## 2 Functional Traits--From Field to Remotely Sensed Observations
Functional traits at the leaf level include pigments (chlorophyll, carotenoids, anthocyanins), nutrients (nitrogen, phosphorus, calcium, potassium, magnesium, iron), structural compounds (lignin, cellulose, polyphenols), water content (equivalent water thickness, EWT), and mass per unit area (LMA), whereas at the plant level, tree height and canopy characteristics (e.g., size and shape, leaf area index, leaf angle distribution, fractional cover) [15; 38; 67; 68; 80; 81; 82; 83]. At the individual level, both sets of traits are related to light capture and growth, photoproduction, stress resistance and defense, maintenance, and metabolism. At the ecosystem level, these traits are informative of photosynthesis, primary production, carbon storage, nutrient cycling, decomposition, allocation and growth, stress resistance, and hydraulic regulation. The retrieval of leaf- and plant-level functional traits from optical data is based on the development of relationships between trait measurements and individual bands, band indices, or the whole spectra via parametric and non-parametric regression, physically based, and hybrid modeling approaches (Figure 2a, Stage 2; [16; 77; 84; 85; 86; 87; 88]). Parametric approaches include models that establish relationships between a trait of interest and individual bands, spectral indices [78; 89; 90; 91], metrics derived from the shape of spectra [92; 93], or uncorrelated, synthetic variables derived through principal component analysis (PCA) [65; 94]. Non-parametric approaches often use the entire spectrum and include linear (e.g., partial least squares regression--PSLR), e.g., [80; 95; 96; 97], and non-linear, including machine learning models, e.g., [98; 99]. Physical-based approaches make use of coupled leaf-canopy radiative transfer models (RTM) to estimate canopy reflectance via forward mode or leaf traits by model inversion; these models require look-up tables as well as trait data sets calibrated both in the lab and field [100; 101; 102; 103]. Finally, hybrid models combine both approaches [104; 105; 106]. Scaling and mapping plant functional traits (Figure 2a,b, Stages 3-4) are often part of large studies aimed at the development of new sensors, complex instruments, and applications (Figure 2a, Stages 1-2; [79; 107; 108; 109]), yet they may also take place in other contexts [110]. These studies consider retrieval methods, conduct simulations, collect in situ field and sensor calibration and validation measurements, and pre-process the imagery to different degrees. Central to scaling and mapping are in situ measurements of canopy reflectance at relatively homogenous sampling points using field spectroradiometers or complex airborne instruments and/or estimates derived from RTMs (Figure 2a, Stage 1-2). Sampling points may represent elementary sampling units (ESU) within plots matching pixels of a given airborne or spaceborne sensor, tree crown pixels or full tree crowns, and field subplots within plots whose size and shape may represent pixels of a given sensor [46; 80; 82; 109; 111]. Extensive pre-processing of air- and spaceborne multispectraland hyperspectral data may involve data fusion, estimating fractional vegetation cover, masking out clouds, bare ground, and tree-canopy shade, delineating tree canopies, and characterizing canopy geometry (Figure 2a, Stage 1b; [15, 107, 112]). At this stage, the models linking optical data with leaf- and plant-level traits are applied to the pre-processed remotely sensed data to scale up predictions and map these traits at pixel, plant, image scene, and region levels (Figure 2a Stage 3; [16, 38, 82]). Scaling may involve averaging pixel-level traits for individual tree crowns [82, 113] or averaging the spectra of multiple pixels within sampling plots [12, 38] or weighing vegetation indices or traits by the corresponding plant abundance, e.g., tree crown area [80, 114].