Journal
AGRICULTURAL AND FOREST METEOROLOGY
Volume 291, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.agrformet.2020.108098
Keywords
UAV; Light response curve; Maximum net photosynthetic rate; Initial quantum efficiency; Modified structural insensitive pigment index; SIPIM
Categories
Funding
- National Natural Science Foundation of China [31971784]
- National Key R and D Program [2018YFD0300805]
- Jiangsu Collaborative Innovation Center for Modern Crop Production
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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 (alpha) were estimated using LRCs and then the leaf layer maximum net photosynthesis (Pnl-max) and initial quantum efficiency (alpha(1)) were estimated using the Gaussian integration method. The results showed that the dynamic change characteristics of Pnl-max and alpha(1) 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(R-720-R-550)/(R-800-R-680)) correlated with an R-2 of 0.72 and 0.61 for Pnl-max and alpha(1),, respectively. Therefore, Pnl-max and alpha(1) of the rice leaf layer could be obtained quickly by UAV. In addition, the leaf layer light response curve (LRC1) model could be estimated by combining the canopy respiration (R-d) ob- tained by accumulating different leaf layers' respiration rates with Pnl-max and alpha(1). Daily photosynthetically active radiation (PAR) variation, measured using a QSO-S PAR sensor, was used as the input parameter of an LRC1 model. This allowed the prediction of daily variation of rice canopy photosynthesis based on UAV and the LRC1 model.
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