Extending leaf optical property models for improved retreval of surface and biochemical characteristics

扩展叶子光学特性模型以改进表面和生化特性的检索

• 批准号: RGPIN-2019-05241
• 负责人: Noble, Scott
• 金额: $ 1.82万
• 依托单位: University of Saskatchewan
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=738141
• 关键词: Extending; leaf; optical; property; models

Given Canada's role as a food producing nation, and related threats posed by climate change to global food security, developing new crops with improved tolerances to environmental stresses is a challenge we are uniquely positioned to address.  In Canada, and around the world, major investments of public funds are being made into high-throughput data acquisition products and services for crop development and plant science in general. These "plant phenotyping" systems employ a number of instruments but are generally built around close-range, high-resolution imagery and spectral imagery. However, the models required to reliably determine the underlying plant biochemistry based on those data sources have yet to be developed and validated at the sub-canopy scale. Without those models the full value of these investments in our global food security will not be realized. Working within the context of a major plant phenotyping initiative based at the University of Saskatchewan the long-term objective of this research program is to realize the value of investments made by improving the extraction of detailed biophysical and biochemical information from spectral imagery of plants and plant canopies. In support of the long-term objectives of the research program, we will compare existing leaf optical property models by performing a sensitivity and correlation analysis of model parameters. This will inform efforts to manage model complexity while improving parameter retrievals. We will work toward incorporating leaf surface characteristics into the models by combining directional scattering, polarization, and surface measurement techniques to parameterize leaf surface characteristics. Informed by these efforts we will extend current leaf optical property models to include additional pigments and surface traits. By developing hi-fidelity models describing leaf optical properties we will allow plant physiology and breeding research to make full use of major public and private investments in spectral imaging infrastructure. These models will facilitate high-throughput measurement of key traits linked to drought, heat, and cold tolerance in food crops, aiding in crop breeding efforts. This work will also alleviate the current lack of datasets for leaf model development in remote sensing and environmental monitoring world-wide.

鉴于加拿大作为粮食生产国的角色，以及气候变化对全球粮食安全构成的相关威胁，开发对环境压力的耐受性更强的新作物是我们独一无二的挑战。在加拿大和世界各地，公共资金的重大投资正在用于作物开发和植物科学的高通量数据采集产品和服务。这些“植物表型”系统使用了许多仪器，但通常建立在近距离、高分辨率图像和光谱图像的基础上。然而，根据这些数据来源可靠地确定潜在植物生物化学所需的模型尚未开发，并在亚冠层尺度上得到验证。如果没有这些模式，这些投资在我们全球粮食安全方面的全部价值将无法实现。在萨斯喀彻温省大学一项主要的植物表型研究计划的背景下，这项研究计划的长期目标是通过改进从植物和植物冠层的光谱图像中提取详细的生物物理和生化信息来实现投资的价值。为了支持研究计划的长期目标，我们将通过对模型参数的敏感性和相关性分析来比较现有的叶片光学特性模型。这将有助于在改进参数检索的同时管理模型复杂性的努力。我们将致力于将叶表面特征结合到模型中，通过结合方向散射、偏振和表面测量技术来参数化叶表面特征。受这些努力的启发，我们将扩展当前的叶片光学属性模型，以包括更多的色素和表面特征。通过开发描述叶片光学特性的高保真模型，我们将允许植物生理学和育种研究充分利用光谱成像基础设施方面的主要公共和私人投资。这些模型将有助于高通量测量与粮食作物的干旱、耐热和耐寒有关的关键性状，帮助作物育种工作。这项工作还将缓解目前在全球遥感和环境监测中缺乏叶模型开发数据集的问题。