Monitoring daily variation of leaf layer photosynthesis in rice using UAV-based multi-spectral imagery and a light response curve model

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.