In response to increasing global food demand and the significant water requirements of rice cultivation, this study aims to enhance water use efficiency in rice farming. Focusing on Jiayu County, a subtropical humid region in China, where rice is grown as a single crop every year, we investigated temporal variations in rice water requirements and the influence of effective rainfall on irrigation strategies. Data were collected from an experimental station within the Sanhulianjiang Reservoir in Jiayu County. Utilizing the Mann–Kendall trend test and the Seasonal–Trend Decomposition using the LOESS (STL) method, we analyzed historical data on rice water requirement (ET_c) and effective rainfall ($R_e$ ). Our findings reveal that annual water requirements for rice range between 432 mm and 746 mm, with peaks corresponding to critical growth stages such as tillering and jointing–booting. Effective rainfall contributes significantly to meeting these needs, providing 27–35% of the total water requirement during specific periods. Developed water-saving irrigation strategies, including optimized irrigation scheduling and the introduction of drought-resistant rice varieties, demonstrate a potential reduction in irrigation demands by approximately 33.84%. This study underscores the importance of integrating effective rainfall data into irrigation practices to enhance water use efficiency and promote sustainable rice production amidst climate variability challenges.

Leaf water content (LWC) and chlorophyll content (CHL) are pivotal physiological indicators for assessing citrus growth and stress responses. However, conventional measurement techniques—such as fresh-to-dry weight ratio and spectrophotometry—are destructive, time-consuming, and limited in spatial and temporal resolution, making them unsuitable for large-scale monitoring. To achieve efficient large-scale monitoring, this study proposes a synergistic inversion framework integrating UAV multispectral remote sensing with intelligent optimization algorithms. Field experiments during the 2024 growing season (April–October) in western Hubei collected 263 ground measurements paired with multispectral images. Sensitive spectral bands and vegetation indices for LWC and CHL were identified through Pearson correlation analysis. Five modeling approaches—Partial Least Squares Regression (PLS); Extreme Learning Machine (ELM); and ELM optimized by Particle Swarm Optimization (PSO-ELM), Artificial Hummingbird Algorithm (AHA-ELM), and Grey Wolf Optimizer (GWO-ELM)—were evaluated. Results demonstrated that (1) VI-based models outperformed raw spectral band models; (2) the PSO-ELM synergistic inversion model using sensitive VIs achieved optimal accuracy (validation R²: 0.790 for LWC, 0.672 for CHL), surpassing PLS by 15.16% (LWC) and 53.78% (CHL), and standard ELM by 20.80% (LWC) and 25.84% (CHL), respectively; and (3) AHA-ELM and GWO-ELM also showed significant enhancements. This research provides a robust technical foundation for precision management of citrus orchards in drought-prone regions.

Evaptotranspiration (ET_c) is a crucial link in the farmland water cycle process. To accurately obtain the citrus ET_c in different slope positions, the METRIC, RSEB, and FAO Penman–Monteith (P-M) models were constructed based on unmanned aerial vehicle (UAV) multispectral images to invert the ET_c values. The ET_c of citrus calculated by the P-M model was used as a reference standard, and the accuracy of the ET_c inversion was evaluated by the METRIC model and the RSEB model. The results showed that the R², RMSE, and SE of the METRIC model and the RSEB model were 0.396 and 0.486, 4.940 and 3.010, and 4.570 and 2.090, respectively, indicating a higher accuracy of the RSEB model for inverting the ET_c values. Furthermore, the accuracy of the RSEB model could be improved by introducing the optimal correction coefficient (after correction: RMSE = 1.470, SE = 0.003). Based on the modified RSEB model, the ET_c values of the citrus in different slope positions were obtained. We also found that the middle slope ET_c > the top slope ET_c > the bottom slope ET_c, indicating that the slope position indeed affected the citrus ET_c. This research provides a favorable framework for the ET_c inversion, and the results are of theoretical and practical importance to realize crop water conservation.

Severe water scarcity has become a critical constraint to global agricultural development. Enhancing both the timeliness and accuracy of crop evapotranspiration (ET_c) retrieval is essential for optimizing irrigation scheduling. Addressing the limitations of conventional ground-based point-source measurements in rapidly acquiring two-dimensional ET_c information at the field scale, this study employed unmanned aerial vehicle (UAV) remote sensing equipped with multispectral and thermal infrared sensors to obtain high spatiotemporal resolution imagery of a representative citrus orchard (Citrus reticulata Blanco cv. ‘Yichangmiju’) in western Hubei at different phenological stages. In conjunction with meteorological data (air temperature, daily net radiation, etc.), ET_c was retrieved using two established approaches: the Seguin-Itier (S-I) model, which relates canopy–air temperature differences to ET_c, and the multispectral-driven single crop coefficient method, which estimates ET_c by combining vegetation indices with reference evapotranspiration. The thermal-infrared-driven S-I model, which relates canopy–air temperature differences to ET_c, and the multispectral-driven single crop coefficient method, which estimates ET_c by combining vegetation indices with reference evapotranspiration. The findings indicate that: (1) both the S-I model and the single crop coefficient method achieved satisfactory ET_c estimation accuracy, with the latter performing slightly better (accuracy of 80% and 85%, respectively); (2) the proposed multi-source fusion model consistently demonstrated high accuracy and stability across all phenological stages (R² = 0.9104, 0.9851, and 0.9313 for the fruit-setting, fruit-enlargement, and coloration–sugar-accumulation stages, respectively; all significant at p < 0.01), significantly enhancing the precision and timeliness of ET_c retrieval; and (3) the model was successfully applied to ET_c retrieval during the main growth stages in the Cangwubang citrus-producing area of Yichang, providing practical support for irrigation scheduling and water resource management at the regional scale. This multi-source fusion approach offers effective technical support for precision irrigation control in agriculture and holds broad application prospects.