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Remote Sensing Technology and Application  2017, Vol. 32 Issue (4): 606-614    DOI: 10.11873/j.issn.1004-0323.2017.4.0606
    
Improving Soil Moisture Estimation by Joint Assimilationof MODIS Land Surface Temperature and Airborne L band Microwave Brightness Temperature
Cao Yongpan1,2,3,Huang Chunlin1,2,Chen Weijing4,Zhang Ying1,2,3
(1.Heihe Remote Sensing Experimental Research Station,Northwest Institute of EcoEnvironmentand Resources,CAS,Lanzhou 730000,China;2.Key Laboratory of Remote Sensing of Gansu Province,Northwest Institute of EcoEnvironment and Resources,CAS,Lanzhou 730000,China;
3.University of Chinese Academy of Sciences,Beijing 100049 4.School of Resource andEnvironmental Science,Wuhan University,Wuhan 430079,China)
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Abstract  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%.
Key words:  Data assimilation      Soil moisture      Soil temperature      Ensemble Kalman smoother      Common Land Model
     
Received:  09 October 2016      Published:  13 September 2017
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. Improving Soil Moisture Estimation by Joint Assimilationof MODIS Land Surface Temperature and Airborne L band Microwave Brightness Temperature. Remote Sensing Technology and Application, 2017, 32(4): 606-614.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2017.4.0606     OR     http://www.rsta.ac.cn/EN/Y2017/V32/I4/606

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