Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach

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As the world's leading corn producer, the United States supplies more than 30% of the global corn production. Accurate and timely estimation of corn yield is therefore essential for commodity trading and global food security. Recently, several deep learning models have been explored for corn yield forecasting. Despite success, most existing models only provide yield estimations without quantifying the uncertainty associated with the predictions. Also, the traditional deep learning approaches typically require a large training set and are easily prone to overfitting when the number of samples in the training set is relatively small. To address these limitations, in this study, we developed a county-level corn yield prediction model based on Bayesian Neural Network (BNN) using multiple data sources that are publicly available, including time-series satellite products, sequential climate observations, soil property maps, and historical corn yield records. Using preceding years since 2001 for model training, the developed BNN model achieved an average coefficient of determination (R2) of 0.77 for late-season prediction across the U.S. Corn Belt in testing years 2010–2019, and outperformed five other state-of-the-art machine learning models. Detailed evaluation in three representative testing years demonstrated that the proposed BNN model could accurately estimate corn yield not only in normal years but also in abnormal years when extreme weather events happened. Moreover, the timeliness of the prediction was evaluated within the growing season with an R2~0.75 achieved by middle August, which is about 2 months before the harvest. We also assessed the predictive uncertainty, and more than 84% of the observed yield records were successfully enveloped in the 95% confidence interval of the predictive yield distribution. Our results also showed that the uncertainty level decreased steadily as time proceeded and stabilized around early August. Uncertainties in yield prediction were mainly induced by the observation noise and also related to the interannual and seasonal variabilities of environmental stress such as heat and water stress. This paper provides a robust framework for the within-season prediction of crop yield and highlights the need to obtain a deeper understanding of the effects of environmental stress on agricultural productivity and crop yield estimation.