针对植被虚拟仿真的LAI反演研究

植被环境因素（如土壤、水体的化学成分，地表温、湿度，光照强度等）是提高植被虚拟仿真可用性的关键。有效、准确地从遥感数据中反演植被特征，在遥感数据与植被虚拟仿真的建模接口建立联系则是关键所在。LAI（叶面积指数）则是典型的植被特征。应用IAI反演的等密度模型、非密度模型和两流近似法及其效率分析，给出了通过光合作用辐射吸收系数（FPAR）实现LAI反演的过程和方法。该研究为基于空间遥感信息的植被虚拟仿真工作提供重要方法和理论支撑，是数字农业的基础性工作，对于我国东北地区玉米作物的生长、发育的可视化监测和估产有着重要的意义。表1，参11。

Research on LAI Retrieving Applied for Virtual Reality Simulation of Vegetation

It is the key process for developing usability of virtual reality simulation of vegetation taking into account soil, chemistry elements of water, temperature and humidity of land surface, and lighting. Retrieving the characteristics of vegetation from remote sensing data effectively and exactly, building the relationship between remote sensing data and interfaces of VR - vegetation models are impor- tant. The paper discusses dense vegetation model, nondense vegetation model, and two-stream approximation model applied for LAI re- trieving, analyzes the efficiency of the methods; presents the process and method of LAI retrieving based on FPAR. The research offers the key methods and theoretic support for virtual reality simulation of vegetation based on spatial remote sensing information. The research is the primary work of digital agriculture, and important for monitoring corn growth and yield estimation in northeast of China.