Evapotranspiration (ET) is a key component of the water cycle, and accurate estimation of reference crop evapotranspiration (ET_o) is essential for irrigation management. To build a precise, lightweight ET_o estimation model, this study takes key meteorological factors as inputs and applies machine learning models and hybrid models Crested Porcupine Optimizer kernel extreme learning machine (CPO-KELM), the Dung Beetle Optimizer Algorithm KELM (DBO-KELM), and Particle Swarm Optimization KELM (PSO-KELM) to estimate ET_o at 38 meteorological stations across China’s seven major geographical regions. The results indicate that maximum temperature (T_max), average temperature (T_ave), and relative humidity (RH) are the primary factors affecting ET_o and were therefore used as model inputs. The standalone kernel extreme learning machine (KELM) model shows acceptable ET_o estimation performance, with R², RMSE, MAE, and NSE ranging from 0.802-0.885, 0.512-0.911, 0.464-0.970, and 0.802-0.885, respectively. Hybrid models outperform the standalone KELM, among which CPO-KELM is the most accurate: its R², RMSE, MAE, and NSE range from 0.881-0.942, 0.413-1.147, 0.284-0.763, and 0.881-0.942. At the regional scale, the CPO-KELM model exhibits its best performance in the Northeast and North China regions, with R², RMSE, MAE, and NSE ranging from 0.923-0.936, 0.413-0.511, 0.284-0.358, and 0.923-0.936, respectively. In contrast, its weakest performance is observed in parts of South China and Northwest China, with R², RMSE, MAE, and NSE ranging from 0.881-0.905, 0.675-1.147, 0.506-0.763, and 0.881-0.905. Compared to standalone KELM, CPO-KELM improves accuracy significantly: R² and NSE rise by 6.4%-9.9%, while MAE drops by 21.3%-38.8%. Thus, the hybrid CPO-KELM model effectively enhances ET_o estimation accuracy across China’s regions. Therefore, the proposed CPO-KELM hybrid model provides a high-accuracy and lightweight alternative for ET_o estimation in data-scarce regions, and offers reliable technical support for intelligent water resources management and irrigation optimization across diverse climatic zones in China.

Soil moisture is a critical component of the soil-plant-atmosphere continuum (SPAC) in fruit trees. However, high-precision monitoring of orchard soil moisture at the regional scale still remains a challenge. This study presents a two-stage feature space model to estimate root zone soil moisture using UAV remote sensing data. The results indicate that the temperature-leaf area index (TLDI) is negatively correlated with soil water content. The upper triangular space performs highly effectively for deep soil moisture inversion, with R² values ranging from 0.56 to 0.66, RMSE between 0.20 and 0.27, and RPD from 1.25 to 1.50. Conversely, the lower triangular space yields superior results for shallow soil moisture inversion, with R² values between 0.67 and 0.82, RMSE from 0.15 to 0.19, and RPD between 1.67 and 2.09. The results suggest that the lower triangular space is optimal for shallow soil moisture inversion, while the upper triangular space is more suited for deep soil moisture inversion. This study presents a novel approach for estimating deep soil moisture in orchards, providing a theoretical basis for improving soil moisture management.

As a crucial fruit tree crop, the health and yield of apple trees are intricately linked to soil moisture conditions. This study aimed to integrate the enhanced WOFOST model with the HYDRUS model to simulate the growth and development of apple trees, as well as the dynamics of soil moisture under varying degrees of water deficit. The outputs of evapotranspiration (ET₀) and leaf area index (LAI) from the WOFOST model during the apple growth phase were specifically integrated with HYDRUS-1D. These parameters served as intermediaries to assess the impact of different water deficit scenarios on apple tree growth and soil moisture content. The experimental design included three levels of water deficit treatments in addition to control, with irrigation volumes for the deficit treatments set at 85%, 70%, and 55% of the control’s volume, respectively. The model-predicted LAI across all irrigation treatments exhibited an R² range of 0.89-0.95, a normalized root mean square error (NRMSE) between 8.02% and 14.57%, and yield prediction errors ranging from 6.27% to 9.61%, closely aligned with empirical data. The accuracy of simulated soil moisture content was enhanced in the 0-30 cm layer, with a slight decrease in accuracy observed in the 30-60 cm layer. For each irrigation treatment, the R² values for simulated soil moisture content ranged from 0.77 to 0.89 in the 0-30 cm layer and from 0.75 to 0.81 in the 30-60 cm layer. This study validated the capability of the WOFOST-HYDRUS model to accurately simulate the effects of varied water deficit treatments on soil moisture, LAI, and apple tree yield, providing valuable insights for developing optimal irrigation strategies.