The accurate spatial upscaling of evapotranspiration (AET) models from point to regional scale is crucial for enhancing irrigation efficiency, optimizing water resource allocation, and ensuring food security. However, challenges including model parameter uncertainty and data fusion complexity have significantly limited the reliability and broad application of these models. In this study, based on the Penman-Monteith (PM) framework, three canopy resistance (rc)-based AET models were developed using observations from a Bowen Ratio Energy Balance (BREB) system and field-measured Leaf Area Index (LAI) data from a maize field in Daoli District, Harbin, Heilongjiang Province, China, collected between 2021 and 2024. These models incorporate aerodynamic resistance (the PMrc-KP model), the canopy-air temperature difference (the PMrc-IS model), and combined meteorological and crop physiological factors (the PMrc-ST model), respectively. Parameterized versions of these models were applied to estimate maize AET at 19 representative sites across Heilongjiang Province, utilizing Sentinel-2 high-resolution remote sensing data and ground meteorological observations. The model estimates were validated against ERA5-Land reanalysis data. The results indicate that the PMrc-ST model consistently outperformed the PMrc-KP and PMrc-IS models at all sites. The AET estimates from the PMrc-ST model achieved R2 values above 0.92, with RMSE ranging from 36.92 to 96.37 W m-2 across sites. The PMrc-KP model ranked second in performance, while the PMrc-IS model showed relatively lower accuracy, with notably higher RMSE and MAE values. Vapor Pressure Deficit (VPD) was identified as the primary factor influencing the performance of all three models, showing a significant positive correlation with model residuals. In contrast, LAI exhibited a significant negative correlation with the residuals of the PMrc-ST model. Regional climate differences introduced considerable spatial variability in model performance. Highest accuracy was observed at sites in humid regions, such as Baoqing and Jixi, whereas performance was constrained at sites like Anda (semi-arid) and Huma (cold, high-latitude) due to water and heat stress. This study provides valuable insights for regional farmland-scale AET estimation and demonstrates the potential application of such models for precision agricultural water management.