1Jiangsu Collaborative Innovation Center for Modern Crop Production/Key Laboratory of Crop Physiology and Ecology in Southern China, Nanjing Agricultural University, Nanjing, China.
2International Institute for Earth System Sciences, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing University, Nanjing, China
3State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
4These authors contributed equally to this work
| Received 23 Oct 2022 |
Accepted 12 Apr 2023 |
Published 09 May 2023 |
Enhancing the photosynthetic rate is one of the effective ways to increase rice yield, given that photosynthesis is the basis of crop productivity. At the leaf level, crops’ photosynthetic rate is mainly determined by photosynthetic functional traits including the maximum carboxylation rate (Vcmax) and stomatal conductance (gs). Accurate quantification of these functional traits is important to simulate and predict the growth status of rice. In recent studies, the emerging sun-induced chlorophyll fluorescence (SIF) provides us an unprecedented opportunity to estimate crops’ photosynthetic traits, owing to its direct and mechanistic links to photosynthesis. Therefore, in this study, we proposed a practical semimechanistic model to estimate the seasonal Vcmax and gs time-series based on SIF. We firstly generated the coupling relationship between the open ratio of photosystem II (qL) and photosynthetically active radiation (PAR), then estimate the electron transport rate (ETR) based on the proposed mechanistic relationship between SIF and ETR. Finally, Vcmax and gs were estimated by linking to ETR based on the principle of evolutionary optimality and the photosynthetic pathway. Validation with field observations showed that our proposed model can estimate Vcmax and gs with high accuracy (R2 > 0.8). Compared to simple linear regression model, the proposed model could increase the accuracy of Vcmax estimates by >40%. Therefore, the proposed method effectively enhanced the estimation accuracy of crops’ functional traits, which sheds new light on developing high-throughput monitoring techniques to estimate plant functional traits, and also can improve our understating of crops’ physiological response to climate change.
