Characterizing key foliar functional traits of subtropical evergreen forests in South China using leaf and UAV-based spectroscopy

Spectroscopy provides an efficient way of predicting foliar functional traits at both leaf and canopy scales. Previous studies mainly used hyperspectral data from airborne and spaceborne platforms, and few studies assessed the feasibility of the more cost-effective and flexible unmanned airborne vehicle (UAV) −based imaging spectroscopy. Moreover, the research was mainly conducted in temperate and tropical biome, with less attention to subtropical evergreen forests. This study aims to characterize key foliar functional traits of subtropical evergreen forests in South China using leaf and UAV-based spectroscopy data. Dry and fresh leaf spectra as well as key foliar functional traits (leaf chlorophyll and carotenoids, water, dry matter, nitrogen, phosphorus, and potassium) were collected from 360 samples over 97 species across four field sites. For the site Heshan, we also collected UAV-based hyperspectral imageries at two dates. Partial least squares regression (PLSR) and random forest regression (RF) were used to link foliar traits with dry leaf spectra, fresh leaf spectra and canopy spectra, respectively. Cross-site and cross-season validations were performed to assess model transferability. Results showed that dry and fresh leaf spectra could estimate most foliar traits with comparable accuracies (normalized RMSE (NRMSE) < 20 %). Leaf trait models can be transferred across sites and species. Canopy models could provide accurate estimations of pigments, N_mass, N_area, C/N and N/P (NRMSE = 12.92 – 21.02 %), but had low transferability across seasons due to phenological differences in both foliar traits and canopy spectra. PLSR outperformed RF in predicting foliar traits with leaf spectra, whereas RF achieved better estimations at the canopy scale. Trait maps from PLSR and RF were consistent, and both can capture the seasonal variation of foliar traits. We conclude that in-situ measurements covering the variability of foliar functional traits and spectra are needed to develop robust and transferable models. Our study provides a template for characterizing key foliar functional traits in subtropical evergreen forests and can promote our understanding of ecosystem functioning and processes.