利用微波辐射计AMSR-E的京津冀地区大气水汽反演

王永前, 施建成, 刘志红, 冯文兰

王永前, 施建成, 刘志红, 冯文兰. 利用微波辐射计AMSR-E的京津冀地区大气水汽反演[J]. 武汉大学学报 ( 信息科学版), 2015, 40(4): 479-486. DOI: 10.13203/j.whugis20130530
引用本文: 王永前, 施建成, 刘志红, 冯文兰. 利用微波辐射计AMSR-E的京津冀地区大气水汽反演[J]. 武汉大学学报 ( 信息科学版), 2015, 40(4): 479-486. DOI: 10.13203/j.whugis20130530

利用微波辐射计AMSR-E的京津冀地区大气水汽反演

基金项目: 国家自然科学基金资助项目(41471305,41301653,41301653);重庆市气象局开放式研究基金资助项目(Kfjj 201402);四川省杰出青年基金资助项目(2015JQ0037)
详细信息
    作者简介:

    王永前,博士,副教授。主要从事主被动微波遥感地表参数和大气参数反演研究。

  • 中图分类号: P208

  • 摘要: 目的 发展了微波遥感水汽反演算法,对于裸露地表,通过极化差比值形式消除地表信息对大气水汽反演的干扰;针对非裸露地表,首先反演了地表发射率并对不同波段地表发射率之间的关系进行分析,进而建立了非裸露地表上空大气水汽的反演算法。本文算法的反演结果与GPS探测结果的对比显示均方根误差为7.4mm,与MODIS大气水汽产品空间分布特征的对比也显示了两者较高的区域一致性。最后对京津冀平原地区和山地地区的水汽进行了时间序列的分析。
  • [1] WangYong,LiuLintao,XuHouze,etal.Retrie vingChangeofPrecipitableWaterVaporinChineseMainlandbyGPSTechnique[J].犌犲狅犿犪狋犻犮狊犪狀犱犐狀犳狅狉犿犪狋犻狅狀犛犮犻犲狀犮犲狅犳 犠狌犺犪狀犝狀犻狏犲狉狊犻狋狔,2007,32(2):152 155(王 勇,柳 林 涛,许 厚 泽,等.利 用GPS技术反演中国大陆水汽变化[J].武汉大学学报·信息科学版,2007,32(2):152 155)[2] NoeelS,Buchwitz M.Atmospheric WaterVaporAmountsRetrievedfrom GOMESatelliteData[J].犌犲狅狆犺狔犻犮犪犾犚犲狊犲犪狉犮犺犔犲狋狋犲狉狊,1999,26(13):1841 1844[3] ZhaoQiang,YangShizhi,QiaoYanli,etal.StudyofSimultaneousNon linearRetrievalofAtmospher icParametersandSurfaceSkinTemperaturefromMODISInfraredData[J].犌犲狅犿犪狋犻犮狊犪狀犱犐狀犳狅狉犿犪狋犻狅狀犛犮犻犲狀犮犲狅犳 犠狌犺犪狀犝狀犻狏犲狉狊犻狋狔,2009,34(4):400 403(赵强,杨世植,乔延利,等.利用MODIS红外资料反演大气参数以及表层温度的研究[J].武汉大学学报·信息科学版,2009,34(4):400 403)[4] Gao B C.Comparison of Column Water VaporMeasurementsUsingDownward lookingNear infra redandInfraredImagingSystemsandUpward loo king MicrowaveRadiometers[J].犑狅狌狉狀犪犾狅犳 犃狆狆犾犻犲犱犕犲狋犲狅狉狅犾狅犵狔,1992,31(10):1193 1201[8] GaoB C,Goetz A F,WestwaterE R.PossibleNear IRChannelsforRemoteSensingPrecipitableWaterVaporfromGeostationarySatellitePlatforms[J].犑狅狌狉狀犪犾狅犳 犃狆狆犾犻犲犱 犕犲狋犲狅狉狅犾狅犵狔,1993,32(12):1791 1801[5] AiresF,PrigentC,Rossow W B,etal.A NewNeuralNetworkApproachIncludingFirstGuessforRetrievalofAtmosphericWaterVapor,CloudLiq uidWaterPath,SurfaceTemperature,andEmissiv itiesoverLandfrom Satellite MicrowaveObserva tions[J].犑狅狌狉狀犪犾狅犳犌犲狅狆犺狔狊犻犮犪犾犚犲狊犲犪狉犮犺,2001,106(D14):14887 14907[6] LiuQ,WengF.One dimensionalVariationalRe trievalAlgorithm ofTemperature,Water Vapor,andCloudWaterProfilesfromAdvancedMicrowaveSoundingUnit(AMSU)[J].犐犈犈犈犜狉犪狀狊犪犮狋犻狅狀狊狅狀犌犲狅狊犮犻犲狀犮犲犪狀犱犚犲犿狅狋犲犛犲狀狊犻狀犵,2005,43(5):1087 1095[7] DeeterM N.ANewSatelliteMethodforRetrievingPrecipitableWaterVaporoverLandandOcean[J].犌犲狅狆犺狔狊犻犮犪犾犚犲狊犲犪狉犮犺犔犲狋狋犲狉狊,2007,34:L02815[9] WangH X,ZhangL,DawesW R.ImprovingWa terUseEfficiencyofIrrigatedCropsintheNorthChinaPlain measurementsandModelling[J].犃犵狉犻犮狌犾狋狌狉犲犠犪狋犲狉犕犪狀犵犿犲狀狋,2001,48:151 167[10]ChenK,WuT,TsangT,etal.EmissionofRoughSurfacesCalculatedbytheIntegralEquationMethodwith Comparison to Three dimensional MomentMethodSimulations[J].犐犈犈犈犜狉犪狀狊犪犮狋犻狅狀狊狅狀犌犲狅狊犮犻犲狀犮犲犪狀犱犚犲犿狅狋犲犛犲狀狊犻狀犵,2003,41:90 101[11]JacksonTJ,Cosh M H,BindlishR.AFive yearValidationofAMSR ESoilMoistureProducts[C].IEEEInternationalGeoscienceandRemoteSensingSymposium,Boston,USA,2008犘犪狊狊犻狏犲犕犻犮狉狅狑犪狏犲犚犲犿狅狋犲犛犲狀狊犻狀犵狅犳犘狉犲犮犻狆犻狋犪犫犾犲犠犪狋犲狉犞犪狆狅狉狅狏犲狉犅犲犻犼犻狀犵犜犻犪狀犼犻狀犎犲犫犲犻犚犲犵犻狅狀犅犪狊犲犱狅狀犃犕犛犚犈犠犃犖犌犢狅狀犵狇犻犪狀1,2,3 犛犎犐犑犻犪狀犮犺犲狀犵2 犔犐犝犣犺犻犺狅狀犵1 犉犈犖犌犠犲狀犾犪狀11 CollegeofEnvironmentalandResourceScience,ChengduUniversityofInformationTechnology,Chengdu610225,China2 StateKeyLaboratoryofRemoteSensingScience,InstituteofRemoteSensingApplications,ChineseAcademyofSciences,Beijing100101,China3InstituteofMeteorologyScience,ChongqingMeteorologicalBureau,Chongqing401147,China犃犫狊狋狉犪犮狋:Comparedwithvisible/infraredsensors,satellitedata basedpassivemicrowaveradiometerscouldprovideamorefeasiblemethodforretrievingprecipitablewatervapor(PWV).Thispaperpres entsaschemethatretrievesPWVoverBeijing Tianjin Hebeiregionusingsatelliteradiometermeas urementsfromadvancedmicrowavescanningradiometer(AMSR E).Forbaresurfaces,thepolariza tiondifferenceratio(PDR_WV)obtainedfrom23.8and18.7GHzwasfoundtobesensitivetoPWV.477武 汉 大 学 学 报 · 信 息 科 学 版2015年4月Forthesurfacecoveredbyvegetation,surfaceemissivitywasretrievedbyAMSR EwiththehelpoftheMODISatmosphericprofileproduct.Throughanalyzingthestatisticalrelationshipofemissivitypolarizationdifference,analgorithmforretrievingPWV wasbuilt.ComparedwiththeGPSresults,therootmeansquareerrorofouralgorithmis7.4mm.Regionalconsistencywasfoundbetweentheresultsfrom MODISandouralgorithm.犓犲狔狑狅狉犱狊:Beijing Tianjin HebeiRegion;Precipitablewatervapor;AMSR E;Polarizationdifference犉犻狉狊狋犪狌狋犺狅狉:WANGYongqian,PhD,associateprofessor,specializesinthetheoriesandmethodsofretrievingsurfaceandatmosphereparametersbyremotesensing.E mail:wyqq@cuit.edu.cn犉狅狌狀犱犪狋犻狅狀狊狌狆狆狅狉狋:TheNationalNaturalScienceFoundationofChina,Nos.41471305,41301653,41301653,theOpenResearchFundProgramofChongqing MeteorologicalBureau,No.Kfjj 201402,theProjectofPreeminentYouth FundofSichuanProrince,No.檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪檪2015JQ0037.(上接第465页)犜犺犲犜狉犻狆犾犲犆狅犾犾狅犮犪狋犻狅狀犫犪狊犲犱犉狌狊犻狅狀狅犳犐狀狊犻狋狌犪狀犱犛犪狋犲犾犾犻狋犲犚犲犿狅狋犲犛犲狀狊犻狀犵犇犪狋犪犳狅狉犛狀狅狑犇犲狆狋犺犚犲狋狉犻犲狏犪犾犡犝犑犻犪狀犺狌犻1 犛犎犝犎狅狀犵11 StateKeyLaboratoryofInformationEngineeringinSurveyingMappingandRemoteSensing,WuhanUniversity,Wuhan430079,China犃犫狊狋狉犪犮狋:Becauseoftheinsufficientaccuracyandspatialresolutionofsnowdepthproductsretrievedbypassivemicrowaveremotesensing,anew multi sourcesdatafusionapproachisdevelopedforre trievingsnowdepth.Thedatafromdifferentsourcescontainsvisible,passivemicrowavesatelliteandin situdata.Thedailyin situ,AMSR EandSSM/Iretrievedsnowdepthproductsareusedinthisstudy.First,combiningin situsnow depth,thesnow depthof Northern Xinjiangisestimatedthroughgeostatisticalanalysis.Thentheerrorvariancesofeachproductarecalculatedusingatriplecollocation(TC)method.Finally,thenewsnowdepthproductsareobtainedby mergingin situ,AMSR EandSSM/IsnowdepthdatainaleastsquarescriterionwheretheoptimalweightsofeachproductaredeterminedwiththeTC basederrorvariances.Themergedsnowdepthisvalidateda gainstin situsnowdepthandexhibitsahighercorrelationwithin situobservationsthanthatwitho riginalAMSR EandSSM/Isnowdepth.Theresultswithhigheraccuracydemonstratetheeffective nessofourapproach.犓犲狔狑狅狉犱狊:SnowDepth;AMSR E;SSM/I;TripleCollocation;LeastSquareMethod犉犻狉狊狋犪狌狋犺狅狉:XUJianhui,PhDcandidate,specializesinspatio temporaldataanalysisanddataassimilation.E mail:xujianhui306@163.com犆狅狉狉犲狊狆狅狀犱犻狀犵犪狌狋犺狅狉:SHU Hong,PhD,professor.E mail:shu_hong@whu.edu.cn犉狅狌狀犱犪狋犻狅狀狊狌狆狆狅狉狋:TheNationalNaturalScienceFoundationofChina,No.41171313;theKeyProjectofNationalNaturalScienceFoundationofChina,No.41331175;theOpenResearchFundoftheKeyLaboratoryofGeo informaticsofNationalAdministrationofSurveying,MappingandGeoinformation,No.201329;theHubeiProvincialNaturalScienceFoundationofChina,No.2014CFB725/ZRY2014000982.
  • 期刊类型引用(11)

    1. 李欢,万玮,冀锐,李国元,陈晓娜,朱思宇,刘宝剑,徐玥,罗增良,王胜蕾,崔要奎. 中国卫星遥感地表水资源监测能力分析与展望. 遥感学报. 2023(07): 1554-1573 . 百度学术
    2. 陈明,王永前,吴锡. 基于随机森林的AMSR2青藏高原有云地区水汽反演. 遥感信息. 2022(02): 84-90 . 百度学术
    3. 赵庆志,杜正,吴满意,姚宜斌,姚顽强. 利用多源数据构建PWV混合模型. 武汉大学学报(信息科学版). 2022(11): 1823-1831+1846 . 百度学术
    4. 刘备,王勇,娄泽生,占伟. CMONOC观测约束下的中国大陆地区MODIS PWV校正. 测绘学报. 2019(10): 1207-1215 . 百度学术
    5. 雷忠腾,江涛,崔相辉,颜明捷. TERRA/MODIS热红外通道陆地晴空大气可降水分裂窗反演. 测绘与空间地理信息. 2018(02): 98-101 . 百度学术
    6. 王勇,徐肖遥,刘严萍,李江波. 基于GPS的河北省冬春季节MODIS水汽校正模型研究. 大地测量与地球动力学. 2018(10): 1001-1004+1010 . 百度学术
    7. 刘礼杨,苏泳娴,陈修治,邵怀勇. 一种针对旱季与雨季差异的AMSR-E被动微波遥感地表温度反演经验模型. 热带地理. 2017(03): 434-442 . 百度学术
    8. 周爱明,鲍艳松,魏鸣,陆其峰. FY-3近红外与热红外资料大气柱水汽总量反演对比. 遥感技术与应用. 2017(04): 651-659 . 百度学术
    9. 王永前,施建成,王皓,冯文兰,王雁君. 基于多源遥感数据陆面大气水汽反演的物理统计算法研究. 中国科学:地球科学. 2016(01): 43-56 . 百度学术
    10. WANG YongQian,SHI JianCheng,WANG Hao,FENG WenLan,WANG YanJun. Physical statistical algorithm for precipitable water vapor inversion on land surface based on multi-source remotely sensed data. Science China(Earth Sciences). 2015(12): 2340-2352 . 必应学术
    11. 段苗苗,马盈盈,龚威,王伦澈. 利用微波辐射计观测计算云衰减. 武汉大学学报(信息科学版). 2015(12): 1606-1612 . 百度学术

    其他类型引用(6)

计量
  • 文章访问数:  820
  • HTML全文浏览量:  44
  • PDF下载量:  530
  • 被引次数: 17
出版历程
  • 收稿日期:  2013-09-04
  • 修回日期:  2015-04-04
  • 发布日期:  2015-04-04

目录

    /

    返回文章
    返回