We compiled a comprehensive dataset of leaf traits and spectra to explore the transferability of predictive models. We found that while PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. The findings underscored the importance of greater spectral diversity in model training to boost its transferability.
Ji, F., Li, F., Hao, D., Shiklomanov, A.N., Yang, X., Townsend, P.A., Dashti, H., Nakaji, T., Kovach, K.R., Liu, H., Luo, M. and Chen, M. (2024), Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset. New Phytologist https://doi.org/10.1111/nph.19807