Using multi-satellite microwave remote sensing observations for retrieval of daily surface soil moisture across China
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摘要
The objective of this study was to retrieve daily composite soil moisture by jointly using brightness temperature observations from multiple operating satellites for near real-time application with better coverage and higher accuracy.Our approach was to first apply the single-channel brightness radiometric algorithm to estimate soil moisture from the respective brightness temperature observations of the SMAP,SMOS,AMSR2,FY3B,and FY3C satellites on the same day and then produce a daily composite dataset by averaging the individual satellite-retrieved soil moisture.We further evaluated our product,the official soil moisture products of the five satellites,and the ensemble mean(i.e.,arithmetic mean)of the five official satellite soil moisture products against ground observations from two networks in Central Tibet and Anhui Province,China.The results show that our product outperforms the individual released products of the five satellites and their ensemble means in the two validation areas.The root mean square error(RMSE)values of our product were 0.06 and 0.09 m~3/m~3 in Central Tibet and Anhui Province,respectively.Relative to the ensemble mean of the five satellite products,our product improves the accuracy by 9.1%and 57.7%in Central Tibet and Anhui Province,respectively.This demonstrates that jointly using brightness temperature observations from multiple satellites to retrieve soil moisture not only improves the spatial coverage of daily observations but also produces better daily composite products.
The objective of this study was to retrieve daily composite soil moisture by jointly using brightness temperature observations from multiple operating satellites for near real-time application with better coverage and higher accuracy.Our approach was to first apply the single-channel brightness radiometric algorithm to estimate soil moisture from the respective brightness temperature observations of the SMAP,SMOS,AMSR2,FY3B,and FY3C satellites on the same day and then produce a daily composite dataset by averaging the individual satellite-retrieved soil moisture.We further evaluated our product,the official soil moisture products of the five satellites,and the ensemble mean(i.e.,arithmetic mean)of the five official satellite soil moisture products against ground observations from two networks in Central Tibet and Anhui Province,China.The results show that our product outperforms the individual released products of the five satellites and their ensemble means in the two validation areas.The root mean square error(RMSE)values of our product were 0.06 and 0.09 m~3/m~3 in Central Tibet and Anhui Province,respectively.Relative to the ensemble mean of the five satellite products,our product improves the accuracy by 9.1%and 57.7%in Central Tibet and Anhui Province,respectively.This demonstrates that jointly using brightness temperature observations from multiple satellites to retrieve soil moisture not only improves the spatial coverage of daily observations but also produces better daily composite products.
The objective of this study was to retrieve daily composite soil moisture by jointly using brightness temperature observations from multiple operating satellites for near real-time application with better coverage and higher accuracy.Our approach was to first apply the single-channel brightness radiometric algorithm to estimate soil moisture from the respective brightness temperature observations of the SMAP,SMOS,AMSR2,FY3B,and FY3C satellites on the same day and then produce a daily composite dataset by averaging the individual satellite-retrieved soil moisture.We further evaluated our product,the official soil moisture products of the five satellites,and the ensemble mean(i.e.,arithmetic mean)of the five official satellite soil moisture products against ground observations from two networks in Central Tibet and Anhui Province,China.The results show that our product outperforms the individual released products of the five satellites and their ensemble means in the two validation areas.The root mean square error(RMSE)values of our product were 0.06 and 0.09 m~3/m~3 in Central Tibet and Anhui Province,respectively.Relative to the ensemble mean of the five satellite products,our product improves the accuracy by 9.1%and 57.7%in Central Tibet and Anhui Province,respectively.This demonstrates that jointly using brightness temperature observations from multiple satellites to retrieve soil moisture not only improves the spatial coverage of daily observations but also produces better daily composite products.
引文
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Dorigo,W.A.,Gruber,A.,De Jeu,R.A.M.,Wanger,W.,Stacke,T.,Loew,A.,Albergel,C.,Brocca,L.,Chung,D.,Parinussa,R.M.,et al.,2015.Evaluation of the ESA CCI soil moisture product using ground-based observations.Remote Sens.Environ.162,380-395.https://doi.Org/10.1016/j.rse.2014.07.023.
Du,J.Y.,Kimball,J.S.,Jones,L.A.,2016.Passive microwave remote sensing of soil moisture based on dynamic vegetation scattering properties for AMSR-E.IEEE Trans.Geosci.Remote Sens.54(1),597-608.https://doi.org/10.1109/TGRS.2015.246275 8.
Enenkel,M.,Reimer,C.,Dorigo,W.,Wanger,W.,Pfeil,I.,Parinussa,R.,Jeu,R.D.,2016.Combining satellite observations to develop a global soil moisture product for near-real-time applications.Hydrol.Earth Syst.Sci.20(10),4191-4208.https://doi.org/10.5194/hess-20-4191-2016.
Entekhabi,D.,Njoku,E.G.,O'Neill,P.E.,Kellogg,K.H.,Crow,W.T.,Edelstein,W.N.,Entin,J.K.,Goodman,S.D.,Jackson,T.J.,Johnson,J.,et al.,2010.The soil moisture active passive(SMAP1)mission.Proc.IEEE98(5),704-716.https://doi.org/10.1109/jproc.2010.2043918.
Fischer,G.,Nachtergaele,F.,Prieler,S.,Van Velthuizen,H.T.,Verelst,L.,Wiberg,D.,2008.Global Agro-Ecological Zones Assessment for Agriculture(GAEZ 2008).Laxenburg.
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Ford,T.W.,Quiring,S.M.,Frauenfeld,O.W.,Rapp,A.D.,2015.Synoptic conditions related to soil moisture-atmosphere interactions and unorganized convection in Oklahoma.J.Geophys.Res.:Atmosphere 120(22),11519-1153 5.https://doi.org/10.1002/2015JD023975.
Friedl,M.A.,Menashe,D.S.,Tan,B.,Schneider,A.,Ramankutty,N.,Sibley,A.,Huang,X.M.,2010.MODIS Collection 5 global land cover:Algorithm refinements and characterization of new datasets.Remote Sens.Environ.1 14(1),168-1 82.https://doi.org/10.1016/j.rse.2009.08.016.
Gallego-Elvira,B.,Taylor,C.M.,Harris,P.P.,Ghent,D.,Veal,K.L.,Folwell,S.S.,2016.Global observational diagnosis of soil moisture control on the land surface energy balance.Geophys.Res.Lett.43(6),2623-263 1.https://doi.org/10.1002/2016GL068 178.
He,M.,Kimball,J.S.,Running,S.,Ballantyne,A.,Guan,K.,Huemmrich,F.,2016.Satellite detection of soil moisture related water stress impacts on ecosystem productivity using the MODIS-based photochemical reflectance index.Remote Sens.Environ.186,173-1 83.https://doi.org/10.1016/j.rse.2016.08.019.
Huang,Y.Y.,Gerber,S.,Huang,T.Y.,Lichstein,J.W.,2016.Evaluating the drought response of CMIP5 models using global gross primary productivity,leaf area,precipitation,and soil moisture data.Glob.Biogeochem.Cycles 30(12),1827-1846.https://doi.org/10.1002/2016GB005480.
Jackson,T.J.,Schmugge,T.J.,1991.Vegetation effects on the microwave emission of soils.Remote Sens.Environ.36(3),203-212.https://doi.org/10.1016/0034-4257(91)90057-D.
Jackson,T.J.,1993.III.Measuring surface soil moisture using passive microwave remote sensing.Hydrol.Process.7(2),139-152.
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