叶面积指数是森林与大气相互作用的重要表征参数，也是生态系统和全球气候变化研究中的关键因子。遥感是大尺度森林叶面积指数反演唯一的技术手段，主动微波遥感的全天候观测能力，可克服光学遥感在气候复杂的热带雨林地区难以获得理想数据的局限性，能为全球森林观测提供可靠的观测手段。本项目开展极化干涉SAR反演森林叶面积指数的方法研究，基于对森林体积层的电磁波散射理论研究，研究森林叶枝干多散射体的散射统计特征；结合极化分解技术，开展干涉复相干系数的分解，提取树叶层复相干散射分量，建立树叶层复相干散射模型；联合星载激光雷达波形数据，建立考虑复杂垂直结构森林的极化干涉反演方法，反演树叶层消光系数及高度等参数；基于Foldy-Lax近似，建立森林体积层内电磁传播常数计算方法，提出消光系数与森林叶面积指数的关系模型；最后，综合上述研究，建立极化干涉SAR反演叶面积指数的完善技术方法，并开展微波暗室和野外试验验证。

Leaf area index (LAI) is an important characteristic parameter for the interaction between the forest and atmosphere, and also is a key factor for ecosystem and climate change researches. Remote sensing is the only technology for global leaf area index inversion. The all-weather observation ability of active microwave remote sensing makes it a reliable technique, and breaks through the limitation that the optical data acquisitions are difficult for cloudy area such as tropical forests. This project will carry out the PolInSAR research for leaf area index retrieval. The scattering statistical characterization of the leaves, branches and trunks in forest will be investigated based on the electromagnetic scattering theory. The model of leaves complex coherence scattering model will be constructed by the decomposition of the complex coherence coefficient and the retrieval of the leaves coherence scattering component. Meanwhile, we will investigate the PolInSAR technique considering the complex forest vertical structure through combining spaceborne LiDAR waveform data, to retrieve the attenuation coefficient and height of leaves layer. Based on Foldy-Lax approximation, the propagation constant of forest volume will be retrieved and the relationship between attenuation coefficient and leaf area index will be constructed. At last, based on the above researched, the integrated technique and method of inversion of leaf area index by PolInSAR will be constructed, and finally, the measurements in microwave anechoic chamber and field experiments will be carried out to validate the accuracy of the research.