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

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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.