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Summary: Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (Vc,max), an important trait for modelling photosynthesis, can be inferred from other easier‐to‐measure traits. However, whether trait–Vc,max relationships are robust across different forest types remains unclear.Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area‐based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (Vc,max), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China.We found weak and forest type‐specific relationships between Vc,max and the four morphological and biochemical traits (R2 ≤ 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait–Vc,max relationships across three forest biomes into a single robust model for Vc,max (R2 = 0.77), and also accurately estimated the four traits (R2 = 0.75–0.94).These findings challenge the traditional use of the empirical trait–Vc,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising Vc,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology. [ABSTRACT FROM AUTHOR]

Aerosols can affect crop photosynthesis by altering radiation and meteorological conditions. By combining field observations, mechanistic modeling, and satellite‐retrieved solar‐induced chlorophyll fluorescence (SIF), we assessed aerosols' impacts on crop photosynthesis from leaf to regional scale. We found that the initial increase in aerosol optical depth (AOD) enhanced photosynthesis of sun leaves, shade leaves, and canopy, which reached their maximum at AOD = 0.76, 1.13, and 0.93, respectively, and then decreased. Aerosol‐induced changes in radiation regime and the concurrent high relative humidity led to such nonlinear responses. Similarly, the SIF of croplands in the North China Plain (NCP) also showed a bell‐shaped response to aerosols. The optimal AOD level at which SIF reached the maximum value varied from 0.56 to 1.04, depending on the background meteorological conditions. Approximately 76%–90% of the NCP exceeded the optimal AOD level, suggesting that stringent aerosol pollution control could promote cropland productivity in this region. Plain Language Summary: High aerosol loading could produce either a positive or negative impact on crop photosynthesis. However, experimental tools for manipulating aerosol loading in the field are lacking, creating significant uncertainty in assessing the impact of aerosols on crop yields. The cyclical fluctuations in aerosol loading in the North China Plain (NCP) provide a unique opportunity to study aerosols' effect on crop productivity. We found that the photosynthesis of sun and shade leaves and canopy reached their maximum at a moderate level of aerosol loading. Similarly, satellite‐observed solar‐induced chlorophyll fluorescence across the NCP also demonstrated that intermediate aerosols enhance crop photosynthesis at regional scales. The further analysis indicated that the changes in direct and diffuse radiation and the concurrent high air humidity together led to the nonlinear response of soybean photosynthesis to aerosol loading. Our findings suggested that stringent aerosol pollution control will increase crop productivity in the NCP. Key Points: Soybean sun and shade leaf photosynthesis show a quadratic response to aerosols, peaking at aerosol optical depth of 0.76 and 1.13, respectivelyThe changes in radiation regimes and concurrent high relative humidity lead to such nonlinear responses of leaf photosynthesisSolar‐induced chlorophyll fluorescence in the North China Plain also demonstrate that intermediate aerosols enhance crop photosynthesis [ABSTRACT FROM AUTHOR]

Accurate understanding of the variability in foliar physiological traits across landscapes is critical to improve parameterization and evaluation of terrestrial biosphere models (TBMs) that seek to represent the response of terrestrial ecosystems to a changing climate. Numerous studies suggest imaging spectroscopy can characterize foliar biochemical and morphological traits at the canopy scale, but there is only limited evidence for retrieving canopy photosynthetic capacity (e.g., maximum carboxylation rate, V c,max and maximum electron transport rate, J max). Moreover, the effect of canopy structure within forest communities on scaling up spectra-trait relationships from leaf to canopy level is not well known. To advance the spectra-trait approach and enable the estimation of key traits using remote sensing, we collected imaging spectroscopy data from an Unoccupied Aerial System (UAS) platform over two forest sites in China (a subtropical forest in Mt. Dinghu and a tropical rainforest in Xishuangbanna). At these sites, we also collected ground measurements of leaf spectra and traits, including biochemical (leaf nitrogen, phosphorus, chlorophyll, and water content), morphological (leaf mass per area, LMA) and physiological (V c,max25 and J max25) traits (n = 135 tree-crowns from 42 species across two sites). Using a partial least-squares regression (PLSR) approach, we built and tested spectra-trait models with repeated cross-validation. The spectral models developed with leaf spectra were directly transferred to canopy spectra to evaluate the effect of canopy structure. We further applied canopy spectral models to map these traits at individual tree-crown scale. The results demonstrate that (1) UAS-based canopy spectra can be used to estimate V c,max (R 2 = 0.55, nRMSE = 11.79%), J max (R 2 = 0.54, nRMSE = 12.34%), and five additional foliar traits (R 2 = 0.38–0.60, nRMSE = 10.11–13.56%) at the tree-crown scale with demonstrated generalizability across two sites; (2) canopy structure strongly affects the spectra-trait relationships from leaf to canopy level, but the effects vary considerably across foliar traits and cannot be well captured by the 4SAIL canopy radiative transfer model. UAS-based imaging spectroscopy maps large variability in all foliar traits (including physiological traits) with spatially explicit information, reproducing the field-observed inter- and intra-specific variations. These results demonstrate the capability of using UAS-based imaging spectroscopy for characterizing the variability of foliar physiological traits at individual tree-crown scale over forest landscapes and highlight the similar generalizability but different biophysical mechanisms underlying spectra-trait relationships at leaf and canopy levels. • UAS-based imaging spectroscopy well estimates canopy foliar photosynthetic capacity. • Canopy structure strongly affects the canopy-scale spectra-trait relationships. • UAS imaging spectroscopy maps trait variations at inter- and intra-specific levels. [ABSTRACT FROM AUTHOR]