Corn as a dominant and productive cereal crop has been recognized as indispensable to the global food system and industrial raw materials. China’s corn consumption reached 2.82 × 108 t in 2021, but its production was only 2.65 × 108 t, and China’s corn industry is still in short supply. Timely and reliable corn yield estimation at a large scale is imperative and prerequisite to prevent climate risk and meet the growing demand for corn. While crop growth models are well suited to simulate yield formation, they lack the ability to provide fast and accurate estimates of large-scale yields, owing to the sheer quantity of data they require for parameterization. This study was conducted in the typical rain-fed corn belt, Liaoning province, to evaluate the applicability of our modeling practices. We developed the factors using climate data and MCD43A4 production, and built a county-level corn yield estimation model based on correlation analysis and corn growth mechanisms. We used corn yield data from the county between 2007 and 2017, leaving out 2017 for verification. The results show that our model, with an R2 (the Coefficient of Determination) of 0.82 and an RMSE (Root Mean Square Error) of 279.33 kg/hm2, significantly improved estimation accuracy compared to only using historical records and climate data. Our model’s R2 was 0.34 higher than the trend yield estimation model and 0.27 higher than the climate yield estimation model. Additionally, RMSE was reduced by 300–400 kg/hm2 compared to the other two models. The improvement in performance achieved by adding remote sensing information to the model was due to the inclusion of variables such as monitored corn growth state, which corrected the model predictions. Our work demonstrates a simple, scalable, and accurate method for timely estimation of corn yield at the county level with publicly available multiple-source data, which can potentially be employed in situations with sparse ground data for estimating crop yields.