Scaling Functional Traits from Leaves to Canopies

Abstract

In this chapter, we begin by exploring the relationship between plant functional traits and functional diversity and how this relates to the characterization and monitoring of global plant biodiversity. We then discuss the connection between leaf functional traits and their resulting optical properties (i.e., reflectance, transmittance, and absorption) and how this related to remote sensing (RS) of functional diversity. Building on this, we briefly discuss the history of RS of functional traits using spectroscopy and imaging spectroscopy data. We include a discussion of the key considerations with the use of imaging spectroscopy data for scaling and mapping plant functional traits across diverse landscapes. From here we provide a review of the general methods for scaling and mapping functional traits, including empirical and radiative transfer model (RTM) approaches. We complete the chapter with a discussion of other key considerations, such as field sampling protocols, as well as current caveats and future opportunities.

Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

“…leaf area index and leaf angle distribution) (Asner, 1998; Roberts et al ., 2004; Ollinger, 2011). At the same time, other challenges to spaceborne retrieval of V c,max25 are associated with a multitude of issues, including sensor design, uncertainties in the retrieval of surface reflectance, the sun‐sensor geometry effect, and the mixture effect associated with the spatial resolution issue (Roberts et al ., 2004; Thompson et al ., 2019; Serbin & Townsend, 2020). Therefore, additional research is needed to understand the impacts of these issues on satellite retrievals of V c,max25 , yet new opportunities in spaceborne image spectroscopy could yield new insights (Guanter et al ., 2015; Stavros et al ., 2017; Schimel & Poulter, 2021).…”

“…Importantly, leaf reflectance spectroscopy or the measurement of reflected radiance from leaves in many narrow, continuous spectral channels across a portion of the electromagnetic spectrum (Serbin & Townsend, 2020), may fill an important role in enhancing our understanding of trait variation within and across Earth's terrestrial ecosystems. Leaf reflectance spectra are a collection of optical properties that are linked to a large number of leaf morphological and biochemical characteristics by electronic and vibrational absorption (Curran, 1989; Elvidge, 1990; Kokaly et al ., 2009).…”

Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery

“…Different plant species respond in their own way to incoming solar radiation according to their pigment, water, and biochemical content, as well as leaf and canopy structure. Thus, the variability in the remotely sensed spectra might enable detection of plant species diversity [13][14][15][16][17]. This concept represents the basis of the spectral variability hypothesis (SVH): as the number of plant species increases for a given area, the spectral diversity observed from that area should also increase [18,19].…”

Rapid estimation of photosynthetic leaf traits of tropical plants in diverse environmental conditions using reflectance spectroscopy

“…Moreover, the specific signals associated with Rubisco ( V cmax ) or, cytochrome b6f ( J max ), are likely to be a minor component of the overall reflectance signal associated with other constituents of the leaves [ 37 ]. Assumption 2), a more likely assumption, is that the PLSR captures a range of direct and indirect correlations among a host of leaf traits and properties also known to influence leaf reflectance [ 67 ] to enable the estimation of the net photosynthetic parameters. That is, the PLSR method develops a model based on the strongest covariance between the reflectance spectra and response variable so direct or indirect correlations between the response variable and other traits are likely to be equally captured.…”