局域尺度上的水文或农业应用亟需较高空间分辨率的土壤湿度（SM）数据，微波土壤湿度空间降尺度是实现这一需求的重要途径。其中“光学/热红外与微波数据融合”的降尺度方法展现出了较大的应用潜力，然而这类方法依赖于遥感地表温度LST（Land Surface Temperature）或由LST分解得到的SM指数，受限于LST“云污染”、LST与SM解耦效应和LST分解不确定性等问题。为规避上述问题，本文通过构建3种地表蒸散效率LEE（Land surface Evapotranspiration Efficiency）与SM的降尺度函数关系（指数、余弦、余弦平方），利用MODIS地表蒸散数据（MOD16A2）计算得到的LEE（空间分辨率500 m）实现了SMAP土壤湿度产品（空间分辨率36 km）的空间降尺度。研究从动态范围、能量守恒、SM地面稀疏验证站、SM地面核心验证站等角度对降尺度算法进行评价分析。结果表明，本算法有效增加了原SM产品的空间细节特征、保持了原SM产品的动态范围并且降尺度前后能量守恒；与地面验证数据的对比分析表明，降尺度结果有效保持了原SM与地面实测数据的良好一致性；敏感性分析表明，余弦平方函数对MOD16A2产品误差的敏感性相对最小。

Improving the spatial resolution of microwave Soil Moisture (SM) production is of great significance for hydrological and agricultural applications on a regional scale. Downscaling microwave satellite SM with optical/thermal infrared and microwave fusion method shows great application potential. However, it mostly relies on remote sensing surface temperature (LST) or the SM index derived by LST decomposition, which is limited by the cloud contamination problems, LST decomposition uncertainties, and the decoupling effect between LST and SM. To circumvent these problems, we made a primary study on downscaling microwave SM by coupling MOD16 and SMAP data. In this study, we constructed three parameterized downscaling functions (i.e., exponent, cosine, cosine squared) between Land surface Evapotranspiration Efficiency (LEE) and SM. MOD16 products is employed to calculate LEE, which has a spatial resolution of 500 m. Combining the parameterized downscaling functions and the high-resolution LEE, original SMAP SM (spatial resolution, 36 km) data were successfully downscaled to a spatial resolution of 500m. The downscaled SM was evaluated in terms of dynamic range, energy conservation, in situ SM at sparse stations, and in situ SM at Core Validation Station (CVS). Results demonstrated that the downscaling algorithm increases the spatial detail characteristics of original SM, maintains the dynamic range of SM, and preserves energy during the downscaling process. Moreover, it maintains the performance of the original SM as compared with in situ SM at CVS and sparse stations. Sensitivity analysis showed that the cosine-square downscaling function is less sensitive to errors in MOD16 production than the other two downscaling functions.