Reference evapotranspiration estimation in data-limited environments: An interpretable stacking ensemble framework integrating heterogeneous feature selection

• A novel ETo framework fusing heterogeneous feature selection and stacking. • High accuracy (R 2 = 0.967) with only four key meteorological factors. • Physical interpretability of the seasonally varying roles of key drivers. • Significant improvement in winter ETo estimation (p < 0.001). • High transferability (R 2 > 0.960) for data-limited agricultural regions. Effective irrigation planning and agricultural water management, particularly in regions with scarce meteorological data, fundamentally depend on the accurate calculation of reference evapotranspiration (ETo). Herein, a robust and interpretable ETo estimation framework combining heterogeneous integration feature selection (HIFS) and a stacking ensemble learning model was developed using daily meteorological data (1969–2019) from 32 sites across the Central Plains of China. HIFS created a consensus feature ranking by combining outputs from three embedded learners, extreme gradient boosting (XGBoost), random forest (RF), and gradient-boosted decision tree, through a fuzzy Borda aggregation approach. This method enabled the identification of four dominant input factors: relative humidity, wind speed at 2 m, sunshine duration, and maximum temperature. The stacking ensemble model, which integrated RF, XGBoost, and multilayer perceptron at the base level with linear regression as the metamodel, achieved peak performance with this optimal input combination (R 2 = 0.9672, 95% confidence interval [CI]: 0.9653–0.969; root mean square error [RMSE] = 0.3175 mm/d, 95% CI: 0.3127–0.3223), outperforming all individual base models. Seasonal evaluation revealed that the model substantially improved winter ETo estimation (R 2 improved from 0.6033 to 0.9046; errors decreased significantly, p < 0.001), a standard limitation of traditional models. Shapley additive explanation analysis confirmed the physically meaningful and seasonally varying roles of the key drivers. Neighbor-based transfer experiments revealed strong model transferability (R 2 > 0.96) when data from neighboring sites were used for training. This study offers a data-efficient and interpretable alternative to the FAO-56 PM equation, providing crucial technological support for agrometeorological decision-making in agricultural regions with similar temperate monsoon climates. However, the applicability of the proposed approach in other zones warrants further investigation.