Improved Estimation Of Gross Primary Production Of Paddy Rice Cropland With Changing Model Parameters Over Phenological Transitions

Paddy rice is one of the main grain crops in the world. Accurate estimations of the gross primary production (GPP) of paddy rice are essential for assessing rice grain production and monitoring the carbon cycle in paddy fields with the aim of providing ideal conditions for crops throughout the growing season. Several studies have demonstrated the advantages of combining the eddy covariance technique with remotely sensed data to model GPP at CO2 eddy flux tower sites. As paddy rice continuously changes during its growth and development, and important growth events frequently occur, it is critical to observe the growing conditions at various stages of the process. To better understand the variations in GPP at different growth stages, two key parameters that drive the vegetation photosynthesis model (VPM) are analyzed and estimated at various phenological phases. Specifically, general piecewise logistic functions are used to extract phenological transitions from data at four paddy rice flux tower sites. The maximum light-use efficiency (LUE) and optimum temperature are estimated from these phenological transitions, and these indicators are used to drive the VPM to simulate GPP over multiple years at the four sites. The simulation results show that GPP based on our phenological transition-based VPM (GPP(PVPM)) agrees reasonably well with the variations of GPP estimated from CO2 flux data (GPP(EC)) (R-2 > 0.9). In addition, a comparison indicates that GPP(PVPM) tracks the seasonal dynamics of GPP(EC) better than GPP estimated from the original VPM. Furthermore, GPP based on the improved maximum LUE is lower than GPP(EC) at most flux sites and GPP based on the improved optimum temperature is higher than GPP(EC). These comparisons imply that the maximum LUE and optimum temperature estimated in the phenological transitions of paddy rice are beneficial to enhance the accuracy of GPP estimation. The improved estimation of GPP provides phenological insights into the temporal dynamics of vegetation photosynthesis in paddy fields.