This study introduces an innovative method for forecasting corn yield and price variations, critical for food security and strategic planning. Unlike traditional methods reliant on pre-harvest production data, which are often difficult to access, our approach utilizes satellite-derived gross primary production (GPP) data and dimension-reduction techniques to predict national corn yield and price changes. We conducted case studies in the US, Malawi, and South Africa to validate this approach, extracting predictors from annual GPP variations during peak growing seasons. We utilized various dimension reduction strategies, including spatial averaging, Empirical Orthogonal Functions (EOFs), and deep learning approaches like Autoencoders (AEs) and Variational Autoencoders (VAEs), to extract meaningful features from the GPP datasets. These extracted features serve as predictors in statistical models such as Generalized Linear Models and the Least Absolute Shrinkage and Selection Operator (LASSO), along with a neural network trained to predict variations directly from GPP-derived latent features. Model performances were evaluated using the Area Under Curve, Brier Skill Score, and Matthew Correlation Coefficient. Our results indicate that neural network models based on both AEs and VAEs exhibit superior predictive capabilities across all three countries, with the VAE excelling in the US and the AE leading in South Africa and Malawi. Similarly, the VAE outperforms other approaches for price prediction in the US and South Africa, while the AE achieves the best results in Malawi. Our study demonstrates the potential of combining satellite data and dimension reduction methods to significantly enhance large-scale corn yield and price predictions months before harvest.