土壤湿度作为天气、农林业、水循环研究中重要的地球物理参数，对气候变化有重要影响。陆面数据同化发展较晚，研究集中在同化土壤/积雪的常规观测与遥感观测来提高土壤湿度廓线/雪水当量的估计精度。卫星遥感资料的同化是一个研究热点，同化遥感数据对提高土壤湿度估计精度有积极的作用。基于CLM4.0(Common Land Model 4.0)陆面过程模式，采用集合卡尔曼滤波(EnKF)同化方法，在美国内布拉斯加州地区的Clay Center、Red Cloud及Grand Island观测站点进行了3个单点同化实验，同化的观测数据是由CDF(Cumulative Distribution Function)技术匹配调整后的卫星遥感资料——CCI(Climate Change Initiative)数据，同化分析实验时间为2008年5月1日至2008年10月31日，利用站点实测数据对0~2cm土壤湿度的同化结果与间接受其同化影响10cm处的土壤湿度估计值进行了验证。结果表明：通过单点同化卫星遥感资料的方法可以提高表层土壤湿度的估计精度，并且受其同化影响，靠近同化层的土壤，其土壤湿度的估计精度也得到提高。
英文摘要:
As a key geophysical parameter in weather, agroforestry and hydrologic cycle, soil moisture has important effects on climatic change. The development of land surface data assimilation is relatively late, and most of the studies focus on assimilating the routine observations and remote sensing data of soil/snow to improve the estimation accuracy of soil moisture profile/snow water equivalent. The assimilation of satellite remote sensing data is a hot research topic, it has positive effects on improving soil moisture estimation accuracy. Three point-scale experiments were performed based on CLM4.0 (Common land Model 4.0) and EnKF method at Clay Center, Red Cloud and Grand Island observation stations in Nebraska, USA. The observation data used in data assimilation was remote sensing data-CCI (Climate Change Initiative) data which was processed by CDF (Cumulative Distribution Function) matching technique. The assimilation experiments started from May 1, 2008, and ended on October 31, 2008. After the experiments, the assimilated 0~2cm and the estimated 10 cm soil moisture (which was affected by the assimilation) results, were validated with observation data. The results indicated that, the point-scale method of assimilating remote sensing data improved the soil moisture estimation in topsoil; also, under the influences of the assimilation,the soil moisture estimation accuracy in the layer closed to the assimilated layer could also be improved.