Surface evapotranspiration (ET) is a vital process that connects the water cycle, energy budget, and carbon cycle between the land and atmosphere. ET measurements using the eddy covariance (EC) technique typically encounter large gaps. In this study, a physics-based full-factorial scheme for filling gaps in ET from EC observations is proposed based on the decoupling model of the Penman-Monteith equation, which mechanically interconnects the full range of ET influential factors from the atmosphere, vegetation, and soil. The new method was tested and intercompared with four typical gap-filling methods, i.e., marginal distribution sampling, mean diurnal variation, constant reference evaporative fraction, and constant evaporative fraction, using the data collected at 136 sites from AmeriFlux, FLUXNET, and Tibetan Plateau Data Center over the crop, grass, forest, and other remaining land-cover types. The validation results showed that (1) the full-factorial scheme performed well for filling the artificially randomly generated hourly and daily gaps of the EC-based ET measurements, with a root mean square error (RMSE) of 28.8–96.1 W/m² for the hourly gaps and an RMSE of 19.9–38.7 W/m² for the daily gaps, (2) for filling the hourly gaps, the accuracy decreased with the increase in the gap length; for filling the daily gaps, the accuracy decreased with the increase in the gap length at first and then remained almost unchanged, (3) the full-factorial scheme outperformed the four typical methods for filling both the hourly and daily gaps, and (4) all five gap-filling methods presented the best performance at the grass sites, followed by the crop, and the worst performance at the forest sites. In conclusion, the proposed full-factorial scheme can produce reasonable gap-filled hourly and daily ET and is superior to the existing typical gap-filling methods. Therefore, it could be a good candidate for filling the ET gaps in EC measurements.

地表蒸散 (ET) 是连接陆地和大气之间的水循环、能量收支和碳循环的重要过程。使用涡流协方差 (EC) 技术的 ET 测量通常会遇到很大的差距。在本研究中，基于 Penman-Monteith 方程的解耦模型，提出了一种基于物理学的全因子方案，用于填补 EC 观测中的 ET 空白，该模型将大气、植被、和土壤。新方法与边缘分布采样、平均日变化、恒定参考蒸发分数和恒定蒸发分数四种典型的间隙填充方法进行了测试和比对，使用在 AmeriFlux、FLUXNET 和青藏高原的 136 个站点收集的数据作物上的数据中心，草、森林和其他剩余的土地覆盖类型。验证结果表明 (1) 全因子方案在填补基于 EC 的 ET 测量的人工随机生成的小时和日间隙方面表现良好，均方根误差 (RMSE) 为 28.8-96.1 W/m²小时间隔和 19.9–38.7 W/m ^{2的 RMSE}对于每日缺口，（2）对于填补小时缺口，精度随着缺口长度的增加而降低；对于填补每日缺口，准确性首先随着缺口长度的增加而下降，然后几乎保持不变，（3）全因子方案优于填补小时缺口和每日缺口的四种典型方法，以及（4）所有五种间隙填充方法在草地上的表现最好，其次是作物，在森林里表现最差。综上所述，所提出的全因子方案可以产生合理的填补空缺的每小时和每日 ET，并且优于现有的典型空缺填补方法。因此，它可能是填补 EC 测量中 ET 空白的良好候选者。