Remote sensing-based winter wheat yield estimation integrating machine learning and crop growth multi-scenario simulations

Accurate and timely winter wheat yield prediction is critical for effective agricultural management and food security. This study used the World Food Studies (WOFOST) model, a widely adopted crop growth simulation model, to dynamically simulate winter wheat yield under various growth scenarios to produce a simulated dataset. Based on this dataset, custom yield estimation models were developed based on available remote sensing data. Validation with field-measured and county-level statistics demonstrated a robust and spatially extensive capability for accurate yield estimation, with R2, RMSE, and MRE values of 0.57, 424.80 kg/ha, and 6.57% at the plot level, and 0.58, 345.53 kg/ha, and 4.93% at the county level, notably improving on traditional field-based methods (R2 = 0.03–0.46) that primarily rely on limited field surveys and statistical models. Model simplification showed that accuracy decreased when fewer remote sensing images were used, yet achieved reasonable estimates (two temporal phases: R2 of 0.41/0.40 at plot/county level). Findings highlighted that data collection during key growth stages is essential for accuracy, and that a dataset of at least 5,000 records suffices for reliable results. This study offers important insights and direction for enhancing yield prediction with efficient data acquisition and modeling strategies in large-scale applications.