Field Scale Winter Wheat Yield Estimation with Sentinel-2 Data and a Process Based Model.

Accurate and timely regional crop yield information, particularly field-level yield estimation, is essential for commodity traders and producers in planning production, growing, harvesting, and other interconnected marketing activities. In this study, we propose a novel data assimilation framework. Firstly, we construct the county-level prior and likelihood constraints for a process-based crop growth model based on the previous year's statistical yield and the current year's field observations. Then, we infer the posterior sets of model-simulated time-series LAI and the final yield of winter wheat with an MCMC (Markov chain Monte Carlo) method for each meteorological data grid of ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5). Finally, we estimate the winter wheat yield at the spatial resolution of 10 m by combining Sentinel-2 LAI and the WOFOST model in Hengshui, the prefecture-level city of Hebei province of China. The results show that the proposed framework can estimate the winter wheat yield with a coefficient of determination R-2 equal to 0.29 and mean absolute percentage error MAPE equal to 7.20% compared with field measurements. However, agricultural stress that crop growth models cannot quantitatively simulate, such as lodging, can greatly reduce the accuracy. The results also suggest good agreements with county-level statistics of the growing year with a coefficient of determination R2 equal to 0.52 and mean absolute percentage error MAPE equal to 7.19%.