Predicting leaf traits of temperate broadleaf deciduous trees from hyperspectral reflectance: can a general model be applied across a growing season?

Field spectroscopy is a powerful tool for monitoring leaf functional traits in situ, but it remains unclear whether universal statistical models can be developed to predict traits from spectral information, or whether recalibration is necessary as conditions vary. In particular, multiple leaf traits vary simultaneously across growing seasons, and it is an open question whether these temporal changes can be predicted successfully from hyperspectral data. To explore this question, monthly changes in 21 physiochemical leaf traits and plant spectra were measured for eight deciduous tree species from the UK. Partial least-squares regression (PLSR) was used to evaluate whether each trait could be predicted from a single PLSR model from reflectance spectra, or whether species- and month-level models were needed. Physiochemical traits and spectra varied greatly over the growing season, although there was less variation among mature leaves harvested between June and September. Importantly, leaf spectroscopy was able to predict seasonal variations of most leaf traits accurately, with accuracies of prediction generally higher for mature leaves. However, for several traits, the PLSR estimation models varied among species, and a single PLSR model could not be used to make accurate species-level predictions. Our findings demonstrate that leaf spectra can successfully predict multiple functional foliar traits through the growing season, establishing one of the fundamentals for monitoring and mapping plant functional diversity in temperate forests from air- and spaceborne imaging spectroscopy.

Automated summary

Field spectroscopy is a powerful tool for accurately predicting seasonal changes in leaf functional traits, with better predictions for mature leaves. However, the models vary among species, making it difficult to accurately predict leaf traits for different species using a single model.