Leveraging Satellite Data With Machine and Deep Learning Techniques for Corn Yield and Price Forecasting

This research introduces a new method for predicting changes in corn yield and price, critical for food security. Instead of relying on difficult-to-access pre-harvest production data, our approach uses satellite-derived gross primary production (GPP) data and dimension-reduction techniques to forecast national corn yield and price changes. We conducted case studies in the U.S., Malawi, and South Africa to validate this approach, analyzing predictors from annual GPP variations during peak growing seasons. We applied dimension-reduction strategies, such as spatial averaging and empirical orthogonal functions (EOFs). Additionally, we used deep learning techniques such as autoencoders (AEs) and variational autoencoders (VAEs) to extract meaningful features from the high-dimensional GPP datasets. These features were then used as predictors in yield and price classification models based on generalized linear models (GLMs) and ElasticNet. We also considered a neural network model trained to predict yield and price variations from GPP input data directly. We evaluated the model performances using metrics such as area under the curve (AUC), brier skill score (BSS), and Matthew’s correlation coefficient (MCC). Our results indicate that dimension-reduction techniques based on AEs and VAEs provided better predictive capabilities across all three countries compared to EOF, particularly in Malawi, where the BSS increased from 0.26 with EOF to 0.81 with AE for yield and from −0.004 with EOF to 0.77 with VAE for price. This study demonstrates that integrating open-access satellite data with dimension-reduction techniques can significantly improve crop forecast accuracy, providing an accessible tool to enhance agricultural management and food security.