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Winter Wheat Yield Estimation by a New Way Coupling Markov Chain Monte Carlo and Ensemble Kalman Filtering

In this work, we compare the impact of two ways of representing the crop growth model uncertainty (the crop growth model was calibrated by Markov Chain Monte Carlo (MCMC)) on the subsequent assimilation of the Sentinel-2 LAI through Ensemble Kalman Filter (EnKF): generating N sets of crop parameters based on the Optimal-posterior parameter value (reference method, we name it OMCMC-EnKF) and Randomly selecting N sets of crop parameters from the most frequent parameters (our proposed method, RMCMC-EnKF). The results show that our proposed method is better than the reference model, when compared with the field measured winter wheat LAI and yield, its performance in the R2, RMSE, and uncertainty estimation is better. Furthermore, our proposed method is more robust to feedback from remote sensing data and more resistant to lower quality observations encountered during assimilation.