Sun-induced chlorophyll fluorescence (SIF) from plants offers an effective proxy for estimating gross primary productivity (GPP) by modeling SIF-GPP relationships, a widely used method to evaluate the global carbon sink. However, most SIF-GPP models ignore SIF differences between shaded and sunlit leaves, resulting in GPP underestimation, particularly in dense vegetation. This study aims to partition the contributions of sunlit and shaded leaves to canopy SIF and GPP to refine the SIF-GPP estimation model. Data from 40 eddy covariance (EC) sites representing eight major biomes and TROPOMI SIF satellite data were used for site-specific and global-scale analyses. Our results showed that the contributions of sunlit and shaded leaves to canopy SIF were 80 % and 20 %, and to canopy GPP were 55 % and 45 %, respectively. For site-specific or satellite data, the SIF-GPP relationships were the strongest for sunlit leaves (R2 > 0.51, RMSE = 4.03 μmol m−2 s−1, p < 0.001). The new SIF-GPP model, including sunlit-shaded SIF separation, can improve the accuracy of GPP estimation (R2 = 0.53, RMSE = 4.38 μmol m−2 s−1, p < 0.001). Compared with the model established with observed data, R2 was increased by 0.1, and RMSE decreased by 13.26 μmol m−2 s−1, indicating that the ‘two-leaf’ model could notably improve the SIF-GPP model. This study confirms the different contributions of sunlit and shaded leaves to canopy SIF and GPP, and ignoring this disparity would induce systematic bias in GPP estimation. Our methods and findings on sunlit-shaded SIF separation can be referenced by other studies to enhance GPP estimation accuracy.

中文翻译：

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一种改进的瞬时总初级生产力模型，考虑了阳光照射和阴凉的叶子对冠层阳光诱导的叶绿素荧光的贡献差异


来自植物的太阳诱导叶绿素荧光 （SIF） 通过对 SIF-GPP 关系进行建模，为估计总初级生产力 （GPP） 提供了一种有效的代理，SIF-GPP 是一种广泛使用的评估全球碳汇的方法。然而，大多数 SIF-GPP 模型忽略了遮荫和阳光照射的叶子之间的 SIF 差异，导致 GPP 被低估，尤其是在茂密的植被中。本研究旨在划分日照和阴叶对冠层 SIF 和 GPP 的贡献，以改进 SIF-GPP 估计模型。来自代表 8 个主要生物群落的 40 个涡度相关 （EC） 站点的数据和 TROPOMI SIF 卫星数据用于特定站点和全球尺度的分析。结果表明，日照和阴叶对冠层 SIF 的贡献分别为 80 % 和 20 %，对冠层 GPP 的贡献分别为 55 % 和 45 %。对于特定地点或卫星数据，阳光照射的叶子的 SIF-GPP 关系最强 （R2 > 0.51，RMSE = 4.03 μmol m-2 s-1，p < 0.001）。新的 SIF-GPP 模型，包括阳光下阴影的 SIF 分离，可以提高 GPP 估计的准确性 （R2 = 0.53，RMSE = 4.38 μmol m-2 s-1，p < 0.001）。与用观测数据建立的模型相比，R2 增加了 0.1，RMSE 降低了 13.26 μmol m-2 s-1，表明“双叶”模型可以显着改善 SIF-GPP 模型。本研究证实了阳光照射和遮荫的叶子对冠层 SIF 和 GPP 的不同贡献，忽视这种差异会导致 GPP 估计的系统性偏差。我们关于阳光下阴影 SIF 分离的方法和发现可以被其他研究参考，以提高 GPP 估计的准确性。