Fengqi Wu, Shuwen Liu, Constantin M Zohner, Philip A Townsend, Thomas W Crowther, Josep Peñuelas, Daryl Yang, Nan Yang, Tingting Dong, Weiying Xu, Zhihui Wang, Xiaojuan Liu, Guanhua Dai, Jinlong Dong, Sandra M Durán, Fabian D Schneider, Yuan Zeng, J Hans C Cornelissen, Jens Kattge, Jin Wu, Gregory P Asner, Jeannine Cavender-Bares, Peter B Reich, Zhengbing Yan
Global trait axes reveal overarching dimensions of plant functional variation. However, how these dimensions are spatially organized within and across forest types remains unclear. We combined drone-based full-range imaging spectroscopy with crown-level measurements of 16 physiological, morphological and biochemical traits across temperate, subtropical and tropical forests in China to enable spatially-explicit trait mapping. Through site-training scenario, leaf-to-canopy scaling and spectral-domain modelling tests, we find that reliable canopy trait retrieval depends not only on trait and spectral coverage, but also on preserving trait-spectral relationships across sites and scales. Spectral predictions recovered observed multivariate covariation, summarizing crown variation into a leaf-economics dimension and two additional biochemical dimensions related to hydro-thermal regulation and defence/metabolism. Mapping these dimensions revealed distinct community-level trait organization alongside substantial species- and crown-level variation within forests. These findings link remotely sensed trait retrieval to environmental filtering and plant functional differentiation, providing a scalable framework for monitoring forest functional diversity.