土壤水分是气候变化研究的重要物理量和气象水文模型的重要参数，土壤水分监测对天气预报、农业生产、干旱监测、水资源管理等应用具有重要价值。SMAP是目前最先进的土壤水分对地观测计划。其最大特点是同时搭载了主动和被动微波传感器，能同时提供L波段的后向散射和亮温数据。本研究针对SMAP的传感器配置，分别利用地表微波散射模型、辐射模型，研究和表达不同土壤水分条件下，时间序列主被动微波数据之间的物理关系。在此基础上引入泰勒级数展开尺度转换模型，构建具有前向模型物理机理的亮温降尺度模型，解决低分辨率像元内植被异质性引起的降尺度误差纠正问题。最后，基于降尺度亮温，进行被动微波遥感土壤水分反演方法研究。该研究成果不仅可为我国新一代微波传感器的设计、研制提供技术支持，而且反演得到的高空间分辨率、高精度的地表土壤水分数据，可以为气象、环境等领域中的应用提供重要的数据源。

Soil moisture is an important parameter in the research on climate change and a key variable in meteorological and hydrological model. Soil moisture monitoring is important for applications involving weather forecasting, agriculture, drought hazard monitoring and water management. Soil Moisture Active Passive (SMAP) mission is the most advanced Earth observation satellites designed to measure and map Earth's soil moisture. The SMAP instrument incorporates an L-band radar and an L-band radiometer. In this study, firstly the relationship between active and passive microwave data will be analyzed based on microwave scattering model and radiation model. With fine resolution backscatter and optical data, downscaling algorithm of the coarse resolution brightness temperature will be developed based on Taylor Series Expansion method. The new downscaling algorithm can correct the downscaling error induced by the vegetation heterogeneity. Subsequently, with the downscaled brightness temperature, new soil moisture retrieval algorithm will be studied. This research will provide some essential technology supports in the designing and developing of the new generation of microwave sensor for our country in the future. The fine spatial resolution and high temporal resolution soil moisture product will provide important information for applications including hydrology and meteorology.