积雪由于其反照率高及消融变化会对气候变化和水循环产生重要的影响，因此积雪无论对于区域性气象、水资源、陆地水文过程、农田与灾害监测，还是全球气候研究都是十分重要的。而雪水当量（雪深）是反映地表积雪量的重要因子，是积雪面积、积雪分布、雪深和积雪密度参数的综合体现。被动微波遥感技术在探测雪水当量（雪深）方面有着不可替代的优势。本文一方面从影响被动微波反演雪水当量的典型因子——地形、湖冰入手，分析典型因子对当前雪水当量反演算法的影响；另一方面从中国区域的独特下垫面特征分析，改进和发展了FY3B/MWRI 中国区域被动微波雪深反演算法。山区地形通过高程和地形之间的相互辐射来影响微波辐射，从而引起微波辐射计接收的微波亮温信号产生改变，进而会对当前雪水当量反演算法反演SWE的结果产生一定影响。本文利用郭英等（2009）发展的基于本地方位角、极化旋转角的、卫星尺度的被动微波的地形校正算法，对当前反演雪水当量算法常用的频率（18GHz，36GHz）地形校正的结果评价，并分析了不同极化方式组合条件下，地形对低频和高频的亮温差的影响。通过对东北积雪实验观测数据和HUT（the Helsinki University of Technology）积雪-冰-水层模型模拟数据的比较分析，描述了积雪-冰-水系统的发射率特征。HUT模型在各个角度下模拟的亮温与实测的亮温均较吻合，其中模拟的水平极化亮温的拟合结果要好于垂直极化的结果。HUT模拟和地面测量结果的水平极化的R2为0.9316，垂直极化的R2为0.9194。进而通过地面观测数据，我们分析了湖冰对现有被动微波反演积雪算法的影响。实验中辐射计观测视场的积雪厚度比较薄，覆盖冰层的积雪厚度有5-8cm；冰层下面是液态水体。观测中的频率亮温差（18.7GHz-36.5GHz）,H（水平）极化亮温差达到-21.4K，而V（垂直）极化亮温差达到-31.9K。湖上的亮温差（18.7GHz-36.5GHz）为负值，因而若利用当前雪水当量（SWE）的半经验线性算法来反演湖冰上的积雪，会造成很大的误差。最后我们利用HUT模型进行了雪层厚度和冰层厚度对当前雪水当量反演所用的亮温差的敏感性分析，发现当前的雪水当量反演算法对冰层厚度非常敏感，尤其在冰层比较薄的情况下更为敏感；要精确获得富湖泊区的雪水当量，还需要更进一步的研究。本文利用2002-2009年的全国气象站点的地面积雪参数观测资料和相应时间、空间的AMSR-E L2A数据，根据高分辨率中国土地利用数据，结合中国区域的下垫面特征，把中国区域划分成森林、农田、草地和裸地四种地物类型，利用混合像元分解的方法，发展出了较高精度和高时间分辨率的雪深反演算法，应用到FY3B MWRI业务化的雪深产品。

Snow is a very important factor in meteorology and hydrology and has a great significance on the study of the global climate change, large-scale runoff estimation, and snow disaster monitoring. And Snow water equivalence (SWE) is an important factor in snow characteristics and is comprehensive reflection of area of snow, snow distribution, and snow depth and snow density. Passive microwave in detecting SWE (Snow Depth) has the irreplaceable advantage. In this paper, firstly, the typical influencing factors of retrieval of snow water equivalence (SWE) using passive microwave remote sensing were analyzed, such as the topography factor and the lake ice factor. Then, the snow depth retrieval algorithm of China region based on FY3/MWRI was improved, considering Chinese unique land use characteristics. The current algorithms of retrieval of snow water equivalence (SWE) using passive microwave remote sensing is based on the linear brightness temperature difference. In the mountain areas, the topography affects the microwave signal received by the microwave radiometer by ways of changing the elevation and the radiation between the terrains. Accordingly it can exert some certain influences on the result of SWE using the current algorithms of the retrieval of SWE. In this paper, Guo’s terrain correction algorithm（2009） of passive microwave remote sensing is applied to correct the brightness temperature globally at AMSR-E’s frequencies which are used in the current algorithms of the retrieval of SWE. We evaluated the terrain impact on brightness temperature at 18.7 GHz and 36.5 GHz at each polarization and polarization difference, respectively.In this work we chose the Helsinki University of Technology (HUT) snow emission model to character the emission behavior for a snowpack-ice-water system with the ground microwave radiometer observation. This snow and lake ice surveys was conducted in Songhua River, Songyuan city, Jilin Province, on Jan. 21- 22, 2010. Compared to the ground measurements over shallow snow-covered lake ice surface, the simulated brightness temperature at horizontal polarization was better than that at vertical polarizations. The R2 of the HUT simulation and measured value was 0.9304 at H-pol, and 0.9194 at V-pol, respectively. Further, we investigated the impact of lake on snow retrieval using passive microwave remote sensing using these ground-based observations. The snow depth in our ground snow survey was almost 5~8cm, ,the brightness temperature difference at the frequencies of 18.7 GHz and 36.5 GHz at V-pol and H-pol was up to -21.4K, -31.9K, respectively. These negative brightness temperature difference (18.7 GHz-36.5 GHz) over lakes caused errors in current SWE retrieval algorithms. Finally, we took sensitivity analysis of ice thickness and snow depth using HUT model on the show that the current brightness temperature difference snow retrieval algorithm was very sensitive to ice thickness, especially in the case of thinner ice. It needed us to make more study in the lake-rich area to improve the snow estimation accuracy. Finally, the snow depth retrieval algorithm of China region based on FY3/MWRI was improved. The new algorithm considered Chinese unique land use characteristics. And China Area was divided into forest, grass, bare and farmland, using China land use data provided by Institute of Geographic Sciences and Natural Resources Research, CAS.