The objective of this study is to estimate the crop yields in the key corn-producing counties of Jilin Province, China. An accurate, efficient, scalable, and cost-effective model was developed using Sentinel-2 remote sensing data. High spatial and temporal resolution was offered along with the comprehensive meteorological data. A robust framework was built to estimate the maize yield. The limitations of traditional estimation were examined using ground surveys or lower-resolution satellite imagery. These were time-consuming, resource-intensive, and prone to errors, due to sampling biases or limited coverage. Sentinel-2 data was incorporated to provide a continuous and consistent view of crop growth patterns over a large area. The vegetation productivity model (VPM) was integrated to calibrate the yield conversion coefficient. VPM approach was used to estimate the crop biomass, according to the vegetation indices from remote sensing data. The biomass was converted directly into the yield. A yield conversion coefficient was also required to consider the agronomic conditions and crop varieties in the study area. The accuracy and relevance of the model were then enhanced to fine-tune the coefficient with the local yield data. The dynamic variables were integrated into the dynamic observation index in the VPM model. The relatively stable parameters were integrated into the conversion coefficient. The accuracy of yield estimation of the improved model (R2=0.53, RMSE=0.81, MRE=9.40%, NRMSE=11.73%) was superior to the traditional models (R2=0.35, RMSE=1.03 t/hm², MRE=13.19%, NRMSE=14.97%). The obtained model was then applied to estimate the corn yields in the target counties of Jilin Province, where the yield range of maize per unit area was found to be 7-13 t/hm². There was a distinct spatial pattern, where the higher yields were concentrated in the central regions and then gradually decreased towards the peripheries. This pattern was aligned with the geographical features, including soil fertility, irrigation availability, and climatic conditions. The high-resolution Sentinel-2 data was used to better capture these subtle variations in the yield patterns. Sensitivity analysis was conducted to further validate the robustness of the model. A systematic investigation was implemented to explore the impact of various factors, including the spatial resolution of remote sensing data, the vegetation indices, and the calibrated conversion coefficient. The precision of yield estimation was enhanced to employ the precise yield conversion coefficients, high-resolution remote sensing data, and maize planting distribution data. The rest factors were the spatial resolution of remote sensing data, the spatial resolution of crop distribution data, and the fineness of yield conversion coefficients. Furthermore, the future research direction was compared to further improve the accuracy of the model. The significant implications were achieved in agricultural modernization and food security. The timely and accurate information was obtained on crop yields, in order to optimize the planting strategies and decision-making on resource use. The findings can provide valuable insights into the influencing factors on agricultural productivity, and sustainability, particularly for food security at the regional and global levels.