Crop evapotranspiration (ET) is an essential part of agricultural water consumption, and robust monitoring of remote sensing (RS)-based ET at the field scale improves agricultural water management against water shortages. In this study, we propose a high-resolution optical RS-driven daily ET estimation framework coupling water vaporization and carbon assimilation based on Sentinel-2 satellite data. To determine if the proposed framework is accurate compared with flux observations, three tower sites are chosen (Guantao and Huailai from the Haihe Basin; Daman from the Heihe Basin), with a total of four years of observations adopted for model validation. The correlation coefficient R ranges from 0.870 to 0.912, and the RMSE ranges from 0.89 to 1.21 mm/day. Sensitivity analyses indicate that ET is most sensitive to air temperature, followed by ambient CO₂ concentration and absorbed shortwave radiation, which provides indications into potential future farming strategies to confront global climate change. Finally, we discuss the scale effects on the proposed model at the field scale. Results from three sites show that for a larger area of interest (AOI) the impact of scales increases. This research provides insights into ET calculations across several spatial scales and application potential in precision agricultural water management.

作物蒸散量（ET）是农业用水的重要组成部分，在田间规模上对基于遥感（RS）的ET进行强大的监控可改善农业用水管理，以应对水资源短缺的情况。在这项研究中，我们提出了基于Sentinel-2卫星数据的结合水汽化和碳同化的高分辨率光学RS驱动的每日ET估算框架。为了确定所提出的框架与通量观测值相比是否准确，选择了三个塔点（海河盆地的瓜岛和怀来；黑河盆地的达曼），并采用了四年的观测值进行模型验证。相关系数R的范围为0.870至0.912，RMSE的范围为0.89至1.21 mm /天。敏感性分析表明，ET对气温最敏感，₂集中和吸收的短波辐射，这为应对全球气候变化的潜在未来农业战略提供了迹象。最后，我们讨论了在野外规模对拟议模型的规模效应。来自三个站点的结果表明，对于较大的关注区域（AOI），规模的影响会增加。这项研究提供了在几个空间尺度上进行ET计算的见识，以及在精确农业用水管理中的应用潜力。