Estimating leaf photosynthetic capacity using hyperspectral reflectance: Model variability and transferability

Abstract

Leaf photosynthetic capacity is a crucial parameter for characterizing plant growth status and global nitrogen-carbon cycles. While leaf trait and reflectance models have been widely applied to assess leaf photosynthetic capacity across various plant species, the variability and transferability of these estimation models remain unclear. Thus, this study investigated the variability in estimating leaf maximum carboxylation rate of Rubisco (Vcmax, μmol m−2 s−1) using hyperspectral reflectance across seven plant species datasets. To improve model transferability, we proposed model updating by adding new samples and developed a stacking model to integrate multiple regression model results to reduce variability in the predictions. The PROSPECT model, coupled with spectral derivatives and similarity metrics, was used to retrieve leaf structural and biochemical traits. Our results showed that Vcmax was significantly correlated with the contents of leaf chlorophyll (Cab) and protein (Prot), and other traits such as leaf structure, carotenoid content, and water content also influenced Vcmax. However, the strength of these correlations varied among different datasets due to differences in vegetation types, growth periods, and the number of species. Leaf trait relationships also varied with datasets, with Cab proving to be a good proxy of photosynthesis across all datasets. Leaf traits were superior to leaf reflectance in characterizing the differences between datasets. While leaf reflectance performed well in estimating Vcmax for most datasets, leaf traits were more suitable for constructing transferable estimation models of Vcmax between different datasets. Model transferability was affected by differences in datasets, such as data range and plant species. Model updating by adding 10% new samples significantly improved the assessment of Vcmax, with leaf reflectance yielding better estimation results. Our data also revealed that different models produced inconsistent results, and a stacking model combining multiple models optimized the estimation of Vcmax using leaf traits and reflectance, with the cross-validation coefficient of determination and relative root mean square error of 0.88 and 19.92%, respectively. These findings offer new methods and ideas for assessing leaf photosynthetic capacity across different agro-ecosystems. Further recalibration of the proposed model with canopy radiative transfer models and datasets would enable monitoring of plant photosynthesis at large scales.