Applications of Bayesian radiative transfer model inversion to remote mapping plant functional traits

Plant functional trait observations are critical to understanding and representing ecosystem composition and function, including responses to environmental change. However, existing trait measurement approaches are too labor intensive for sampling at the requisite spatial scales, and their destructive nature precludes studying acclimation and adaptation of individuals over time. Spectral data from both field observations and remote sensing platforms present a rich, widely available, and non-destructive source of information on plant traits. Here, we discuss the application of radiative transfer model (RTM) inversion to estimation of foliar traits from reflectance measurements at spatial scales ranging from leaf to landscape.