Abstract Photosynthesis is the basis of crop yield and quality. Real-time, quantitative monitoring of crop photosynthetic parameters is important to assess crop growth status, and to predict yield and quality. In the present study, we conducted two field experiments using two rice cultivars (Japonica and Indica), and nitrogen levels and light response curves (LRCs) of different leaf positions at different growth stages were determined. The leaf maximum net photosynthesis (Pn-max) and initial quantum efficiency (α) were estimated using LRCs and then the leaf layer maximum net photosynthesis (Pnl-max) and initial quantum efficiency (αl) were estimated using the Gaussian integration method. The results showed that the dynamic change characteristics of Pnl-max and αl at the rice leaf layer under the different growth stages presented the same trend: Increasing first and then decreasing. The relationship between the photosynthetic parameters of the leaf layer and multi-spectral vegetation indices obtained from an unmanned aerial vehicle (UAV) multi-spectral reflectance showed that the modified structure-insensitive pigment index (SIPIm (R720-R550)/(R800-R680)) correlated with an R2 of 0.72 and 0.61 for Pnl-max and αl, respectively. Therefore, Pnl-max and αl of the rice leaf layer could be obtained quickly by UAV. In addition, the leaf layer light response curve (LRCl) model could be estimated by combining the canopy respiration (Rd) obtained by accumulating different leaf layers’ respiration rates with Pnl-max and αl. Daily photosynthetically active radiation (PAR) variation, measured using a QSO-S PAR sensor, was used as the input parameter of an LRCl model. This allowed the prediction of daily variation of rice canopy photosynthesis based on UAV and the LRCl model.

中文翻译：

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使用基于无人机的多光谱图像和光响应曲线模型监测水稻叶层光合作用的日变化

摘要 光合作用是作物产量和品质的基础。作物光合参数的实时、定量监测对于评估作物生长状况以及预测产量和质量非常重要。在本研究中，我们使用两个水稻品种（粳稻和籼稻）进行了两次田间试验，并确定了不同生长阶段不同叶片位置的氮水平和光响应曲线（LRC）。使用LRCs估计叶片最大净光合作用（Pn-max）和初始量子效率（α），然后使用高斯积分方法估计叶层最大净光合作用（Pnl-max）和初始量子效率（αl）。结果表明，不同生育阶段水稻叶层Pnl-max和αl的动态变化特征呈现相同趋势：先增加后减少。叶层光合参数与无人机多光谱反射得到的多光谱植被指数的关系表明，改良结构不敏感色素指数（SIPIm（R720-R550）/（R800-R680） )) 与 Pnl-max 和 αl 的 R2 分别为 0.72 和 0.61 相关。因此，无人机可以快速获取水稻叶片层的 Pnl-max 和 αl。此外，叶层光响应曲线（LRCl）模型可以通过将不同叶层呼吸速率累加得到的冠层呼吸（Rd）与Pnl-max和αl相结合来估计。使用 QSO-S PAR 传感器测量的每日光合有效辐射 (PAR) 变化用作 LRCl 模型的输入参数。