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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(Lband 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4~6K;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%。
A soil moisture data assimilation framework based on Common Land Model (CoLM), Lband Microwave Emission of the Biosphere and Ensemble Kalman Smoother (EnFS, Ensemble Kalman Smoother) Co-assimilation MODIS surface temperature and airborne L-band passive microwave brightness temperature data. Based on the experiment of midstream Yutaka Super Station during the HiWATER trial in 2012, the effects of three LAI data products on soil temperature simulation results were analyzed. Then the effects of combined assimilation surface temperature and microwave brightness temperature on soil moisture estimation were analyzed. The results showed that three kinds of LAI data had significant effects on soil temperature simulation results. MODIS LAI products were significantly underestimated in the study area, resulting in overestimation of soil temperature simulation results from 4 to 6K. Assimilation brightness temperature, assimilated surface temperature and co-assimilation Can improve the estimation accuracy of soil moisture. The combined assimilation of surface temperature and brightness temperature has the most significant improvement on soil moisture, and the RMSE of soil moisture assimilation decreases by 31% -53%.