The maximum carboxylation rate of plant leaves () at 25 °C () 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 induces considerable uncertainties in TBMs. Recently, a model was developed to estimate 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 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 () to at 25 °C () based on TROPOMI SIF (solar-induced chlorophyll fluorescence) data (RS-). 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 acclimates to the environment. The simulated constrained by MODIS LCC and RS- agreed well with seasonal variations in field-measured at 18 sites (R = 0.76, RMSE = 13.40 μmol·m·s), showing better accuracy than the simulation without incorporating leaf absorptance and (R = 0.63, RMSE = 31.59 μmol·m·s). Our results indicated that variations in leaf absorptance and constrained the acclimation of to the environment and contributed to the variation in that cannot be fully captured by environmental factors alone in the EEO model. The remote-sensing-based leaf absorptance and captured the sensitivity of these two parameters to environmental conditions on the global scale. The influence of leaf absorptance on was primarily affected by the irradiance level, while was determined by the growing season mean temperature. The simulated 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 , this study can help improve the global carbon cycle and leaf trait modelling.

中文翻译：

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基于生态进化最优性假设和遥感改进 C3 光合能力季节变化的全球估计


25°C 时植物叶片的最大羧化率 () 是陆地生物圈模型 (TBM) 中估计 C3 生物群落碳同化的基本参数。据报道，忽略季节变化会导致隧道掘进机产生相当大的不确定性。最近，开发了一种模型，根据生态进化最优假设，根据气候数据机械地估计 C3 生物群落，该假设假设植物适应环境，以最小的相关成本实现最大的碳同化。然而，这种基于最优性的模型（EEO 模型）的不确定性被发现与叶片氮含量相关，部分原因是缺乏关于除了 RuBisCO 之外的光合氮如何限制 的适应的参数化。这一约束可以通过全球遥感方法进行参数化。在本研究中，我们开发了基于 MODIS LCC（叶片叶绿素含量）数据和基于 TROPOMI SIF 的植物叶片最大电子传输速率 () 与 25 °C 时 () 的比率的遥感方法来估算叶片的辐射吸收率（太阳诱导的叶绿素荧光）数据（RS-）。这两个参数包含与光捕获、电子传输和羧化相关的光合氮信息，然后将它们纳入EEO模型中以约束对环境的适应方式。 MODIS LCC 和 RS- 约束的模拟与 18 个站点现场测量的季节变化非常吻合（R = 0.76，RMSE = 13.40 μmol·m·s），显示出比未考虑叶片吸收率的模拟更好的精度（R = 0.63，RMSE = 31.59 μmol·m·s)。 我们的结果表明，叶片吸收率的变化限制了对环境的适应，并导致 EEO 模型中仅靠环境因素无法完全捕获的变化。基于遥感的叶片吸收率并捕获了这两个参数对全球范围内环境条件的敏感性。叶片吸收率的影响主要受辐照度水平的影响，同时还受生长季平均温度的影响。模拟在全球范围内存在较大的时空变化，环境对变化模式的驱动力大于生物群落分布。通过相当准确的季节变化，这项研究可以帮助改进全球碳循环和叶子性状建模。