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摘要： 构建了基于通用陆面模型（CoLM，Common Land Model）、微波辐射传输模型LMEB（L band Microwave Emission of the Biosphere）和集合平滑算法（EnKS，Ensemble Kalman Smoother）的土壤水分数据同化框架，用于联合同化MODIS地表温度和机载L波段被动微波亮温数据。以2012年HiWATER试验期间中游大满超级站为实验站点，分析了3种LAI数据产品对土壤温度模拟结果的影响，进而分析了联合同化地表温度和微波亮度温度对土壤水分估计结果的影响。研究结果表明：3种LAI数据对土壤温度模拟结果的影响显著，MODIS LAI产品在该研究区显著低估，导致土壤温度模拟结果高估4~6 K；同化亮度温度、同化地表温度以及联合同化两者均可以改进土壤水分的估计精度，联合同化地表温度和亮度温度对于土壤水分的改进最为显著，土壤水分同化结果的RMSE减少31%~53%。 关键词： 数据同化;  土壤水分;  土壤温度;  EnKS;  Common Land Model     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%.

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