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摘要： 基于新一代的Sentinel-1SAR数据与FY-3C的MWRI数据，研究植被覆盖地表土壤湿度反演方法。为消除植被对土壤湿度反演影响，首先利用FY-3C/MWRI的微波极化差异指数MPDI，建立植被含水量反演模型；然后，结合植被含水量反演模型和水—云模型，发展一种主被动微波联合反演植被覆盖地表土壤含水量模型；最后，在江淮地区开展反演试验，利用观测的土壤湿度数据进行反演结果的精度验证。结果表明：①对于植被覆盖地表土壤湿度反演，由FY3C/MWRI提取的MPDI对于去除植被影响效果较好；②相比于VH极化哨兵1号卫星数据，VV极化数据更适用于土壤含水量的反演，能够得到更高的土壤湿度反演精度；③哨兵1号卫星数据能够获得较高精度的土壤含水量反演结果，试验反演的土壤湿度值与实测值相关系数为0.561 2，均方根误差为0.044 cm3/cm3。  关键词： 土壤含水量;  Sentinel-1;  FY-3C/MWRI;  水云模型;  植被含水量     Abstract: This study aims to develop soil moisture retrieval model over vegetated areas based on Sentinel-1 SAR and FY-3C data.In order to remove vegetation effect，the MWRI data from FY-3C was applied to establish the inversion model of vegetation water content.The model was combined with the original water-cloud model，and developing a soil moisture retrieval model by combining active and passive microwave remote sensing data.Finally，the experiment of the soil moisture retrieval was conducted in Jiangsu and Anhui province，and validating the inversion accuracy of soil moisture by measured data.The results showed that：①For the vegetation-covered surface，the Microwave Polarization Difference Index obtain from FY-3C/MWRI was suitable for removing vegetation effect.②Compared with the Sentinel-1 VH polarization data，the backscattering coefficient of VV polarization was more suitable for soil moisture retrieval and get a higher accuracy of soil moisture retrieval.③Sentinel\|1 data can obtain high precision soil moisture estimation results，and the correlation coefficient between the estimated and measured soil moisture is 0.561 2 and RMSE is 0.044 cm3/cm3.

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