基于改进型光化学植被指数（PRI）的冬小麦水胁迫监测研究

项目名称： 基于改进型光化学植被指数（PRI）的冬小麦水胁迫监测研究

项目编号： No.41501367

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 天文学、地球科学

项目作者： 刘诗诗

作者单位： 华中农业大学

项目金额： 20万元

中文摘要： 利用遥感技术有效及时地监测农作物水胁迫状态对我国在全球气候变化下的粮食安全有着重要意义。光化学植被指数（PRI）由于能够反映光合作用的快速变化，已被用于农作物的早期水胁迫监测。然而基于PRI的水胁迫监测受农作物生长过程中色素含量和叶面积指数(LAI)的变化的影响，使其很难有效地用于不同生长阶段，且目前的研究都将色素含量和LAI视为相对独立的因子分析其对PRI的影响。本研究将以冬小麦为研究对象，分析在其不同生育期内，色素含量和LAI的联动变化对PRI以及利用PRI进行水胁迫监测的影响，在此基础之上，改进经典的PRI以减小色素含量和LAI对PRI水胁迫监测的干扰，针对所研究的各个生育期，建立基于改进型PRI的冬小麦早期水胁迫监测模型并评价其在不同生育期的适用性， 为推动高光谱遥感大范围监测农作物早期水胁迫和干旱预警提供理论依据和有效方法。

中文关键词： 植被指数；植被生理生态参数；高光谱遥感

英文摘要： Monitoring crop water stress using remote sensing techniques is vital for food security in China under global climate changes. Photochemical Reflectance Index (PRI) reflects the rapid change of photosynthesis, and thus has been used for detecting pre-visual water stress of crops. However, it is not very effective to apply PRI to monitoring water stress during different growth stages of crops, because PRI is affected by natural dynamics of pigment content and leaf area index (LAI) during growth stages. Although pigment content and LAI are inherently correlated, current researches usually treat them as independent factors when studying the impacts of pigment content and LAI on PRI. This research will investigate the impacts of the interactive changes in pigment content and LAI on PRI as well as on the evaluation of water stress with PRI during different growth stages of winter wheat. Based on this investigation, this study will modify the existing PRI to reduce the effect of pigment content and LAI on PRI, build the model to monitor water stress of winter wheat for each studied growth stage, and evaluate the effect of the model for different growth stages of winter wheat. This research will provide solid evidence and effective methods for applying hyperspectral remote sensing to large-scale detection of pre-visual water stress and early warning of drought for crops.

英文关键词： Vegetation Indices;Vegetation Physiological and Ecological Parameters;Hyperspectral Remote Sensing