Improved global estimation of seasonal variations in C3 photosynthetic capacity based on eco-evolutionary optimality hypotheses and remote sensing

Abstract

The maximum carboxylation rate of plant leaves (V_cmax) at 25°C (V_cmax25) is a fundamental parameter in terrestrial biosphere models (TBMs) to estimate carbon assimilation of C3 biomes. It has been reported that ignoring the seasonal variations in V_cmax25 induces considerable uncertainties in TBMs. Recently, a model was developed to estimate V_cmax25 of C3 biomes mechanistically from climate data based on eco-evolutionary optimality hypotheses, which hypothesized that plants acclimate to the environment to achieve maximum carbon assimilation with minimum related costs. However, uncertainties of this optimality-based model (EEO model) have been found to correlate to leaf nitrogen content, partly due to the lack of parameterization on how the acclimation of V_cmax25 is constrained by photosynthetic nitrogen other than that in RuBisCO. This constraint could be parametrized by remote sensing methods globally. In this study, we developed remote sensing methods to estimate leaf absorptance of radiation based on MODIS LCC (leaf chlorophyll content) data and the ratio of the maximum electron transport rate of plant leaves (J_max) to V_cmax at 25°C (r_JV25) based on TROPOMI SIF (solar-induced chlorophyll fluorescence) data (RS-r_JV25). These two parameters contain photosynthetic nitrogen information related to light harvesting, electron transport, and carboxylation, and they were then incorporated into the EEO model to constrain how V_cmax25 acclimates to the environment. The simulated V_cmax25 constrained by MODIS LCC and RS-r_JV25 agreed well with seasonal variations in field-measured V_cmax25 at 18 sites (R² = 0.76, RMSE = 13.40 µmol·m^− 2·s^− 1), showing better accuracy than the simulation without incorporating leaf absorptance and r_JV25 (R² = 0.63, RMSE = 31.59 µmol·m^− 2·s^− 1). Our results indicated that variations in leaf absorptance and r_JV25 constrained the acclimation of V_cmax25 to the environment and contributed to the variation in V_cmax25 that cannot be fully captured by environmental factors alone in the EEO model. The remote-sensing-based leaf absorptance and r_JV25 captured the sensitivity of these two parameters to environmental conditions on the global scale. The influence of leaf absorptance on V_cmax25 was primarily affected by the irradiance level, while r_JV25 was determined by the growing season mean temperature. The simulated V_cmax25 had large spatiotemporal variations on the global scale, and the environment drove the variation pattern more greatly than the biome distribution. With reasonably accurate seasonal variations in V_cmax25, this study can help improve the global carbon cycle and leaf trait modelling.