随着全球气候变化研究的不断深入，土壤水分逐渐成为研究全球干旱和全球降雨等气候现象的一个重要思路。同时，土壤水分是地球科学研究中各个分支的一个重要参数，是气象学和水文学中很多模型涉及的基本参数。土壤水分的反演精度直接影响着诸如天气预报，土壤类型评估，土壤水蚀风蚀治理等职能部门做出的决定。因此，反演土壤水分有着很重要的意义。本论文的主要研究目的是基于一阶参数化模型尝试改进土壤水分的反演算法，提高土壤水分反演的精度。地表温度作为土壤水分反演模型重要的输入参数，在土壤水分反演精度方面起到至关重要的作用。本研究中，首先针对地表温度反演算法做了必要的比较和验证研究。本文选择Mao（2005），Richard（2003），Zhao（2011）三种算法，对青藏高原地区的数据做不同下垫面的地面验证和分析。研究表明，Richard（2003）的单通道算法能够适应低矮植被地区，反演精度高；Zhao（2011）算法在裸土地区的反演精度更高；而Mao（2005）算法出现了低估的情况。研究发现三种算法的绝对误差随不同时间降雨的变化呈现相同的波动趋势，即反演精度受到降雨的影响，降雨量增大，温度反演误差变大；降雨之后，随着地表逐渐干燥，土壤水分逐渐减小，误差随之减小。本文建立了基于植被一阶参数化模型的土壤水分算法，用以提高大尺度土壤水分的反演精度。一直以来，一阶辐射传输模型由于考虑了植被层内的体散射效应，因而可以更准确的描述电磁波在穿过植被层过程中的散射和吸收情况，但是由于一阶辐射传输模型计算复杂，运算效率差，始终没有运用到参数反演上。而柴琳娜等人对一阶模型进行了参数化，将一阶辐射传输模型简化为只用地表信息和植被信息的模型，在一定条件下得到其简单形式。温度参数采用上述验证结果，减少了辅助输入。本论文的创新之处在于，引入了一阶参数化模型，温度输入只使用单通道的亮温数据，采用迭代方法反演土壤水分。研究结果表明，引入一阶参数化模型之后，较茂密植被、较高土壤湿度地区的土壤水分反演精度有所提高。

With the global climate change research unceasingly deeply, soil moisture provides an important idea for researching global drought and global climate phenomena. At the same time, the soil moisture is one of the important parameters of earth science research such as meteorology and hydrology. Soil moisture inversion precision directly influences government department’s decision such as the weather forecast, soil type assessment, water erosion of soil erosion control, etc. Therefore, the inversion of soil water has a very important significance. In general, the passive microwave is used for soil moisture retrievals due to the low attenuation from clouds and the large dielectric contrast between water and soil in passive microwave frequencies. In this study, we develop a soil moisture algorithm by changing the forward microwave emission model from zero-order to the first-order. This improvement is assuming that the first-order model is more accurate than the zero-order model by considering vegetation volume scattering.Surface soil temperature is an important input variable in land surface processes models and input parameter in radiative transfer models. In this study, we firstly did necessary comparison and the validation studies for the surface temperature inversion algorithm. We choose three algorithms of inversing surface temperature which are Mao（2005），Richard（2003）and Zhao（2011），and then makes a comparative and analysis over overall accuracy. The result indicates that the Richard (2003) algorithm appears to be performing well under low vegetated land area. The precision of Zhao’s algorithm is to be better at barren, sparsely vegetated area. The errors of three algorithms absolutely show the same sequence trend of fluctuations over time. The precision decreases as precipitation is increased, after the rain, as the surface drying, soil moisture gradually decreased gradually, the error then decreases.This paper established soil moisture algorithm based on the first order parameterized vegetation model, to improve large scale soil moisture inversion precision. Since always, first order radiation transmission model owing to considering the vegetation of the volume scattering effects, and therefore can be more accurate description of the electromagnetic waves in layers of scattering process. But because the model is complex to calculate, and operation efficiency is poor, it has not be applied to the parameter inversion. And Chai(2010) developed a large scale, a simplified model for radiation transmission only including surface information and information of vegetation in given conditions. The innovations of this paper exist in the introduction of the first order parameterized vegetation model to improve soil moisture algorithm. Using single channel bright temperature data to inversion the land surface temperature, and iteration method to inverse soil moisture. The results of the study show that, (1) to validate the capability of the parameterized first-order model used to inversion soil moisture in this study. At high frequency and for dense vegetation, volume scattering were not negligible, and that, Parameterized first-order model is a function of single scattering albedo and optical depth; (2) retrieval accuracy is improved. In this algorithm, we decide the variable of surface temperature, single scattering albedo to simplify the iteration process. It is important that the first-order model is more accurate in simulation land surface condition; (3) this method can be apply to dense vegetation area.