MODIS-based Maize Vegetation Indices and Yield Maps for the Seven US Corn Belt States

This study developed a remote sensing-integrated crop model (RSCM) incorporating machine learning (ML) methodologies to predict maize yields across the US Corn Belt. The framework integrated MODIS vegetation indices with AgERA5 meteorological data to simulate leaf area index and biomass accumulation at 500 m resolution. Simulations across seven states (2012-2020) representing 70.7% of US maize cultivation achieved yield predictions ranging from 9.1±2.15 t ha-1 (South Dakota) to 11.7±1.96 t ha-1 (Iowa), with inter-regional variability of 0.2 to 2.6 t ha-1 reflecting diverse environmental conditions. The RSCM-ML approach successfully bridges traditional crop modeling and remote sensing integration, providing standardized geospatial datasets with broad applicability to diverse crop systems. The resulting standardized geospatial dataset is openly available via the National Research Data Platform and can support applications in crop monitoring, yield forecasting, and agroclimatic impact assessment. Future improvements through higher-resolution field observations and ensemble modeling will further enhance prediction accuracy for sustainable agricultural monitoring.