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Spatial and Temporal Effects of Yield Prediction

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2026 ASABE Annual International Meeting  2601132.(doi:10.13031/aim.202601132)
Authors:   Xiaoyu Zhang, Sunoj Shajahan
Keywords:   Yield prediction, Satellite imagery, Machine learning, Vegetation indices

Abstract. Accurate and scalable crop yield prediction is crucial for ensuring food security and supporting sustainable agricultural management. This study combines Sentinel-2 satellite images with yield monitoring data from UIUC fields to evaluate yield predictions from spatial, temporal, and modeling dimensions. Combining the Matern-Kriging method, vegetation index and machine learning models, four key aspects were studied: the influence of spatial resolution on ground truth interpolation, the overall prediction accuracy using leave-one-field-out cross-validation (LOFOCV), the influence of spatial proximity of training fields, single year and multi-year prediction performance. Random forest is used as the main predictive model. The results show that a smaller Kriging grid size retains more spatial details, while larger grid leads to detail loss. LOFOCV revealed that there are significant differences in the accuracy of field predictions. It largely depends on the soil and management conditions at specific locations. Compared with fields with a longer spatial distance, training on fields with a closer spatial distance can continuously improve the prediction accuracy. Furthermore, the accuracy of single year predictions is similar to multi-year training. This means targeted, time-constrained datasets can reduce the computational burden without significantly lowering performance. Overall, integrating spatial resolution, temporal, and spatial proximity into the model can significantly enhance the robustness and applicability of the yield prediction framework, providing practical insights for precision agriculture and scalable food production monitoring.

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