Comparison between different major data assimilation algorithms on region tobacco growth simulation

While tobacco plays a significant role in the global economy, research on regional tobacco growth simulation remains limited. This study integrates the WOFOST crop model with satellite remote sensing data, focusing on the data assimilation (DA) of leaf area index (LAI) to enhance the accuracy of regional tobacco growth simulations. Field survey data were used for model calibration, providing the foundation for the analysis. The performance of four 4-Dimensional Variational Assimilation algorithms (4DVAs)—Particle Swarm Optimization (PSO), Simulated Annealing (SA), Shuffled Complex Evolution-University of Arizona (SCE-UA), and Gray Wolf Optimization (GWO)—was compared with four sequential DA algorithms (SDAs)—Ensemble Kalman Filter (EnKF), Ensemble Variational (EnVar), Ensemble Square Root Filter (EnSRF), and Particle Filter (PF). The 4DVAs were developed by integrating constraint DA Algorithms (CDAs) into the 4D-Var framework, enhancing their capability to optimize model states over a time window. Additionally, the performance of their coupled DA algorithms was evaluated. The results indicated that the coupled of SA and PF (SA-PF) achieved the best performance in terms of model accuracy. Compared to field survey data for biomass, stem mass and leaf mass, our method achieved the coefficient of determination (R²) values of 0.89, 0.86, and 0.81, respectively, with normalized root mean square error (NRMSE) values of 0.12, 0.10, and 0.09. The SA-PF coupling algorithm also performs better than some new DA algorithms. This study provides a valuable reference for regional tobacco growth simulation and data assimilation, improving the accuracy and applicability of crop growth models.