Improving the Gross Primary Productivity Estimation by Simulating the Maximum Carboxylation Rate of Maize Using Leaf Age

Abstract

Although the maximum carboxylation rate (Vcmax) is an important parameter to calculate the photosynthesis rate for the terrestrial biosphere models (TBMs), current models could not satisfactorily estimate the Vcmax of a crop because the Vcmax is always changing during crop growth period. In this study, the Breathing Earth System Simulator (BESS) and light response curve (LRC) were combined to invert the time-continuous Vm25 (Vcmax normalized to 25 °C) using eddy covariance measurements and remote sensing data in five maize sites. Based on the inversion results, we propose a Two-stage linear model using leaf age to estimate crop Vm25. The leaf age can be readily calculated from the date of emergence, which is usually recorded or can be readily calculated from the leaf area index (LAI), which can be readily obtained from high spatiotemporal resolution remote sensing images. The Vm25 used to calibrate and validate our model was inversely solved by combining the BESS and LRC and using eddy covariance measurements and remote sensing data in five maize sites. Our Two-stage linear model (R2 = 0.71–0.88, RMSE = 5.40–7.54 μmol m−2 s−1) performed better than the original BESS (R2 = 0.01–0.67, RMSE = 13.25–18.93 μmol m−2 s−1) at capturing the seasonal variation in the Vm25 of all of the five maize sites. Our Two-stage linear model can also significantly improve the accuracy of maize gross primary productivity (GPP) at all of the five sites. The GPP estimated using our Two-stage linear model (underestimated by 0.85% on average) is significantly better than that estimated by the original BESS model (underestimated by 12.60% on average). Overall, our main contributions are as follows: (1) by using the BESS model instead of the BEPS model coupled with the LRC, the inversion of Vm25 took into account the photosynthesis process of C4 plants; (2) the maximum value of Vm25 (i.e., PeakVm25) during the growth and development of maize was calibrated; and (3) by using leaf age as a predictor of Vm25, we proposed a Two-stage linear model to calculate Vm25, which improved the estimation accuracy of GPP.