水文气象学、流域水文学以及农业等领域迫切需要中高空间分辨率的土壤水分，但低空间分辨率的被动微波土壤水分产品已无法满足应用需求。雷达数据是提高土壤水分产品空间分辨率的潜在有力工具。项目立足于SMAP卫星微波辐射计的土壤水分产品，利用Sentinel-1卫星雷达数据对其进行尺度分解：首先基于离散植被散射模型，通过理论分析植被变化对土壤水分响应的影响，建立有效的植被校正方法，优化土壤水分估算模型；然后利用地面雷达观测实验和主被动协同机载实验数据，结合对植被和地表粗糙度敏感的交叉极化后向散射系数，建立主被动微波像元的尺度转换模型；在考虑模型残差的基础上，实现土壤水分的尺度分解，并结合广泛的地面观测数据，进行算法验证和误差分析。本项目的研究成果将提高土壤水分数据在农业和水文等领域的应用价值，并为我国新一代主被动协同观测卫星的发展提供理论支持。

Soil moisture of high spatial resolution are needed for applications associated with hydrometeorology, hydrology and agriculture. However, current passive microwave-based soil moisture product with coarse resolution can not meet the requirements. Radar data is a potential and powerful tool to improve the spatial resolution of soil moisture product. This project aims to utilize radar observations from Sentinel-1 satellite to downscale SMAP radiometer-only soil moisture product. In this study, through analyzing the influence of vegetation variations on soil moisture response based on discrete vegetation scattering model, vegetation effect correction method is established and soil moisture estimation model is improved. Using ground-based radar experiment data and airborne combined passive and active microwave observations, scale transformation model between active and passive microwave pixel is developed combined using cross-polarization backscatter. And then considering model residual, soil moisture downscaling algorithm is developed. Combined with a wide range of soil moisture observation network, the new downscaling algorithm is validated and analyzed. This research will enhance the application potential of soil moisture data in agriculture and hydrology, and will provide support for the development of new combined active and passive microwave satellite of China.