基于FY-3B微波成像仪的海面风速反演

风云三号B星微波成像仪的10.65、18.7、23.8和36.5 GHz频点对海表面粗糙度和介电特性比较敏感,能够用于海面地球物理参数的反演。为获得一种适用于全球大部分海域的海面风速反演算法,利用快速辐射传输模式和再分析大气廓线库模拟微波成像仪海面微波辐射特性,在此基础上建立了半经验反演算式,并利用浮标现场测量数据及WindSat全极化辐射计风速产品对半经验算法和经验算法分别进行了验证和对比。另外,通过分析风向对风速反演的影响,借助AT BV-TBH模型,尝试利用查找表算法对风向造成的晴空区域风速反演偏差进行初步校正。校正风向误差后,反演风速与浮标风速的均方根误差为0.9775 m/s。     

风云三号微波成像仪积雪参数反演算法初步研究

选择新疆地区作为实验区,为风云三号(FY-3)微波成像仪(MWRI)发展中国区域的积雪参数半经验反演算法。使用2003年4个月的新疆地区的台站观测资料和AMSR-E 18.7 GHz,36.5GHz和89 GHz水平和垂直极化亮温作为FY-3 MWRI的模拟数据,在Chang建立的半经验模型的基础上,采用多元线性回归分析,建立一个新算法。用已有方法去除水体、降雨、湿雪、冻土的像元后,用新算法反演了新疆地区的2004年1月的积雪参数,并分别与AMSR-E雪水当量产品和台站观测值进行比较,结果表明新算法在新疆地区优于AMSR-E的反演算法。     

风云三号微波大气探测载荷辐射定标

风云三号气象卫星是我国第二代极轨业务气象卫星,具有全球、全天候和多光谱探测能力,装载有微波温度计和微波湿度计2台微波大气探测载荷,自2008年首发星升空后,风云三号微波大气探测载荷资料在防灾减灾和数值天气预报同化应用中发挥了积极作用。风云三号气象卫星微波载荷辐射定标是从原始观测计数值出发获取目标亮温的数据处理技术过程,包括发射前定标、在轨星上定标、综合辐射定标和历史资料再定标4个技术环节。精确的辐射定标是星载被动微波辐射计遥感资料定量应用的基础。本文综述了风云三号气象卫星微波大气探测载荷综合辐射定标技术,介绍了风云三号微波大气探测载荷综合辐射定标基本原理及技术现状,展望了风云气象卫星微波大气探测载荷综合辐射定标技术未来发展。     

FY-3B微波成像仪图像质量评价

2010年11月5日发射升空的我国新一代极轨气象卫星FY-3B (“风云三号”B星)携带的微波成像仪,可以全天候获取来自地球表面和大气的电磁辐射信息。针对在轨测试期间微波成像仪1.7 s和1.8 s两种扫描周期,详细比较了二者图像质量的差异。在图像质量评价研究中,使用统计方法比较了图像的动态范围;通过功率谱计算,对比了图像的空间纹理特征;通过信息熵计算,分析了图像信息量的不同;此外,还研究了图像的对比度以及通道间配准的情况。结果表明:1.7 s图像的空间纹理结构、高频图像的对比度以及通道间配准明显优于1.8 s,表明1.7 s扫描周期下的图像质量优于1.8 s,该结论可以作为微波成像仪仪器指标设计的参考。     

风云三号卫星微波湿度计成功完成在轨运行二年考核

2008年5月27日,风云三号气象卫星（FY-3A星）在太原卫星发射中心发射升空。其中,由中科院空间中心承担的国家863计划微波湿度计于6月4日正式开机工作,至2010年6月4日已成功在轨运行2年,这标志着FY-3A星微波湿度计的在轨考核成功。     

FY-3C MWRI在轨交叉辐射定标

精确辐射定标是定量遥感的基础。以搭载在全球降水测量(Global Precipitation Measurement,GPM)卫星上的微波成像仪(GPM Microwave Imager,GMI)为辐射基准,用双差异(Double Difference,DD)方法对搭载在我国风云三号C星(Fengyun 3C,FY-3C)上的微波成像仪(Microwave Radiation Imager,MWRI)进行在轨交叉辐射定标。首先,将FY-3C MWRI数据、GMI数据和第五版欧洲中尺度天气预报中心再分析(European Centre for Medium-Range Weather Forecast Re-Analysis V5,ERA5)数据重采样至1°×1°的全球规则格网空间;其次,根据匹配条件收集晴空海面上的匹配观测点,用海洋微波辐射传输模型分别模拟FY-3C MWRI和GMI各个通道大气顶亮温;然后,根据匹配的观测值和模拟值计算DD值和FY-3C MWRI的理论观测值;最后,确定交叉辐射定标系数,并完成对FY-3C MWRI数据的定标重处理。结果表明:相对于GMI,FY-3C MWRI观测值被低估,特别是低频通道,但随着频率的增大,定标误差逐渐变小。FY-3C MWRI升轨(MWRIA)的定标误差比降轨(MWRID)小1.0～2.0 K。在全球天基交叉辐射定标系统(Global Space-based Inter-Calibration System,GSICS)所定义的标准场景亮温下,对于10V/H、18V/H、23V、36V/H和89V/H共9个通道,MWRIA的辐射定标误差分别为-6.7±0.3 K、-8.7±0.7 K、-2.9±0.7 K、-2.0±0.8 K、-2.4±0.7 K、-4.0±0.8 K、-2.4±1.4 K、-1.3±1.0 K和-0.4±1.8 K;而MWRID的辐射定标误差分别为-7.9±0.7 K、-9.7±0.9 K、-4.3±0.9 K、-3.0±0.8 K、-3.5±0.9 K、-5.1±0.8 K、-3.0±1.1 K、-2.4±0.6 K和-1.0±2.1 K。     

移动通讯信号对星载微波辐射计的影响分析-以风云三号气象卫星微波湿度计为例

以风云三号气象卫星微波湿度计为例，通过手机信号对183.31GHz接收机的干扰实验，找到了干扰途径，进行了机理分析，并定量分析了移动通讯基站对在轨运行的星载微波辐射计的影响，给出在星载微波辐射计中预防和解决干扰的措施，并进行了实验验证。
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华北积雪冻融循环微波辐射特征研究

积雪冻融循环监测是陆表水文过程和冰雪自然灾害研究的重要方面。被动微波遥感由于具有对水分敏感、高时间分辨率的特点,尤其适合大尺度的积雪冻融监测及相关参数的反演。该研究于2012年11月6日~27日在河北怀来遥感综合实验站使用车载多频率微波辐射计TMMR观测了积雪冻融循环微波辐射特征。研究发现,36.5GHz的观测亮温对积雪的冻融循环最敏感,18.7GHz次之,融化和冻结的时亮温差别可分别约达80K和60K;HUT单层和多层积雪微波辐射模型对18.7GHz和36.5GHz的模拟亮温能够基本反映冻融循环过程中的亮温变化;多层模型更适合模拟冻融循环的过程,18.7GHz和36.5GHz在V极化的相关系数均为0.97;冻融循环研究中,冰壳、冰层粒径的观测、雪湿度的观测和湿雪介电常数模型仍有待进一步改善。     

利用归一化微波差异指数和表面散射模型反演裸露地表土壤水分

在给定土壤质地和粗糙度状况条件下,用AIEM模型模拟AMSR-E的6.925GHz、10.65GHz和18.7GHz频率下不同含水量时土壤表面发射率和土壤温度的关系,分析表明V极化的发射率受土壤温度的影响很小,其变化主要由土壤水分的变化引起。通过计算不同频率组合V极化通道的归一化微波差异指数,并模拟与土壤水分的关系,然后利用这一关系对塔克拉玛干沙漠中部某地的土壤水分进行反演。结果发现用18.7GHz和10.65GHz V极化通道组合的反演值与AMSR-E Level 3土壤水分产品的吻合程度最好。在此基础上分别用3种常见的半经验表面散射模型:Q/H模型、Hp模型和Qp模型,通过计算上述通道组合的NMDI来反演研究区的土壤水分,结果表明利用3种半经验模型得到的反演值之间差异非常小,并且与用AIEM模型计算NMDI时的反演结果吻合较好。     

封面说明

封面图是神舟4号飞船搭载的多模态微波遥感器中微波辐射计在2003年1-4月间获取数据经处理后所形成的图像。 多模态微波辐射计共有5个频率即6.6GHz、13.9GHz、19.35GHz、22.235GHz和37.0GHz。其中除22.234GHz水汽通道为单极化外,其余频率均为双极化。局部入射角为42.6°。该辐射计的频率选择可以适用于多种用途,如6.6GHz、13.9GHZ和19.35GHZ适合于反演地表参量如土壤水份、植被含水量和地表温度等,而高频范围如22.235GHz、37.0GHz可用于检测大气水汽含量,液水含量及地物分类等。同时该辐射计的频率选择与搭载于Aqua上的AM-     

Global surface soil moisture from the Microwave Radiation Imager onboard the Fengyun-3B satellite

Soil moisture retrievals from China’s recently launched meteorological Fengyun-3B satellite are presented. An established retrieval algorithm – the Land Parameter Retrieval Model (LPRM) – was applied to observations of the Microwave Radiation Imager (MWRI) onboard this satellite. The newly developed soil moisture retrievals from this satellite mission may be incorporated in an existing global microwave-based soil moisture database. To reach consistency with an existing data set of multi-satellite soil moisture retrievals, an intercalibration step was applied to correct brightness temperatures for sensor differences between MWRI and the radiometer of the Tropical Rainfall Measuring Mission’s (TRMM’s) Microwave Imager (TMI), resulting from their individual calibration procedures. The newly derived soil moisture and vegetation optical depth product showed a high degree of consistency with parallel retrievals from both TMI and WindSat, the two satellites that are observing during the same time period and are already part of the LPRM database. High correlation (R > 0.60 at night-time) between the LPRM and official MWRI soil moisture products was shown over the validation networks experiencing semiarid climate conditions. The skills drop below 0.50 over forested regions, with the performance of the LPRM product slightly better than the official MWRI product. To demonstrate the promising use of the MWRI soil moisture in drought monitoring, a case study for a recent and unusually dry East Asian summer Monsoon was conducted. The MWRI soil moisture products are able to effectively delineate the regions that are experiencing a considerable drought, highly in agreement with spatial patterns of precipitation and temperature anomalies. The results in this study give confidence in the soil moisture retrievals from the MWRI onboard Fengyun-3B. The integration of the newly derived products into the existing database will allow a better understanding the diurnal, seasonal and interannual variations, and long-term (35 year) changes of soil moisture at the global scale, consequently enhancing hydrological, meteorological, and climate studies.     

Soil Moisture Retrieval over Vegetated Areas based on Sentinel-1 and FY-3C Data

This study aims to develop soil moisture retrieval model over vegetated areas based on Sentinel-1 SAR and FY-3C data.In order to remove vegetation effect,the MWRI data from FY-3C was applied to establish the inversion model of vegetation water content.The model was combined with the original water-cloud model,and developing a soil moisture retrieval model by combining active and passive microwave remote sensing data.Finally,the experiment of the soil moisture retrieval was conducted in Jiangsu and Anhui province,and validating the inversion accuracy of soil moisture by measured data.The results showed that：①For the vegetation-covered surface,the Microwave Polarization Difference Index obtain from FY-3C/MWRI was suitable for removing vegetation effect.②Compared with the Sentinel-1 VH polarization data,the backscattering coefficient of VV polarization was more suitable for soil moisture retrieval and get a higher accuracy of soil moisture retrieval.③Sentinel\|1 data can obtain high precision soil moisture estimation results,and the correlation coefficient between the estimated and measured soil moisture is 0.561 2 and RMSE is 0.044 cm³/cm³.     

Further validation of a new methodology for surface moisture and vegetation optical depth retrieval

A series of validation studies for a recently developed soil moisture and optical depth retrieval algorithm is presented. The approach is largely theoretical, and uses a non-linear iterative optimization procedure to solve a simple radiative transfer equation for the two parameters from dual polarization satellite microwave brightness temperatures. The satellite retrievals were derived from night-time 6.6?GHz Nimbus Scanning Multichannel Microwave Radiometer (SMMR) observations, and were compared to soil moisture data sets from the USA, Mongolia, Turkmenistan and Russia. The surface temperature, which is also an unknown parameter in the model, is derived off-line from 37?GHz vertical polarized brightness temperatures. The new theoretical approach is independent of field observations of soil moisture or canopy biophysical measurements and can be used at any wavelength in the microwave region. The soil moisture retrievals compared well with the surface moisture observations from the various locations. The vegetation optical depth also compared well to time series of Normalized Difference Vegetation Index (NDVI) and showed similar seasonal patterns. From a global perspective, the satellite-derived surface soil moisture was consistent with expected spatial patterns, identifying both known dry areas such as deserts and semi-arid areas and moist agricultural areas very well. Spatial patterns of vegetation optical depth were found to be in agreement with NDVI. The methodology described in this study should be directly transferable to the Advanced Microwave Scanning Radiometer (AMSR) on the recently launched AQUA satellite.     

Downscaling of passive microwave soil moisture retrievals based on spectral analysis

The retrieval of soil moisture from passive microwave remote-sensing data is presently one of the most effective methods for monitoring soil moisture. However, the spatial resolution of passive microwave soil moisture products is generally low; thus, existing soil moisture products should be downscaled in order to obtain more accurate soil moisture data. In this study, we explore the theoretical feasibility of applying the spectral downscaling method to the soil moisture in order to generate high spatial resolution soil moisture based on both Moderate Resolution Imaging Spectroradiometer and Fengyun-3B (FY3B) data. We analyse the spectral characteristics of soil moisture images covering the east-central of the Tibetan Plateau which have different spatial resolutions. The spectral analysis reveals that the spectral downscaling method is reliable in theory for downscaling soil moisture. So, we developed one spectral downscaling method for deriving the high spatial resolution (1 km) soil moister data from the FY3B data (25 km). Our results were compared with the ground truth measurements from 15 selected experimental days in 16 different sites. The average coefficient of determination (R²) of the spectral downscaling increased nearly doubled than that of the original FY3B soil moisture product. The spectral downscaled soil moister data were successfully applied to examine the water exchange between the land and atmosphere in the study regions. The spectral downscaling approach could be an efficient and effective method to improve the spatial resolution of current microwave soil moisture images.     

A combined passive/active microwave remote sensing approach for surface variable retrieval using Tropical Rainfall Measuring Mission observations

The backscattering and emission measured simultaneously by radar and radiometer show promise for the estimation of surface variables such as near-surface soil moisture and vegetation characteristics. In this paper, the 10.7 GHz Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) channel and 13.8 GHz precipitation radar (PR) observations are simultaneously used for the estimation of the near-surface soil moisture and vegetation properties. The Fresnel model for soil and a simple model for vegetation are used to simulate the passive microwave emission at 10.7 GHz. To determine the PR backscatter signal from a land surface, a theoretical approach is used based on the Geometric Optics Model for simulating bare soil and a semi-empirical water-cloud model for vegetation. The model parameters required in specifying the nature of the soil and vegetation are calibrated on the basis of in situ soil moisture data combined with remotely sensed observations. The calibrated model is subsequently used to retrieve near-surface soil moisture and leaf area index for assumed values of surface roughness and temperature. Algorithm assessment using synthetic passive and active microwave data shows a nonlinearity effect in the system inversion, which results in a varying degree of error statistics in soil wetness and vegetation characteristics retrieval. The technique was applied on TRMM radar/radiometer observations from three consecutive years and evaluated against in situ near-surface (5 cm) soil moisture measurements from the Oklahoma Mesonet showing a consistent performance.     

Passive microwave sensing of soil moisture content: The effects of soil bulk density and surface roughness

Microwave radiometric measurements over bare fields of different surface roughness were made at frequencies of 1.4 GHz, 5 GHz, and 10.7 GHz to study the frequency dependence, as well as the possible time variation, of surface roughness. An increase in surface roughness was found to increase the brightness temperature af soils and reduce the slope of regression between brightness temperature and soil moisture content. The frequency dependence of the surface roughness effect was relatively weak when compared with that of the vegetation effect. Radiometric time-series observations over a given field indicate that field surface roughness might gradually diminish with time, especially after a rainfall or irrigation. The variation of surface roughness increases the uncertainty of remote soil moisture estimates by microwave radiometry. Three years of radiometric measurements over a test site revealed a possible inconsistency in the soil bulk density determination, which is an important factor in the interpretation of radiometric data.     

Improving Soil Moisture Estimation by Joint Assimilationof MODIS Land Surface Temperature and Airborne L band Microwave Brightness Temperature

In this work,a novel soil moisture data assimilation scheme was developed,which was based land surface model (CoLM,Common Land Model),microwave radioactive transfer model (L MEB,L band Microwave Emission of the Biosphere),and data assimilation algorithm (EnKS,Ensemble Kalman Smoother).This scheme is used to improve the estimation of soil moisture profile by jointly assimilatingMODIS land surface temperature and airborne L band passive microwave brightness temperature.The ground based data observed at DAMAN superstation,which is located at Yingke oasis desert area in the middle stream of the Heihe River Basin,are used to conduct this experiment and validate assimilation results.Three LAI products are used to analyze the influence of LAI on soil temperature.Three assimilation experiments are also designed in this work,including assimilation of MODIS LST,assimilation of microwave brightness temperature,and assimilation of MODIS LST and microwave brightness temperature.The results show that the uncertainties in LAI influence significantly soil temperature simulations in different soil layers.MODIS LAI product is seriously underestimated in this study area,which results soil temperature overestimation about 4~6 K.Three assimilation schemes can improve soil moisture estimations to different extend.Joint assimilation of MODIS LST and microwave brightness temperature achieved the best performance,which can reduce the RMSE of soil moisture to 31%~53%.