The maximum rate of Rubisco carboxylation (V_cmax) determines leaf photosynthetic capacity and is a key parameter for estimating the terrestrial carbon cycle, but its spatial information is lacking, hindering global ecological research. Here, we convert leaf chlorophyll content (LCC) retrieved from satellite data to V_cmax, based on plants' optimal distribution of nitrogen between light harvesting and carboxylation pathways. We also derive V_cmax from satellite (GOME-2) observations of sun-induced chlorophyll fluorescence (SIF) as a proxy of leaf photosynthesis using a data assimilation technique. These two independent global V_cmax products agree well ($r^{\mathrm{2}}=\mathrm{0.79},\mathrm{RMSE}=\mathrm{15.46}\mathrm{\mu }$mol m⁻² s⁻¹, P<0.001) and compare well with 3672 ground-based measurements ($r^{\mathrm{2}}=\mathrm{0.69},\mathrm{RMSE}=\mathrm{13.8}\mathrm{\mu }$mol m⁻² s⁻¹ and P<0.001 for SIF; $r^{\mathrm{2}}=\mathrm{0.55},\mathrm{RMSE}=\mathrm{18.28}\mathrm{\mu }$mol m⁻² s⁻¹ and P<0.001 for LCC). The LCC-derived V_cmax product is also used to constrain the retrieval of V_cmax from TROPical Ozone Mission (TROPOMI) SIF data to produce an optimized V_cmax product using both SIF and LCC information. The global distributions of these products are compatible with V_cmax computed from an ecological optimality theory using meteorological variables, but importantly reveal additional information on the influence of land cover, irrigation, soil pH, and leaf nitrogen on leaf photosynthetic capacity. These satellite-based approaches and spatial V_cmax products are primed to play a major role in global ecosystem research. The three remote sensing V_cmax products based on SIF, LCC, and SIF+LCC are available at https://doi.org/10.5281/zenodo.6466968 (Chen et al., 2022), and the code for implementing the ecological optimality theory is available at https://github.com/SmithEcophysLab/optimal_vcmax_R and https://doi.org/10.5281/zenodo.5899564 (last access: 31 August 2022) (Smith et al., 2022).

中文翻译：

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用于生态和地球系统研究的叶片光合能力全球数据集

Rubisco羧化的最大速率（V _cmax）决定了叶片的光合能力，是估算陆地碳循环的关键参数，但其空间信息缺乏，阻碍了全球生态学研究。在这里，我们根据植物在采光和羧化途径之间的氮最佳分布，将从卫星数据中检索到的叶绿素含量 (LCC) 转换为V _{cmax 。}我们还使用数据同化技术从卫星 (GOME-2) 对太阳诱导的叶绿素荧光 (SIF) 的观测中推导出V _{cmax作为叶片光合作用的代表。}这两个独立的全局V _cmax产品非常吻合 ($r^{\mathrm{2}}=\mathrm{0.79},\mathrm{均方根误差}=\mathrm{15.46}\mathrm{微米}$mol m ^-2  s ^-1 , P < 0.001 ) 并与 3672 次地面测量 ($r^{\mathrm{2}}=\mathrm{0.69},\mathrm{均方根误差}=\mathrm{13.8}\mathrm{微米}$对于 SIF， mol m ^-2  s ^-1和P < 0.001；$r^{\mathrm{2}}=\mathrm{0.55},\mathrm{均方根误差}=\mathrm{18.28}\mathrm{微米}$mol m ^-2  s ^-1和LCC 的P < 0.001）。LCC 衍生的 V _cmax产品还用于限制从热带臭氧任务 (TROPOMI) SIF 数据中检索 V cmax 以使用 SIF 和 LCC 信息生成优化_的V cmax产品_。这些产品的全球分布与使用气象变量的生态最优理论计算的V _cmax兼容，但重要的是揭示了有关土地覆盖、灌溉、土壤 pH 值和叶片氮对叶片光合能力影响的更多信息。这些基于卫星的方法和空间V _cmax产品已准备好在全球生态系统研究中发挥重要作用。基于SIF、LCC和SIF + LCC的三种遥感V _cmax产品可在https://doi.org/10.5281/zenodo.6466968（Chen et al., 2022）获取，实现生态最优的代码理论可在https://github.com/SmithEcophysLab/optimal_vcmax_R和 https://doi.org/10.5281/zenodo.5899564 获得（最后访问时间：2022 年 8 月 31 日）（Smith 等人，2022）。