城市遥感 期末论文.doc

城市遥感 期末论文 Reconstruction andanalysis oftemporal and spatial variations in surface soil moisture in Chinausing remote sensing(1Ministry ofEducation KeyLaboratory for Earth SystemModeling,and CenterforEarthSystem Science,Tsinghua University,Beijing100084,China;2The Institute of Remote Sensing Applications,Chinese Academyof Sciences,Beijing100101,China ReceivedNovember1,xx;aepted January10,xx;published onlineMarch31,xx)Abstract:An ensemblemethod was used tobine threesurface soil moisture products,retrieved frompassive microwave remote sensing data,to reconstructa monthlysoil moisture data setfor China betweenxxandxx.Using the ensemble data set,the temporal and spatial variations ofsurface soil moisture were analyzed.The majorfindings were: (1)The ensemble data setwas able to providemore realisticsoil moisture information than individual remote sensing products; (2)during the study period,the soil moisture increasedin semiarid regions anddecreased inarid regions with anoveralldrying trendfor the whole country; (3)the soil moisture variation trends derived from the three retrievalproducts and the ensemble data differ from each other butall data sets showthe dominantdrying trendfor thesummer,and thatmost of the dryingregions werein major agricultural areas; (4)-pared with the precipitationtrends derived from GlobalPrecipitation ClimatologyProject data,it isspeculated thatclimate changeis apossible causefor the drying trend in semiarid regions and the wettingtrend inarid regions;and (5)bining soil moisture trendswith land surface temperature trends derivedfrom Moderate Resolution ImagingSpectroradiomete,the study domain wasdivided intofour categories.Regions with drying and warming trends cover33.2%,the regions with drying and cooling trends cover27.4%,the regionswith wetting andwarming trends cover21.1%and the regionswith wetting and coolingtrendscover18.1%.The firsttwo categoriesprimarily cover the majorgrain producingareas,while thethird categoryprimarily coversnona-rable areassuch asNorthwest Chinaand Tibet.This impliesthat themoisture andheat variation trends in China areunfavorable tosustainable developmentand ecologyconservation.surface soil moisture,passive microwaveremote sensing,temporal and spatial characteristics,variation trends,China Keywords surface soil moisture,passive microwaveremote sensing,temporal andspatial characteristics,variationtrends,China Droughtis considereda majornatural disasterin China.Recently,the spatialextent ofdrought hasshown anex-panding trend in theprimary agriculturalregions ofNorth China,especially in the NorthChina Plain,where boththe droughtseverity anddrought affectedarea haveshowna significantincreasing trend.Several consecutivedrought eventsourred on the NorthChina Plainin orafter the1990s.Among them,the droughtevent of1997and of1999xxwere themost prominent.As aresult ofsues-sive droughts,most partsof theplain suffereda waterdeficit for56years,which inturn causeda hugeloss of agri-culture and ecological deterioration1.In orderto mitigatethe damage,drought monitoring and disasterassessing isnecessary.Currently,droughts areestimated throughindices derivedfrom meteorological observations in the BeijingClimate Center2which areunable tomonitor theagricul-tural droughtsaurately andtimely.Contrary to the limita-tions ofmeteorological drought indices,soil moistureis closely related toagricultural droughtas itis amajor watersource forcrops.Soil moisturealso servesas animportant indicatorfor productionestimation andis thereforekey inagriculture drought monitoringandimpact assessment.In thisresearch,three sets of soil moisture productsre-trieved from the AdvancedMicrowave ScanningRadiome-ters forEOS werebined into a newensem-ble of soil moisture data.With thisensembledata,the tem-poralandspatial variationsof thesurface soil moisturein Chinabetweenxxandxxwere analyzed.Trends ofsurface soil moisturedatawere pared with those of pre-cipitationderived from the GlobalPrecipitation ClimatologyProject(GPCP)data,to identifythe possible causes for the temporalvariation of the soil moisture.The soil moisture trendswere alsobined with those of the land surface temperaturederivedfrom theModerateResolutionImaging Spectroradiometer(MODIS)to assesstheir impacts on theagricultural sustainabilityand ecologicalconservation.1Data andmethods1.1Soil moisture products retrieved from AMSR-E AMSR-E waslaunched onboardthe Aquasatellite by the NationalAeronautics andSpace Administration(NASA)in Mayxxand hasmany advantagesparedwithformer passiveradiometers,such asScanning MultichannelMi-crowave Radiometer(SMMR)6and SpecialSensor Mi-crowave Imager(SSM/I)7.These advantagesinclude: (1)higher spatialresolution atlow frequencies,for example,the spatialresolution is60km ata6.9GHz channel; (2)dual polarization(both verticaland horizontal polarization)and multichannel(6.9,10.65,18.7,36.5and89GHz)ob-servation;and (3)essentially realtime dataacquisition8.Researchers havedeveloped severalalgorithms andre-trieved manyvariables,including thesurface soil moisture,from the brightness temperatureobserved by AMSR-E.NASA,Vrije Universiteitin Amsterdam(VUA)and theJapan AerospaceExploration Agency(JAXA)operationally releaseglobal soil moisture retrievalsfrom AMSR-E.Be-sides thesethree institutes,some agencieshave alsodevel-oped AMSR-E soil moisture products,such as the singlechannel retrievingproducts by the United States Depart-ment ofAgriculture9,and theregression productsby theInstituteofApplied Physicsof theItalian NationalResearch Council10.These products were focusedon specifiase studiesand notoperationally updated,therefore onlythe NASA,JAXA and VUA soil moisture productswere used in this study.a)The VUA AMSR-E soil moisture product.In thisproduct,as thefirst step,soil temperaturewas derivedfrom thebrightness temperatureof the36.5GHz channelusing anempirical regressionfunction.Following this,the LandSurface ParameterModel(LSPM)11was adopted to cal-culate aMicrowave PolarizationDifference Index(MPDI,see eq. (1).Using anonlinear iterationmethod,the soil moisture and vegetation water content wereoptimized tominimize the difference between the simulatedMPDI bythe LSPMand thatcalculated from the satellite observations.The VUAalgorithm usesthe dualpolarized channels of either6.9or10.65GHz.b)The JAXAAMSR-E soilmoisture product.Bright-ness temperatureat fourchannels,i.e.from dualpolarized channelsat6.9and18.7GHz,were used in the JAXA algo-rithm12.First,a brightness temperature databasewas builtbased onthe simulationof aradiative transfermodel13,in whichvarious binationsof soilmoisture,sur-face temperature and vegetation water content were input.And thenMPDI and the Indexof SoilWetness(ISW,see eq. (2)were derivedfromthedatabase.A reference table was then generatedto relatethe MPDIand ISWto the soilmoisture andvegetation watercontent.Finally,the soilmoisture andvegetationwatercontentwereestimated sim-ultaneously fromthe reversedreferencetableusing thesat-elliteobservationsas inputs.where TB(18.7H)and TB(6.9H)represent thebrightnesstemperatureat thehorizontalpolarizationchannelsof18.7and6.9GHz,respectively.c)NASA AMSR-E soilmoistureproduct.This prod-uct isbased onthe algorithmproposed byNjoku et al.14,15where aregression methodwas usedto derivesoilmoisturefromtheMPDI at10.7and18.7GHz.It isavaila-ble fromthe National Snow and Ice Data Center16.Due tothe limitationoftheglobal regressionmethod,the prod-uct containssome biguncertainties inregions outsidethe UnitedStates17.These three products havetwo spatialresolutions:0.5and0.25.Soil moistureis availablein swathtype,daily averageand monthlyaverage.Because thisresearch focusesontheregional soilmoisture distributionand longterm trends,the monthlyaveraged soilmoisture at0.25resolu-tion wasselected.The VUAproduct covers the period fromxxtoxxand the JAXA andNASA productscoverthe period fromxxtoxx.1.2Monthly averagedprecipitation fromGPCP The soilmoisturevariation iscloselyrelatedto precipitationactivities.In this study,the precipitationtrendinChina wasderivedfromGPCP18data for theperiodxxxx.By paringthe precipitationtrend withthe soilmoisture trend,the impactsof precipitationchanges onthe soilwet-ness variationcan beidentified.GPCP version2.2was used in thisresearch,downloaded fromthe NationalOceanic and Atmospheric Administration.The original1resolution datawas thendownscaled to0.25with abilinear interpolationmethod.1.3Land surface temperature fromMODIS MYD11C3Heat andmoisture aretwo fundamentalfactors inestimating agriculturalproductivity andevaluating theecological en-vironment.In this study,the trends of land surfacetemper-ature werebined withthoseofsoilmoisture,to identifytheir impactson agricultureandecologicalsystems inChina during the study period.The landsurface temperaturewastheMYD11C3product derivedfromtheAqua/MODIS ob-servation,downloaded fromthe UnitedStates GeologicalSurvey.The spatialresolution oforiginal data is0.05.A boxingaverage methodwas adopt-edtoconvert itintoa0.25landsurfacedata set.1.4Standardization andensemble analysisAs shownby Wanget al.19a multimodelensemble ap-proach isabletoreduce thebias uncertaintyofasingle model.This studydeveloped amultiproduct ensemblemethod tobine theJAXA,NASA andVUAAMSR-E productsand thenew ensembledata setwasusedto analyzethe temporalandspatialcharacteristics ofsurfacesoilmoistureinChina.All three soilmoisture products sharethe samebasis,i.e.they arederivedfromthebrightnesstemperature observa-tions providedbyAMSR-E,but dueto differencesin theradiative transfermodels,observation channels,ancillary datasets andretrieving techniques,there areobvious gapsamong thethree products.Table1lists the statistic valuesforthethreeproductsduring theresearch period.Their ranges,means andstand-ard deviationsdifferfromeachother.Consequently,thethreeproducts cannotbe directlybined;otherwise,the informationof oneor twoproducts wouldbe overwhelmedbytheproduct withthe largestvalues.To overethis,the ensembleapproach proposedin thepresent studyconsists oftwo steps.Step oneis tostandardize thethree soilmoisture productsaording toeq. (3),as whereA representsthe soilmoisture oftheJAXAor VUA-product,mean()is thestatistical mean,is thestandard deviationandAis thenew soilmoisture valueafter thestandard processing.Step twois tocalculate theaverage valuesofthestandardized productsastheensembledata.In this study,through standardization,theJAXAandVUAproductswere re-projected intothestatisticspace ofNASA.This standardizationmethod ismonly usedin par-ing soilmoisturedatafrom differentsources17.Table1Statistic valuesofthethreesoilmoisture products(%)Maximum MinimumMean VUA50JAXA59.63NASA19.73Standard deviation12.138.543.11.62.286.6523.639.2711.842Summary anddiscussions Threesetsofsoilmoisture products retrieved fromAMSR-E wereusedin thisstudy,to analyzethe temporalandspatialvariationsinChinabetweenxxandxx.Anensemble approachwas proposedto eliminatethediffer-ences betweenthe productsand tobine themerits ofindividual products intoa newdataset.With therecon-structed ensemble soilmoisturedata,the spatial patternsand temporalvariationtrendswereanalyzed.The lineartrendsofsoilmoisture werethen paredwiththoseof GPCP precipitation andMODIS landsurface temperature.The majorconclusions were: (1)Multiproduct ensemblesoilmoisturedataisable toprovidemore realisticspatialpatternsthanindividualprod-ucts. (2)The areaswith dryingtrends(51.1%of total)werelarger thanthose withwetting trends(48.5%of total)be-tweenxxandxx.In general,the semiaridregions be-came drierand aridregions becamewetter,consistent withthe results fromnumerical simulations35. (3)The dominanttrendsofindividualproductsdifferwith eachother forboth annualand seasonalregressions.In summer,the dryingtrend wasdominant forall datasets.Most ofthedryingregions werein themajoragriculturalareas ofChina. (4)There wasa significantcorrelation betweentheen-semblesoilmoisture andGPCPprecipitationduringthestudyperiod.Sixty-eight percentof totalpixels hadsignifi-cant positivecorrelation coefficients.The parisonbe-tween precipitationtrends and soilmoisture trends suggest-ed thatclimate changewas onepossiblecauseforthedrying insemiaridregionsand thewetting inaridregions. (5)Through thebination oflandsurfacetemperaturetrendsandsoilmoisturetrends,thestudydomain wasdi-vided intofour categories.Regions withdryingandwarm-ing trendscovera33.2%area,theregionswithdryingand coolingtrendscover27.4%,regionswithwettingandwarmingtrendcover21.1%and regionswithwettingandcoolingtrendscover18.1%.The firsttwo categorieswere largelyin themajor grainproducingareas,while thethird categorywas largelylocated innonarable regionssuch asthe Northwestand Tibet.This impliesthat themoisture andheat variationtrendinChina isunfavorable tosustainable developmentand ecologyconservation.As illustratedin Table1,there werebig gapsbetweenthethree retrievalproducts.The individualproducts hadsome obvioussystemic biasesin someregions.It suggeststhat theglobal soilmoistureproductsretrieved frompassive microwaveremotesensingare notthe actualsoilmoisturevalues butsome relativesurface wetnessindi-ces17,24.Both thesoilmoistureretrieving algorithmsand theproduct generationneeds furtherstudy.The soilmoistureproducts usedin thisstudy werere-trievedfromAMSR-E,which cannotprovide landsurface The soilmoistureproductsusedin thisstudywerere-trievedfromAMSR-E,which cannotprovide landsurface observationsin densevegetation dueto itslimited wave-length.Consequently,in forestregions,such asthe DaHinggan Mountainsand XiaoHinggan Mountainsin NortheastChina,andtheHengduan Mountainsin westYunnan,thesoilmoisture retrievalswere notreliable.These forestregions wereonly smallportions ofthewholestudydomain,and werenot agriculturalregions,which werethe emphasisofthisstudy.These regionscannot affectthe over-all temporalandspatialcharacteristics.As thelaunch ofthe Soil Moisture andOcean SalinityMission25bytheEu-ropean SpaceAgency andthe oningSoilMoistureAc-tive andPassive Mission26by NASA,L bandmicrowave observationwhich hasa longerwavelength willbe availableandtheshadowing effectsof vegetationcould begradually alleviated.The data usedinthisstudycoverstheperiodfromxxtoxx.The soilmoisture spatiotemporalvariation charac-teristics derivedfrom thisresearch aretherefore justfrom ashort periodof eightyears.By integratingthe remotesens-ing observationsmade bySMMR,SSM/I,Tropical RainfallMeasuring MissionMicrowave Imager27,AMSR-E andoning GlobalChange ObservationMission-Water28withtheproposed ensemblemethod,a longtermsoilmois-ture timeseries beginningin1978can bereconstructed.Such along-term remotesensingdataset hashigh potentialintheassessment ofglobal changeimpactsonwater re-sources,agriculture,and ecology.All thedatausedinthisresearch wasobtained fromremotesensing,which makestheresultsindependent frommeteorologicalobservationsand numericalsimulations.The ensembledatasetandtheresults presentedinthisstudy couldprovide plementaryinformation tothe traditionalclimatic analyses.References1Qin DH.Climate Change:Regional Reasctionsand DiseasterMitigation:The Impactsof ExtremEvents andthe CoreespondingSolutions Underthe ClimateChange Background(in Chinese).Beijing:Science Press,xx2Zou XK,Zhang Q,Wang WM,et al.Drought indicesand operationaldroughtmonitoringintheUSA andChina(in Chinese).Meteorol Mon,xx,31:693Li MX,Ma ZG,Niu GY.Modeling spatialand temporalvariationsinsoilmoistureinChina.Chin SciBull,xx,56:180918204Wang AH,Lettenmaier DP,Sheffield J.Soil moisturedroughtinChina,1950xx.J Clim,xx,24:325732715Ma ZG,Fu CB.Some evidenceof dryingtrend overnorth Chinafrom1951toxx.Chin SciBull,xx,51:291329256Gloersen P,Barath FT.Scanning multichannelmicrowave radio-meter forNimbus-G andSeasat-A.IEEE JOceanic Eng,1977,2:1721787Hollinger JP,Peirce JL,Poe GA.SSM/I instrumentevaluation.IEEE TransGeosci Remote,1990,28:7817908Kawanishi T,Sezai T,Ito Y,et al.The advancedmicrowave scanningradiometer forthe Earthobserving system(AMSR-E),NASDAs contributiontotheEOS forglobal energyand watercycle studies.IEEE TransGeosci Remote,xx,41:1841949Jackson TJ.Measuring surfacesoilmoisturessing passivemicro-waveremotesensing.Hydrol Process,1993,7:13915210Paloscia S,Macelloni G,Santi E.Soil moistureestimates fromAMSR-E brightnesstemperatures byusing adual-frequency algorithm.IEEE TransGeosci Remote,xx,44:3135314411Owe M,De JeuR,Holmes T.Multisensor historicalclimatology ofsatellite-derived globallandsurfacemoisture.J GeophysRes,xx,113:F0100212Koike T,Nakamura Y,Kaihotsu I,et al.Development ofan advancedmicrowave scanningradiometer(AMSR-E)algorithm ofsoilmois-tureandvegetationwatercontent.Ann JHydra EngJSCE,xx,48:21722313Lu H,Koike T,Fuji