Wen Zhuo Jianxi Huang Xiangming Xiao Hai Huang Rajen Bajgain Xiaocui Wu Xinran Gao Jie Wang Xuecao Li Pradeep Wagle
Abstract
Crop growth models are powerful tools for predicting crop growth and yield. Gross primary production (GPP) is a major photosynthetic flux that is directly linked to crop grain yield. To better understand the potential of GPP for regional crop yield estimation, in this study, a novel crop data-model assimilation (CDMA) framework was proposed that assimilates accumulative GPP estimates from the satellite-based vegetation photosynthesis model (VPM) into the WOrld FOod STudies (WOFOST) model using the ensemble Kalman filter (EnKF) algorithm to estimate winter wheat GPP and grain yield. Results showed that the WOFOST simulated GPP agreed with the GPPEC derived from eddy flux tower (R2 = 0.74 and 0.47 in 2015 and 2016, respectively). Assimilating GPPVPM into the WOFOST model improved site-scale GPP estimation (R2 = 0.87 and 0.67 in 2015 and 2016, respectively), and also improved regional-scale winter wheat yield estimates (R2 = 0.36 and 0.29; RMSE= 479 and 572 kg/ha in 2015 and 2016, respectively) compared with the open loop simulations (R2 = 0.14 and 0.10; RMSE= 801 and 788 kg/ha in 2015 and 2016, respectively). Our study demonstrated that assimilation of remotely sensed GPP optimized the results of carbon simulation in the WOFOST model and highlighted the potential of GPP for regional winter wheat yield estimation using a data assimilation framework.
Keywords
Gross primary production; Vegetation photosynthesis model; WOFOST; Data assimilation; Winter wheat yield
Assimilating remote sensing-based VPM GPP into the WOFOST model for improving regional winter wheat yield estimation.pdf