the sensitivity of brightness temperature to land surface parameters (e.g. soil moisture, snow depth, and snow water equivalent), resulting in poorer inversion accuracy. To quantify the effects of microwave radiative properties of litter at different frequencies, and especially the impact on transmissivity, a novel approach was developed for modelling radiative transfer (RT) through litter. This approach is based on a zero-order RT model that accounts for scattering effects (the τ–ω model, τ is the optical thickness; ω is the single scattering albedo). Controlled ground-based experiments were conducted to obtain brightness temperatures at several frequencies (1.4, 18.7, and 36.5 GHz) as affected by the thickness and weight moisture content of the litter. The effects of measurement errors on transmissivity were then evaluated. This novel method, which is not only based on sound theory but also prevents calibration errors, can be used to obtain parameters such as the extinction coefficient and transmissivity. The results of this study provide new insights into the microwave RT theory of forest systems, allowing for more appropriate brightness temperatures corrections for satellites data, and providing a guide for controlled experiments.     

神舟4号飞船多模态微波遥感器地面配合试验

神舟4号飞船微波辐射计(RAD)是一个多波段被动微波传感器,用来测量海洋、大气和陆地表面的地球物理参数。为了对发射后RAD性能进行检验,我们于2002年底到2003年1月,在中国南海进行了神舟4号多模态微波遥感器地面配合试验。试验期间完成了微波辐射计海面同步多角度测量、海面水文气象参数测量以及大气气象探空测量。试验获得了大量宝贵的现场测试数据。对试验过程进行了简单的介绍,重点对于测量结果进行分析。同时将地面试验微波辐射计测量的结果与RAD测量的相应结果进行了比较。
     

Effects of noise on optimal deconvolution accuracy in microwave observations

Due to large footprints of remotely sensed microwave brightness temperatures, accuracy of microwave observations in areas of large surface heterogeneity has always been a technological challenge. Microwave observations in areas dominated by waterbodies typically exhibit observed brightness temperature several tens of kelvins lower than areas having no surface water. The non-linearity between brightness temperature and other geophysical quantities such as soil moisture makes the accuracy of microwave observations a critical element for accurate estimation of these quantities. In retrieving soil moisture estimates, an error of 1 K in remotely sensed microwave brightness temperatures results in about 0.5–1% error in volumetric soil moisture. Large uncertainties in the observed brightness temperatures make such observations unusable in areas of large brightness temperature contrast. In this article, we discuss a deconvolution method to improve accuracy using the overlap in the adjacent microwave observations. We have shown that the method results in improved accuracy of 40% in brightness temperature estimation in regions of high brightness temperature contrast.     

A comparative study of Chang and HUT models for UK snow depth retrieval

For the development of passive microwave remote sensing techniques, brightness temperature information on the medium covering the Earth's surface under different conditions is required. An emission model is a useful tool for the estimation of the brightness temperature of the medium. If the medium is a snow pack, the microwave radiation emitted will depend on the physical temperature, crystal characteristics, stratification and density of the snow. The parameters of microwave emission models available for the retrieval of snow characteristics are highly dependent on local environmental and climatological conditions. The aim of this study was to compare the empirical Chang model with the semiempirical radiative transfer model of snow developed at Helsinki University of Technology (HUT) for snow depth (SD) retrieval for UK snow packs. In the first step we used the HUT model. The root mean square error (RMSE) was used to validate the accuracy of model estimates. The snow events from different days in 1995, 1996 and 1997 were used in this study. In the second step a revised form of the Chang model, which was originally calibrated for global snow monitoring, was applied to estimate the SD. It is evident from this study that the Chang model underestimates the SD whereas the HUT model both underestimates and overestimates the SD for UK snow cover. This study also demonstrates that the application of algorithms for snow pack monitoring requires local calibration for effective and reasonable results.     

Estimation of snow depth in the UK using the HUT snow emission model

Snow cover characteristics have significant effects on upwelling naturally emitted microwave radiation through processes of forward scattering. This study simulates numerically the electromagnetic responses from snow in the UK using the radiative transfer‐based semiempirical model developed at the Helsinki University of Technology (HUT), which takes into account the influence of soil surface, forest canopy and atmosphere on space‐borne observed brightness temperature by using empirical and semiempirical formulas. A sensitivity analysis of the HUT model was conducted to determine the most sensitive parameter affecting upwelling radiation from snow in the UK. The model‐based results were compared with observed Special Sensor Microwave Imager (SSM/I) brightness temperatures to better understand the SSM/I response to snow. The available ensemble of data required for input to the HUT model comprise surface physical temperature, ground level pressure and water vapour content, forest stem volume and land cover water fraction. Based on the sensitivity analyses, numerical parameters representing physical snow pack quantities (e.g. snow grain size, snow moisture and snow depth (SD)) were varied and the method of root mean square error (RMSE) minimization was used to invert the SD. The HUT model was applied to different days in 3 months (23–31 January, 1–5 and 26–27 February and 1–7 March 1995) of records of daily SD and SSM/I observations. The results show that the HUT model both underestimates and overestimates SD prediction. For the month of January 1995, the HUT model underestimated SD with a bias of ?0.59 cm, whereas for February and March 1995 the HUT model overestimated the SD with a bias of 1.89 cm and 1.64 cm, respectively. This study demonstrates that microwave remote sensing of snow can be used successfully in the UK, where most research on snow cover is conducted by using a visible and infrared radiometer. It is also evident from this work that application of algorithms to snow pack monitoring needs local calibration for effective and reasonable results.     

Land cover-based optimal deconvolution of PALS L-band microwave brightness temperatures

An optimal deconvolution (ODC) technique has been developed to estimate microwave brightness temperatures of agricultural fields using microwave radiometer observations. The technique is applied to airborne measurements taken by the Passive and Active L and S band (PALS) sensor in Iowa during Soil Moisture Experiments in 2002 (SMEX02). Agricultural fields in the study area were predominantly soybeans and corn. The brightness temperatures of corn and soybeans were observed to be significantly different because of large differences in vegetation biomass. PALS observations have significant over-sampling; observations were made about 100 m apart and the sensor footprint extends to about 400 m. Conventionally, observations of this type are averaged to produce smooth spatial data fields of brightness temperatures. However, the conventional approach is in contrast to reality in which the brightness temperatures are in fact strongly dependent on land cover, which is characterized by sharp boundaries. In this study, we mathematically deconvolve the observations into brightness temperature at the field scale (500-800 m) using the sensor antenna response function. The result is more accurate spatial representation of field-scale brightness temperatures, which may in turn lead to more accurate soil moisture retrieval.     

Identification of clay pans from AMSR-E passive microwave observations

A new methodology to derive the spatial distribution of clay pans from satellite microwave data is presented. Soil moisture has a different temporal signature in clay pans compared with other soils, which is directly reflected in the satellite-observed brightness temperatures. Three years of Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) 6.9 GHz microwave observations were compiled and analysed over continental Australia to identify clay pans. This led to the development of a brightness temperature variance index (BTVI), which shows a strong spatial correspondence to an existing soil texture map and the ability to map clay pans for semi-arid regions. This simple method emphasizes the potential use of passive microwave remote sensing for soil type mapping.     

地形坡面对被动微波遥感影响的试验研究

地表起伏所形成的倾斜表面,特别是在山区,受地形坡度和坡向变化的影响,地表的微波辐射特征较之平坦地表发生明显变化。基于地基微波辐射地形试验,模拟星载被动微波辐射计AMSR\|E的观测参数,通过建立地形坡面的地貌微缩景观进行观测,探索地表斜坡对被动微波辐射特征的影响,用AIEM模型 和 Fresnel 方程分别模拟裸土地形坡面的微波辐射特征。结果表明,倾斜坡面对被动微波辐射的亮度温度产生了10~15 K的偏差,由坡度形成的本地入射角改变了地表的有效发射率,并随坡向的变化发生微波极化旋转。经试验数据和模型模拟结果对比,认为AIEM 在考虑了表面粗糙度影响时可以较好地模拟地形坡面的被动微波辐射特征。