冬小麦是我国主要的粮食作物，因此在其生长期内的长势情况监测在农业研究中占有重要地位。利用可见光遥感技术对农作物的长势监测应用非常广泛，但由于受到天气状况的影响在长时间序列上的应用受到限制，而被动微波遥感能够克服大气的影响，并且具有很高的时间分辨率，对于农作物实时动态监测具有重要意义。然而被动微波遥感的空间分辨率较低，并且针对微波遥感发展的植被辐射模型形式复杂，难以广泛应用。随着模型理论研究的深入，针对特定的植被对象，发展形式简单、精度较高的参数化模型已经成为研究的重点，也成为降低陆表植被参数反演难度的主要手段。在本研究中，主要以一阶参数化模型为基础，基于被动微波遥感数据对冬小麦的微波响应参数进行反演，从而对冬小麦长势评估提供理论基础。文中以地势平坦的华北平原为研究区，首先基于冬小麦不同生育期的地面实测参数，构建了组成冬小麦冠层的、包括不同尺寸和含水量的介电散射体模拟数据库，并在此基础上，建立冬小麦单散射反照率和光学厚度分别在C（6.925GHz）和X（10.65GHz）波段之间的依赖关系。然后根据一阶参数化模型推导得到的微波植被指数（Microwave Vegetation Indices，MVIs）的物理表达式，结合AMSR-E被动微波亮温数据和MODIS地表温度数据，反演华北平原地区冬小麦不同生育期的单散射反照率和光学厚度。结果显示，冬小麦单散射反照率和光学厚度与冬小麦的生长趋势呈现正相关关系，即随着冬小麦生长逐渐变大，又随着冬小麦的成熟又逐渐减小。该变化趋势与冬小麦每日NDVI的变化趋势大致相同，NDVI在返青期到孕穗期对冬小麦生长变化更为敏感，但单散射反照率在孕穗期到成熟期比NDVI更为敏感，而光学厚度与单散射反照率刚好相反，在返青期到孕穗期对冬小麦生长变化比较敏感，从孕穗期开始变化反而较小。这表明，利用单散射反照率和光学厚度进行植被动态监测，可为农作物长势监测提供理论依据和方法指导，具有一定的现实应用价值。该反演方法还可以进一步推广到其他不同类型的农作物、草地中去，能够为中国区甚至全球范围内低矮植被单散射反照率反演提供新的思路和方法。同时，冬小麦单散射反照率和光学厚度随时间变化趋势与NDVI随时间变化趋势之间的差异，说明两者在指示冬小麦生长方面具有一定的互补作用，也说明微波相对于可见光在进行植被监测时的优势。结合微波的单散射反照率和光学的NDVI，将有望在全球范围内提高对低矮植被的监测能力。由于华北平原地区冬小麦单散射反照率和光学厚度的成功反演，本文把该研究方法推广到华北平原低矮植被微波参数反演应用中。对于低矮植被反演，基本研究思路与冬小麦参数反演相同，而一阶参数化模型对低矮植被的适用性是保证反演顺利进行的关键。低矮植被参数反演在时间范围上扩展到2010年全年，并且根据低矮植被散射体范围重新构建了模拟数据库，得到了低矮植被单散射反照率和光学厚度分别在C和X波段的依赖关系，对华北平原地区低矮植被像元的单散射反照率和光学厚度进行了反演。低矮植被参数反演结果用MODIS 16天NDVI合成产品进行了定性分析和评价，发现低矮植被单散射反照率和光学厚度与NDVI的变化趋势大致相同，并且与植被的生长呈现正相关关系，随着植被的生长逐渐变大，随着植被的成熟又逐渐减小。这表明，利用单散射反照率和光学厚度进行植被动态监测，可为植被长势监测提供理论依据和方法指导，具有一定的现实应用价值。

Winter wheat is one of the main crops that grow in China, and is an important part in crop monitoring. Although the optical remote sensing technology is widely applied in crop growth monitoring, it is seriously affected by the weather condition and limited in time series dynamic monitoring. Obviously, microwave region can penetrate through atmosphere and has high temporal resolution which is significant in crop monitoring. However, the low spatial resolution of the passive microwave remote sensing and the complexity of radiative transfer models of vegetation are the main disadvantages that limit the application of crop monitoring. Further researches on the theoretical models of vegetation based on the radiative transfer equations indicate that the developed parameterized model of a certain kind of vegetation can overcome the difficulties in parameter retrieval of vegetation.In this study, the first-order parameterized model was used in the retrieval of microwave parameters of vegetation based on the passive microwave remote sensing. The North China Plain was selected as our study area because there was little terrain effect. First, a simulating database is established to figure out the relationship of the optical thickness, single scattering albedo at C (6.925GHz) and X (10.65GHz) band, respectively. The database is based on field measured parameters of different seasons of winter wheat and consists of scatterers of different sizes, water content. The microwave vegetation indices (MVIs) with two adjacent frequencies of AMSR-E at H/V polarization derived from the parameterized model are used to cancel out the ground surface emission signals. Finally, the parameterized first-order radiative transfer (RT) model for short vegetation layer is employed to retrieve the single scattering albedo and optical depth of winter wheat by combining passive microwave AMSR-E data with optical MODIS data. Comparison of daily single scattering albedo, optical depth with NDVI (Normalized Difference Vegetation Index) shows that the variation trends of retrieved single scattering albedo /optical depth and NDVI are very similar that both firstly increase and then decrease. It is obvious that NDVI is more sensitive than ω from Returning green Stage to Booting Stage of winter wheat, but from the Heading Stage to Milky Stage, the single scattering albedo is more sensitive. However, the optical depth is just the opposite of single scattering albedo and it is more sensitive from Returning green Stage to Booting Stage. Above all, the retrieved parameters of winter wheat and NDVI are the complementation of each other and can monitor the growth of winter wheat at different periods. Furthermore, the retrieval of winter wheat parameters can provide the theoretical support of global dynamic monitoring of vegetation.Because of the success in retrieval of winter wheat parameters, the paper applied this technology to the retrieval of single scattering albedo and optical depth of low vegetation. The simulating database was constructed based on the low vegetation range and the relationship of ω and τ were figured out and finally the ω and τ of low vegetation over 2010 were retrieved.The retrieval results of low vegetation were analyzed and evaluated qualitatively by using MODIS 16day products. The results showed that the variation trends of retrieved ω and τ of low vegetation and NDVI are very similar that both firstly increase as the vegetation grow and decrease along with mature of vegetation. These two microwave parameters retrieved can monitor the growth of low vegetation and have great value in vegetation monitoring.