Harnessing data-driven approaches to predicting corn yields across different weather zones
| Authors: | Weiwei Xie, Yanbo Huang, Qingmin Meng, Bo Tang, Ying Ouyang |
| Year: | 2026 |
| Type: | Scientific Journal |
| Station: | Southern Research Station |
| DOI: | https://doi.org/10.1117/1.JRS.20.024501 |
| Source: | Journal of Applied Remote Sensing |
Abstract
Forecasting corn yields with precision across diverse weather zones is vital to supporting food supply resilience and sustainable agriculture. We introduce a data-integrated modeling framework applied across the Greater Mississippi River Basin- one of the largest corn-producing regions in the United States. Using MODIS-based NDVI time series, historical meteorological data, and spatial crop coverage, we investigate the relationships between vegetation health indicators and corn yields under varying climatic conditions. A new vegetative metric is introduced to enhance correlation with yield outcomes. Both statistical regressions and advanced machine learning models-including support vector regression, XGBoost, and Gaussian processes-are employed. Among these, XGBoost demonstrated the highest predictive accuracy. The results underscore the benefits of combining multisource data with nonlinear models for regionally robust and scalable corn yield estimation.