Abstract:

Plant functional traits are the measurable characteristics that indicates plant adaptation to the environment, and understanding the patterns of certain characteristics, and their drivers is an essential component of plant ecology and earth system modeling research. Traditional field-based approaches for characterizing plant functional traits are time-consuming, labor-intensive and expensive, and usually focus on the traits of peak growing season and dominant species, making the scaling extension and spatiotemporal coverage of plant functional traits a great challenge. In contrast, newly emerging multi-scale hyperspectral remote sensing techniques potentially provide new avenues to easily identify and characterize functional traits. Here we first overview the principles and brief history of hyperspectral remote sensing technology for plant functional traits monitoring. Then, we detailed the principal methods for modelling the spectral-trait relationships, including empirical and semi-empirical statistical methods and inversion methods relying on physical-based modelling, among which the statistical partial least squares regression is widely used. We then used case studies to demonstrate the application while illustrating the remaining problems of plant functional traits monitoring using the hyperspectral remote sensing techniques respectively at leaf, community and landscape scales. Finally, we highlight four important future directions to advance hyperspectral remote sensing of plant functional traits, including: 1) exploring the generalizability and underlying mechanisms of spectral-trait modelling; 2) developing novel, transparent methodology that scales the spectral-trait relationships from leaf, canopy to satellite levels; 3) elucidating the pattern and drivers of remotely sensed plant functional traits and diversity across various spatiotemporal scales; and 4) investigating the linkage among environment, plant functional diversity, biodiversity and ecosystem functioning.

Key words: hyperspectral remote sensing, plant functional trait, spatiotemporal generalizability, scaling extension, spectral-trait modelling