植被含水量是评估植被生长状态的一个重要参数，被广泛应用于农情监测、植被生物量估算。同时，植被含水量也是利用主、被动微波遥感数据反演土壤水分的关键参数之一。因此，发展具有较高可行性的植被含水量反演算法对于指导农业生产、进行土壤水分监测、估算植被生物量等具有重要的现实意义。目前，大量的先期研究多是基于光学－红外波段数据，选取对水分含量敏感的波段，构建可以间接反映植被含水量的水分指数，利用统计回归的方法建立植被含水量与这些指数之间的经验关系，从而实现对植被含水量的反演。然而，光学遥感数据对植被的穿透性不够，反映的多是植被冠层和叶片的信息，且易受到云雨的影响，想要获取连续的光学遥感数据比较困难。微波遥感作为一种综合、宏观、快速、实时的观测手段，是进行植被含水量监测的有效方式之一。微波具有全天时全天候的工作特点，对植被具有良好的穿透性，可提供比可见光和红外波段丰富的植被结构信息，对于获取浓密植被覆盖区域的植被含水量优势显著。本文基于对成熟物理模型模拟数据的分析和对物理公式的逻辑推导，发展了一种基于C波段双角度被动微波亮温数据和冬小麦LAI数据的冬小麦重量含水量反演算法，并利用地基实验获取的冬小麦灌浆期间C波段多角度被动微波亮温观测数据和冬小麦LAI实测数据开展了对灌浆期间冬小麦重量含水量的反演工作。然后，我们针对玉米的植被三维结构参数对所发展的C波段冬小麦重量含水量反演算法进行了改进，并进一步将修改后的玉米重量含水量反演算法推广到机载尺度，发展了基于L波段双角度被动微波亮温数据和玉米LAI及其株高和株密度数据的玉米重量含水量反演算法，并利用2012年HiWATER试验期间获取的PLMR机载L波段多角度被动微波亮温数据和GLASS LAI数据以及玉米株高和株密度数据开展了对区域面积玉米重量含水量的反演。本文在低矮植被重量含水量反演算法发展过程中，首先分别构建了基于C波段和L波段被动微波遥感双角度亮温数据的低矮植被光学厚度估算方法。其次，基于包含宽范围冬小麦重量含水量和散射体结构参数的冬小麦LAI及其C波段光学厚度模拟数据，构建了冬小麦C波段光学厚度与其重量含水量和LAI之间的定量关系。将C波段低矮植被光学厚度估算方法引入到冬小麦C波段光学厚度与其重量含水量和LAI之间的定量关系中，构成基于C波段双角度被动微波亮温数据和冬小麦LAI数据的冬小麦重量含水量反演算法。然后，我们针对玉米的植被三维结构参数，基于包含宽范围玉米重量含水量和散射体结构参数的玉米LAI及其L波段光学厚度模拟数据，构建了玉米L波段光学厚度与其重量含水量和LAI等三维结构参数之间的定量关系。将L波段低矮植被光学厚度估算方法引入到玉米L波段光学厚度与其重量含水量和LAI等三维结构参数之间的定量关系中，构成基于L波段双角度被动微波亮温数据和玉米LAI及其株高和株密度数据的玉米重量含水量反演算法。最后，基于地基实验观测数据开展了对灌浆期间冬小麦重量含水量的反演工作。利用2012年HiWATER试验期间获取的PLMR机载L波段多角度被动微波亮温数据和GLASS LAI数据以及玉米株高和株密度数据开展了对区域面积玉米重量含水量的反演。验证结果表明，在地基尺度上基于C波段双角度被动微波亮温数据和冬小麦LAI数据的冬小麦重量含水量反演算法反演的冬小麦重量含水量与实测的冬小麦重量含水量吻合较好，该冬小麦重量含水量反演算法具有一定的可行性，可为农作物长势监测提供理论依据和方法指导。在像元尺度上基于L波段双角度被动微波亮温数据和玉米LAI及其株高和株密度数据的玉米重量含水量反演算法也具有一定的可行性，可实现对区域面积玉米重量含水量的实时监测，但反演精度很大程度上受限于输入参数LAI的精度。

Vegetation water content (VWC) is one of the most important parameters for evaluating the growth of vegetation. VWC has been widely used to monitor agricultural conditions, estimate biomass, etc. Moreover, VWC is a key parameter for retrieving soil moisture using active and passive microwave remote sensing data. Therefore, developing a feasible VWC inversion method is highly important for agricultural production, soil moisture monitoring, and biomass estimation. At present, the retrievals of VWC primarily use visible- and infrared-band data to select the band most sensitive to water content to establish a vegetation moisture index that can indirectly reflect the VWC. Statistical regression methods are used to establish an empirical relationship between the VWC and these indices. So, we can use these relationships to retrieve VWC. However, optical remote sensing cannot penetrate vegetation. Therefore, optical remote sensing obtains more information on the vegetation canopy, and optical remote sensing methods are vulnerable to the effects of clouds and rain. Thus, the collection of continuous optical remote sensing data is difficult. Passive microwave remote sensing is an effective technique for monitoring land surface parameters due to its comprehensive, macro-scale, efficient, and real-time observations. It can operate at all times and in all types of weather. Microwave radiation can penetrate vegetation and provide more vegetation structure information than the visible- and infrared-bands. This method has a significant advantage in terms of obtaining VWC in regions with dense vegetation.In this study, first, we developed a new algorithm to retrieve the gravimetric vegetation water content (%) of winter wheat through analyzing numerical simulations and deriving physical formulas. In the algorithm, C-band bi-angular passive microwave brightness temperatures and the winter wheat LAI data are used to obtain the gravimetric vegetation water content of winter wheat. We used ground-based C-band multi-angle passive microwave brightness temperatures of winter wheat and the winter wheat LAI data which obtained by ground-based experiment during the winter wheat Grain-filling Stage to retrieve the gravimetric vegetation water content of winter wheat. Then, we focused on the three-dimensional structure parameters of corn to improve the inversion method of the winter wheat gravimetric vegetation water content at L-band, and extended the modified inversion method of corn gravimetric vegetation water content to the airborne scale. We used Polarimetric L-band Microwave Radiometer (PLMR) airborne L-band multi-angle passive microwave brightness temperatures (obtained by the 2012 Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project), the Global Land Surface Satellite (GLASS) leaf area index (LAI) product and the height and density of corn to retrieve the regional GVWC of corn. In this paper, we established the inversion method of low-vegetation optical depth based on the C-band bi-angular passive microwave brightness temperatures and L-band bi-angular passive microwave brightness temperatures, respectively. Second, based on in-situ measured winter wheat-related three-dimensional structure parameters and a mature physical model, a simulation database of winter wheat LAIs and optical depths in the C-band was established. This database covers wide ranges of winter wheat gravimetric vegetation water content and scattering features structure parameters. We established a quantitative relationship between the winter wheat optical depth in the C-band, the winter wheat gravimetric vegetation water content and the LAI based on the numerical simulations. Then, we added the estimation method for low-vegetation optical depth in the C-band to the quantitative relationship to form the inversion method of the winter wheat gravimetric vegetation water content based on C-band bi-angular passive microwave brightness temperatures and the LAI data of winter wheat. Third, we focused on the in-situ measured corn-related three-dimensional structure parameters during various growth stages and a mature physical model, a simulation database of corn LAIs and optical depths in the L-band was established. This database covers wide ranges of corn gravimetric vegetation water content and scattering features structure parameters. We established a quantitative relationship between the corn optical depth in the L-band, the corn gravimetric vegetation water content, the LAI, and other corn-related three-dimensional structure parameters based on the numerical simulations. Then, we added the estimation method for low-vegetation optical depth in the L-band to the quantitative relationship to form the inversion method of the corn gravimetric vegetation water content based on L-band bi-angular passive microwave brightness temperatures, the LAI data, and the height and density of corn. Finally, we used the C-band multi-angle passive microwave brightness temperatures and the LAI of winter wheat which obtained by ground-based experiment to retrieve the gravimetric vegetation water content of winter wheat. We used PLMR bi-angular brightness temperatures, the GLASS LAI product and the height and density of corn to retrieve the regional gravimetric vegetation water content of corn. The validate results show that, in the ground-based, the retrieved winter wheat water contents based on the C-band bi-angle passive microwave brightness temperatures and the LAI are consisted with the field measurements. It proves that the inversion method of winter wheat gravimetric vegetation water content proposed in this study is feasible. It can provide some theoretical basis and guidance method for monitor agricultural conditions. In the pixel scale, the corn gravimetric vegetation water content inversion method proposed in this study is feasible for monitoring regional corn gravimetric vegetation water content in real time. However, the accuracy of the retrieval results is limited by the accuracy of the LAI input parameters.