HUT模型的改进及其雪深反演

基于风云-3B（FY-3B）卫星的微波成像仪（MWRI）数据对HUT模型（Helsinki university of technology snow emission model）进行验证,结果表明,无论是18.7 GHz还是36.5 GHz水平极化亮温,HUT模型模拟亮温都与MWRI亮温存在较大的偏差。因此,本文对消光系数进行了本地化改进,得到了改进的HUT模型（IMPHUT模型）。IMPHUT模型在18.7 GHz水平极化和36.5 GHz水平极化时的模拟亮温偏差分别为-0.91 K和-4.19 K,较原始的HUT模型模拟精度（偏差分别为14.03 K和-16.33 K）有很大提高。最后,利用遗传算法进行雪深反演,基于IMPHUT模型的雪深反演（偏差为-6.79 cm）优于HUT模型和Chang算法,反演与实测雪深具有较好的一致性。     

FY-3B微波成像仪海洋数据无线电干扰识别

中国风云3号B星(FY-3B)上的微波成像仪MWRI通过5个频率(10.65 GHz,18.7 GHz,23.8 GHz,36.5 GHz和89.0 GHz)的双极化通道对地球表面进行监测。研究表明,MWRI资料的低频波段数据中存在着无线电频率干扰(RFI)现象,这些污染信号对遥感数据和反演产品质量产生极大的影响。本文尝试使用多通道回归方法和双主成分分析(DPCA)方法识别MWRI的10.65 GHz水平通道亮温海洋区域中的RFI信号。结果表明,双主成分分析法可以有效地识别出海洋上的RFI信号。微波成像仪10.65 GHz水平通道亮温数据中的RFI信号主要分布在地中海等欧洲附近海域,也存在于美国、日本、澳大利亚等近岸地区。     

FY-3B微波成像仪资料的地理定位误差与订正

风云3号B星(FY-3B)上的微波成像仪(MWRI)通过10.65 GHz,18.7 GHz,23.8GHz,36.5 GHz和189.0 GHz5个频率的双极化通道对地球表面进行监测。自卫星发射至今,MWRI资料的地理定位误差还未进行深入研究。为了提高FY-3B MWRI L1级数据地理定位精度,基于海、陆响应的升、降轨亮温差理论NDM(Node Differential Method),通过卫星位置和速度矢量建立卫星姿态模型、采用非线性最优化方法估计卫星姿态偏差,进而对MWRI 89 GHz通道的地理定位误差进行分析与订正。结果表明,2015年1—9月份俯仰、滚动和偏航角度的平均偏差分别为-0.220°,0.068°和0.062°,对应沿轨误差大约3—4 km,跨轨误差小于1 km。定位误差订正后,地中海、澳大利亚区域海岸线附近的升降轨亮温差明显减小;观测亮温在红海和南美洲东南部区域的分布和海岸线更加吻合,定位精度得到明显提高。     

FY-3D微波成像仪非线性特征及计算方法优化

风云三号D星(FY-3D)微波成像仪采用10.65 GHz、18.7 GHz、23.8 GHz、36.5 GHz和89.0 GHz双极化通道对地表辐射信息进行测量,定标精度会直接影响遥感数据的准确性。影响定标精度的主要因素之一是成像仪系统的非线性特征,用物理参数-非线性系数表示。所以准确计算非线性系数是地面热真空定标试验的主要目的之一,在95-298 K范围内设置不同变温源温度点,每一个温度点对应一个非线性系数,这些非线性系数的平均值即是系统最终的非线性系数值。受系统原理和试验条件的限制,计算平均值时需要确定变温源温度的有效范围,目前使用的范围确定方法是设计师首先依据经验提出几组可能的温度范围,然后对比每个范围的定标结果,选出最优的温度范围。显然,这种方法很容易受到人为因素的影响,而且由于不具备遍历所有温度范围的试验条件,无法确定每个变温源温度点的有效性。为了准确获得非线性系数值,首先通过分析热真空定标试验数据证明非线性系数与场景亮温无关,然后提出了一种优化计算方法,采用t准则确定有效范围得到非线性系数值。对比优化前后两种方法的结果表明,新方法的非线性亮温拟合残差的平均值和标准差值以及非线性亮温的最大拟合值都减小了。由此可见,新方法在计算非线性系数时可以获得更准确的变温源温度范围,可以优化非线性亮温的拟合效果,并且将定标精度提高0.04 K,优化效果在10 GHz接收机以及环境温度较高时尤为明显。     

Microwave response of seasonal snow-cover measured by using a ground-based radiometer at 6.93 and 18.7 ghz frequencies and at dual polarization

Snow cover is an important variable for climatic and hydrologic models due to its effects on surface albedo, energy, and moisture budgets. Passive microwave sensors can be used to monitor temporal and spatial variations in large-scale snow cover parameters, avoiding problems of cloud cover and polar nights. In the present study, brightness temperature values were estimated (using calibration curves) for moist snow on natural and blackbody/metal surface. T_B response on snow depth, density, SWE and angular variation from nadir were measured and found that T_B decreases with increase of snow depth and with increase of angle from nadir. Empirical relations were used to estimate emissivity, dielectric constant and dielectric loss factor. It was observed that emissivity decreases with the increase of dielectric constant. The dielectric constant and dielectric loss factor both increases with the increase of density. Experiments were performed during winter of year 2005 at Dhundi and Solang (H.P.), India, using ground based passive microwave radiometer having 6.9 and 18.7 GHz antenna frequencies at dual polarization.     

FY-3C微波成像仪电视信号干扰识别和分析

由于卫星微波成像仪频率的非保护性,微波成像仪资料受到相近频率的无线电信号干扰影响,主要干扰源于洋面反射的静止电视卫星信号。为提高微波成像仪资料的准确性,尤其是洋面资料的可用性,对电视卫星信号干扰的检测和特征分析尤为重要。中国风云3号C星(FY-3C)上搭载了微波成像仪。本文使用标准化主成分分析法来识别FY-3C微波成像仪的电视卫星干扰信号,并分析电视卫星信号干扰的主要空间特征。研究表明,FY-3C微波成像仪中心频率为10.65 GHz和18.7 GHz的4个低频通道受到卫星信号干扰。10.65 GHz通道在欧洲海域英吉利海峡和地中海西部希腊沿岸地区受到电视卫星信号干扰,并且干扰信号位置和强度与FY-3C卫星和欧洲静止电视卫星相对空间位置有关;18.7 GHz通道主要受到美国电视卫星的干扰,在北美东、西沿岸海域都存在干扰信号。FY-3C微波成像仪在中国海域不受电信号干扰影响,主要因为中国静止电视卫星频率与微波成像仪各通道频率不一样。     

风云三号C星微波成像仪升降轨偏差问题分析及订正

风云三号(FY-3)的微波成像仪(MWRI)能够全天候获取全球大气水汽含量、云雨参数及海面温度等的空间分布,并可为数值天气预报提供初始场信息进而提高天气预报的准确性。但FY-3C MWRI O-B(O是卫星观测亮温,B是数值天气预报模式模拟亮温)偏差结果存在较大升降轨差异,严重制约了遥感信息的正确提取以及在数值天气预报模式中的业务同化应用。本文通过分析定标方程各参数:定标黑体物理温度、热反射镜背瓣亮温、热反射镜物理温度、冷空反射镜物理温度、接收通道温度、黑体观测计数值、冷空观测计数值、定标斜率、定标截距,并对定标方程各项进行敏感性分析,找出了引起MWRI升降轨偏差的主要原因是热反射镜的发射率异常增大引起的。经过不断调整MWRI的热反射镜发射率,使升轨O-B与降轨O-B的概率分布逐渐重合,初步估算了热反射镜发射率。本文的订正方法可指导未来仪器的发展,并为直接同化MWRI辐射数据提供了条件。     

Atmospheric correction of AMSR-E brightness temperatures for dry snow cover mapping

Differences between the brightness temperatures (spectral gradient) collected by the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) at 18.7 and 36.5 GHz are used to map the snow-covered area (SCA) over a region including the western U.S. The brightness temperatures are corrected to take into account for atmospheric effects by means of a simplified radiative transfer equation whose parameters are stratified using rawinsonde data collected from a few stations. The surface emissivity is estimated from the model, and the brightness temperatures at the surface are computed as the product of the surface temperature and the computed emissivity. The SCA derived from microwave data is compared with that obtained from the Moderate Resolution Imaging Spectroradiometer for both cases of corrected and noncorrected brightness temperatures. The improvement to the SCA retrievals based on the corrected brightness temperatures shows an average value around 7%.     

Qualitative and quantitative analysis of snow parameters using passive microwave remote sensing

Snow cover is an important variable for climatic and hydrologic models due to its effect on surface albedo, energy, and mass balance. Satellite observations successfully provide a global and comprehensive hemispheric-scale record of the short-term, as well as inter-seasonal variations in snow cover. Passive microwave sensors provide an excellent method to monitor temporal and spatial variations in large-scale snow cover parameters, overcoming problems of cloud cover. Using microwave remote sensing data, snow parameters (snow surface temperature, snow water equivalence, scattering index, emissivity, snow depth) have been retrieved to integrate with the snow cover simulation model developed by SASE for avalanche risk assessment on regional basis. Multispectral and multitemporal brightness temperature data obtained from the Special Sensor Microwave Imager (SSM/I), flown onboard the DMSP satellites, for the period November 2000 to April 2001 and from November 2001 to February 2002 have been analysed. A comparative data set on snow measurements and meteorological observations of a region covering large area of Pir-Panjal and the Greater Himalayan range, available on near real time basis from SASE field observatories were also used. Model calculations were carried out to study the effects of atmospheric transmission on the microwave radiation emitted from the snow covered and snow free ground and atmosphere. The sensitivity of combinations of the SSM/I channels at 19, 37 and 85 GHz, in both horizontal and vertical polarizations, in respect to snow depth, surface temperature of the snowpack have been carried out. Decision rule based algorithms are developed to identify snow cover and non-snow area.     

基于MEMLS模型的积雪深度反演方法

利用被动微波遥感反演积雪深度一直是积雪遥感领域中的研究热点。在现有的积雪深度反演算法中,NASA算法因其简洁、易于扩展的特点,成为应用最为广泛的算法。但NASA算法存在着一定不足:首先,由于NASA算法基于线性拟合得出,在应用到其他研究区域时需要对反演公式进行重新拟合,适用范围受到一定限制;其次,由于算法中引入的19GHz与37GHz的亮温差在雪深达到一定范围时会达到饱和,因此算法会低估积雪深度。本文针对现有反演算法的不足之处,结合蚁群智能算法的特点,发展了基于蚁群算法的积雪深度反演算法;此外,针对NASA算法中存在的雪深低估问题,引入了AMSR-E10.7GHz亮温数据,对算法进行了改进。利用MEMLS模型的模拟数据与AMSR-E辐射亮温数据对算法进行实验,并采用实测数据与AMSR-E雪水当量产品对算法的反演精度进行评价。结果表明,两种积雪深度反演算法均是可行的,反演精度与现有产品相比有较为明显的改进。     

全极化微波辐射计对环境参数敏感性分析

为衡量全极化微波辐射计各通道对不同环境参数的探测能力,在构建全极化微波辐射传输正演模型的基础上,采用敏感性分析的方法,在固定背景场条件下,对全极化微波辐射计不同频率、各极化通道亮温对海面风速、海面风向、海面温度、大气水汽含量和云中液态水含量等重要环境参数的敏感度进行了分析和量化计算,对正演模型仿真亮温、星载全极化微波辐射计Wind Sat实测亮温相对于真实背景场参数、NCEP分析场资料和TAO/TRITON浮标实测海面风场数据的敏感性进行了分析。分析结果验证了全极化微波辐射计对海面风场的观测能力,衡量了上述重要环境参数对星载全极化微波辐射计各通道的单独影响程度,为中国自主研制全极化微波辐射计时通道指标设计、环境参数反演通道选取及算法提供支持。     

Northern Hemisphere Snow-Covered Area Mapping: Optical Versus Active and Passive Microwave Data

Spaceborne passive microwave data have been available for the past 27 years, and have supported the development of several algorithms for the retrieval of snow water equivalent and snow depth that, in turn, can be used for mapping snow-covered areas. In contrast, only recently has the application of spaceborne active microwave instruments been investigated for remote sensing of snow on a global scale. This raises the question of whether a technique combining active and passive microwave data can improve the mapping of snow parameters with respect to techniques based solely on passive data. In this letter, we report results concerning the mapping of snow-covered area (SCA) in the Northern Hemisphere between the years 2000 and 2004 derived from the combination of the brightness temperatures at 19.35 and 37 GHz measured by the Special Sensor Microwave Imager Radiometer with backscatter coefficients at 13.4 GHz measured by the NASA's QuickSCAT. SCA derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) is used as a reference to evaluate the performance of the microwave-based techniques and their combination. Results show that, generally, the technique using passive data provides better agreement with MODIS SCA than the technique using only scatterometer data. However, the results when both datasets are used show considerable improvement, demonstrating the potential benefits of a multisensor approach     

Development of an inversion algorithm for dry snow density estimation and its application with ENVISAT-ASAR dual co-polarization data

Radar remote sensing has great potential to determine the extent and properties of snow cover. Availability of space-borne sensor dual-polarization C-band data of environmental satellite- (ENVISAT-) advanced synthetic aperture radar (ASAR) can enhance the accuracy in measurement of snow physical parameters as compared with single polarization data measurement. This study shows the capability of C-band synthetic aperture radar (SAR) data for estimating dry snow density over snow covered rugged terrain in Himalayan region. The snow density is an important parameter for the snow hydrology and avalanche forecasting related studies. An algorithm has been developed for estimating snow density, based on snow volume scattering and snow-ground scattering components. The radar backscattering coefficients of both horizontal–horizontal (hh) and vertical–vertical (vv) polarization and incidence angle are used as inputs in the algorithm to provide the snow dielectric constant which can be used to derive snow density using Looyenga's semi-empirical formula. Comparison was made between snow density estimated from algorithm using ENVISAT-ASAR hh and vv polarization data and the measured field value. The mean absolute error between estimated and measured snow density was found to be 0.024 g/cm³.     

神舟四号飞船微波辐射计定标和检验（Ⅰ）——微波辐射计外定标

文中针对神舟四号飞船辐射计 (RAD)的在轨定标特点 ,提出一种替代外定标方案 ,即利用地球表面典型地物的微波辐射对RAD的不同通道重新进行定标。选取了全球大洋开阔海域的浮标、星载微波辐射计产品和小岛上气象探空数据 ,通过理论计算得出的亮温作为辐射计测量的低端。利用南美洲热带雨林亮温作为高端 ,重新给出了RAD定标方程。同时 ,为了确定定标的效果 ,分别与国外星载微波辐射计相同或相近通道的亮温进行了比较。另外还结合南海地面配合试验的测量结果对辐射计定标数据进行了分析。     

基于被动微波遥感的青藏高原雪深反演及其结果评价

采用修正的张氏雪深反演算法，用ＳＳＭ／Ｉ３７ＧＨz和19GHz水平极化亮温值计算了青藏高原及其毗邻地区的积雪深度，对其精度进行了评价，并对误差来源进行了分析，结果显示，此算法能够较好地反映研究区的雪深分布，但局部地区误差较大，总体上雪深被高估，其误差主要来源于冻土，深霜层，植被以及雪层中液态水含量，雪粒的形状和粒径的变化带来的影响，SSM／I数据较低的分辨率和研究区复杂的地形使反演的雪深与观测的雪深缺少可比性，给精度的评价带来影响。     

北极一年海冰表面积雪深度遥感反演与时序分析

北极海冰表面的积雪深度是重要的地球物理变量, 是研究物质与能量平衡、计算海冰厚度的重要参数。为减小不同被动微波传感器观测数据的系统误差, 对国防气象卫星计划(Defense Meteorological Satellite Program, DMSP)F17-SSMIS与F13-SSM/I重叠期亮度温度数据进行交叉定标, 建立4个频段48个月尺度定标模型, 并与传统年尺度定标模型进行比较和优选, 在此基础上估算并分析2003-2014年北极一年海冰表面积雪深度变化。结果表明:19H、19V、22V、37V频段1~5月的月尺度模型决定系数高于传统年尺度拟合模型; 2003-2014年, 北极一年海冰表面积雪深度总体呈现下降趋势, 同时积雪深度存在明显的周期性变化, 每年7月积雪深度最小, 9月最大; 东西伯利亚海、拉普捷夫海和巴伦支海海冰表面积雪深度呈现减少的趋势。     

TMI被动微波遥感资料用于地表洪涝特征分析试验

星载被动微波遥感资料为云天条件下地表洪涝特征分析提供了可能。微波极化比 (PR)可以有效刻画地表洪涝特征 ,宏观反映地表湿度信息。正演模拟分析结果表明地表粗糙度、地表植被覆盖状况和地表湿度对上行微波亮温有影响 ;TRMM/TMI低频微波通道微波极化比能减弱大气因素的影响 ,清晰反映地表的洪涝特征。合理确定分类阈值 ,对 1 998年长江流域洪涝区进行地表洪涝特征分析 ,可以在TMI像元尺度 ,将洪涝区分类为不同等级。洪涝区分类结果与经过天 -地校验过的机载和星载SAR资料地表洪涝分类实况对比 ,TRMM/TMI低频微波通道微波极化比 1 0GHz的PR1 0和 1 9GHz的PR1 9的分类正确率不低于 75 %。     

星载微波SSM/Ⅰ遥感在中国东北华北农田的辐射特征分析

国家遥感应用工程技术研究中心ＮａｔｉｏｎａｌＥｎｇｉｎｅｅｒｉｎｇＲｅｓｅａｒｃｈＣｅｎｔｅｒｆｏｒＧｅｏｍａｔｉｃｓ（ＮＣＧ）国家遥感应用工程技术研究中心于１９９６年１２月２５日由国家科委正式批准组建（国科发计字［１９９６］６０３号文件）。中国科学...     

星载微波SSM/Ⅰ遥感在中国东北华北农田的辐射特征分析

本文研究了星载微波ＳＳＭ／Ⅰ１９９６年在中国东北华北平原农田上７个通道辐射亮度温度（ＴＢ）的遥感数据,提出用几个通道ＴＢ组合的散射指数和极化指数来分析中国平原地区农田的微波辐射特征,及其随生长季节的时间性变化。星载ＳＳＭ／Ⅰ数据可以监视农作物的生长和平原地区地面湿度的变化。本文还给出了大气和农作物地表矢量辐射传输的数值模拟结果。     

Synergy of in situ and space borne observation for snow depth mapping in the Swiss Alps

The Swiss Federal Institute for Snow and Avalanche Research in Davos (SLF) provides snow depth maps for Switzerland on a spatial resolution of 1 km × 1 km. These snow depth maps are derived from snow station measurements using a spatial interpolation method based on the dependency of snow depth and altitude. During a winter season the number of operating snow stations varies and the area-wide snow depth interpolation becomes increasingly difficult in spring. The objective of the study is to develop an operational and near-real time method to calculate snow depth maps using a combination of in situ snow depth measurements and the snow cover extent provided from space borne observations. The operational daily snow cover product obtained from the polar-orbiting NOAA-AVHRR satellite is used to gain an additional set of virtual snow stations to densify the in situ measurements for an improved spatial interpolation. The capacity of this method is demonstrated on selected days during winter 2005. Cross-validation tests are conducted to examine the quantitative accuracy of the synergetic interpolation method. The error estimators prove the decrease in error variance and increase of overall accuracy pointing out the high capacity of this new interpolation method that can be run in near real-time over a large horizontal domain at high horizontal resolution. A solid method for snow–no snow classification in the processing of the satellite data is essential to the quality of the snow depth maps.