Predicting tropical plant physiology from leaf and canopy spectroscopy

A broad regional understanding of tropical forest leaf photosynthesis has long been a goal for tropical forest ecologists, but it has remained elusive due to difficult canopy access and high species diversity. Here we develop an empirical model to predict sunlit, light-saturated, tropical leaf photosynthesis using leaf and simulated canopy spectra. To develop this model, we used partial least squares (PLS) analysis on three tropical forest datasets (159 species), two in Hawaii and one at the biosphere 2 laboratory (B2L). For each species, we measured light-saturated photosynthesis (A), light and CO₂ saturated photosynthesis (A_max), respiration (R), leaf transmittance and reflectance spectra (400-2,500 nm), leaf nitrogen, chlorophyll a and b, carotenoids, and leaf mass per area (LMA). The model best predicted A [r² = 0.74, root mean square error (RMSE) = 2.9 μmol m^-2 s^-1)] followed by R (r² = 0.48), and A_max (r² = 0.47). We combined leaf reflectance and transmittance with a canopy radiative transfer model to simulate top-of-canopy reflectance and found that canopy spectra are a better predictor of A (RMSE = 2.5 ± 0.07 μmol m^-2 s^-1) than are leaf spectra. The results indicate the potential for this technique to be used with high-fidelity imaging spectrometers to remotely sense tropical forest canopy photosynthesis.