土壤水分是连接陆地水、能量和碳循环的关键水文变量。主、被动微波信号对土壤水分具有不同的响应机制，能够提供互补信息。如何将两者有机结合得到更加精确的土壤水分是当前的研究热点与难点。针对现有主被动联合反演算法的不足，项目拟基于国内外的机载与星载SMAP主被动微波数据，开展以下研究：(1)选择具有物理一致性的主被动TVG模型为前向模型，进一步整合考虑多次散射效应的AIEM模型对其进行完善，提高其全极化模拟能力；(2)基于详实的地面观测与遥感资料，利用全局敏感性分析方法确定不同地表状况下TVG模型的敏感参数，完成模型的标定；(3)基于标定的TVG模型，通过构建代价函数确定主被动微波及其极化组合的最优模式，进而联合同尺度的主被动微波观测反演得到土壤水分并验证其精度。项目有望进一步揭示主被动微波土壤水分联合反演的遥感机理，提高其适用范围和反演精度，为我国未来的主被动微波卫星提供理论支撑和算法支持。

Soil moisture is a critical hydrological variable that links the terrestrial water, energy, and carbon cycles. Active and passive microwave signals respond differently to soil moisture and can provide complementary information on it. How to combine active and passive microwave observations to obtain more accurate soil moisture retrievals is still a challenging issue and becomes the current research hotspot. To solve the drawbacks of current active-passive combined soil moisture retrieval algorithms, the project will carry out research based on both airborne and spaceborne (i.e., SMAP) active and passive microwave observations in the following aspects: (1) The physically consistent active-passive Tor Vergata (TVG) model will be adopted as the forward model, and the multiple scattering AIEM model will be integrated into the TVG model to further improve the full-polarization simulation accuracy of the TVG model; (2) Based on detailed ground observations and remotely sensed datasets, the global sensitivity analysis method will be employed to determine the sensitive parameters of TVG model under different ground conditions to calibrate the TVG model; (3) Based on the calibrated TVG model, the optimal mode of active and passive microwave signals and their polarization combinations for soil moisture retrieval will be determined by constructing a cost function. Soil moisture will be then retrieved by combining use of both active and passive microwave observations at the same spatial scale, and the accuracy of soil moisture retrievals will be validated by using dense ground measurements. This project is expected to further reveal the mechanism of soil moisture retrieval using both active and passive observations, and to improve both the applicability and accuracy of soil moisture retrieval algorithm, thus providing theoretical and algorithmic support for future active-passive microwave satellites in China.