Remote sensing estimation and mapping of maize yield in the Huang-Huai-Hai Plain using integrated crop growth models and deep learning

China is the world’s second largest corn producer, contributing 23% to global production and playing a vital role in stabilizing the global corn supply. Accurate estimation of corn yield is critical for ensuring food security and formulating agricultural policies. This study combined the World Food Studies (WOFOST) model with deep learning to estimate corn yield and generated a dataset of corn yield per unit area in the Huang-Huai-Hai Plain from 2014 to 2023. Using the WOFOST model, which incorporates meteorological, soil, crop, and management data, this study provides a dynamic simulation dataset of yield formation processes backed by agronomic mechanisms, thereby addressing the issue of sample scarcity in traditional statistical models. Building on this, the deep learning model, the Gated Recurrent Unit, captures nonlinear relationships in time-series data and accounts for the cumulative effects of various growth stages during crop development, thereby improving the accuracy of yield estimation. Subsequently, the initial results were corrected using statistical data to ensure closer alignment with the actual conditions. Results demonstrate that the estimated accuracy from 2014 to 2023 exceeded 85% overall, with correlation coefficients (r) ranging from 0.47 to 0.76 and RMSE values ranging from 487 to 1 168 kg·hm^–2. This method performed well even without relying on field survey data, thereby facilitating the large-scale mapping of corn yield distribution. By integrating crop models based on growth process simulations with deep learning, this method resolves the issue of sample scarcity, strengthens the role of agronomic knowledge in yield estimation, and provides new insights into crop yield estimation with the potential to be applied to other crops.