Estimating wheat yield and quality by coupling the DSSAT-CERES model and proximal remote sensing

• Particle swarm optimization (PSO) algorithm was developed to integrate remotely sensed data into the DSSAT-CERES model for estimating the grain yield and protein content of winter wheat.
• State variable of canopy nitrogen accumulation was estimated using normalized difference red edge index (NDRE).
• Estimating wheat grain yield, and especially quality, could be successfully accomplished by assimilating remotely sensed data into the DSSAT-CERES model.
• Estimating grain protein content by gluten type could improve the estimation accuracy.

Coupling remote sensing data with a crop growth model has become an effective tool for estimating grain yields and assessing grain quality. In this study, a data assimilation approach using a particle swarm optimization algorithm was developed to integrate remotely sensed data into the DSSAT-CERES model for estimating the grain yield and protein content of winter wheat. Our results showed that the normalized difference red edge index (NDRE) produced the most accurate selection of spectral indices for estimating canopy N accumulation (CNA), with R2 and RMSE values of 0.663 and 34.05 kg ha−1, respectively. A data assimilation method (R2 = 0.729 and RMSE = 32.02 kg ha−1) performed better than the spectral indices method for estimation of canopy N accumulation. Simulation of grain yield by the data assimilation method agreed well with the measured grain yield, with R2 and RMSE values of 0.711 and 0.63 t ha−1, respectively. Estimating grain protein content by gluten type could improve the estimation accuracy, with R2 and RMSE of 0.519 and 1.53%, respectively. Our study showed that estimating wheat grain yield, and especially quality, could be successfully accomplished by assimilating remotely sensed data into the DSSAT-CERES model.