Remotely sensed spectral information is increasingly used to monitor ecosystems. Relationships between chemical composition of leaf pigments and remotely sensed spectral traits, for instance, enable to infer leaf traits. However those relationships are based on classical data from more than two decades ago. Technologies have progressed, resolution of biogeochemical analytics has increased, remote sensing detectors have improved, but these classical data and relationships have not yet been updated. In this perspective, we measure the spectral data (e.g., hemispherical, conical) of leaf and canopy traits in the field, and second assess leaf biochemical composition (pigment, waxes, etc.). Particular emphasis is being place on describing and identifying individual leaf pigments using liquid chromatography coupled to light detectors, and then establishing relationships between the two based on their optical properties. Besides focus is made on understanding the epicuticular wax composition and turnover. The aim of the project is to improve correlation and relationships between chemical data and remote sensed traits, integrating analytical advances made in plant biogeochemistry and remote sensing. The expected overall outcome would be that remotely sensed spectral information could be used to infer plant traits on a watershed scale.
Project Funding: University Research Priority Program in Biodiversity and Global changes (URPP GCB)