In response to water scarcity and low agricultural water-use efficiency in arid regions in Northwest China, this study conducted field experiments in Wuwei, Gansu Province, from 2023 to 2024. It aimed to develop a water stress diagnosis model for spring maize to provide a scientific basis for precision irrigation and water management. In this work, two irrigation methods—plastic film-mulched drip irrigation (FD, where drip lines are laid on the soil surface and covered with film) and plastic film-mulched shallow-buried drip irrigation (MD, where drip lines are buried 3–7 cm below the surface under film)—were tested under five irrigation gradients. Multispectral UAV remote sensing data were collected from key growth stages (i.e., the jointing stage, the tasseling stage, and the grain filling stage). Then, vegetation indices were extracted, and the leaf water content (LWC) was retrieved. LWC inversion models were established using Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Regression (SVR). Different irrigation treatments significantly affected LWC in spring maize, with higher LWC under sufficient water supply. In the correlation analysis, plant height (hc) showed the strongest correlation with LWC under both MD and FD treatments, with R² values of −0.87 and −0.82, respectively. Among the models tested, the RF model under the MD treatment achieved the highest prediction accuracy (training set: R² = 0.98, RMSE = 0.01; test set: R² = 0.88, RMSE = 0.02), which can be attributed to its ability to capture complex nonlinear relationships and reduce multicollinearity. This study can provide theoretical support and practical pathways for precision irrigation and integrated water–fertilizer regulation in smart agriculture, boasting significant potential for broader application of such models.

Leaf water content (LWC) is a key physiological indicator for assessing crop water status. However, its spectral response may vary under different irrigation practices, which limits the general applicability of existing models. This study aims to develop irrigation-specific LWC estimation models for spring wheat based on UAV multispectral imagery. Field experiments were conducted during two growing seasons (2023–2024) under three irrigation methods, with five water treatments and three replicates, resulting in a total of 45 experimental plots. Multispectral data and in situ measurements were collected at key growth stages. Irrigation-dependent sensitive vegetation indices were identified through correlation analysis, and machine learning models, including Random Forest (RF), Multiple Linear Regression (MLR), and Backpropagation Neural Network (BPNN), were constructed and evaluated using a five-fold cross-validation framework. The results showed that spectral sensitivity to LWC varied significantly across irrigation methods, with different dominant indicators under FD, ND, and MD. Model performance also exhibited irrigation-dependent differences. Among the three models, RF showed the most stable performance, achieving mean R² values of 0.70, 0.74, and 0.62 and corresponding RMSE values of 0.04, 0.06, and 0.08 under FD, ND, and MD, respectively. In contrast, MLR showed lower predictive accuracy, while BPNN exhibited limited robustness under the current dataset, particularly under ND. These findings highlight the importance of irrigation-specific modeling strategies for improving LWC estimation reliability.

To clarify the optimal water regulation strategy for spring wheat in arid areas, this study set up three irrigation methods [film-mulched drip irrigation (FD), non-mulched drip irrigation (ND), non-mulched subsurface drip irrigation (MD)] and five water treatments [CK: 80% field capacity; W1–W4: irrigation amounts were 90%, 80%, 70%, and 60% of CK, respectively] in the Shiyang River Basin during 2023–2024. The effects of these treatments on the phenotype, yield, and water use efficiency (WUE) of spring wheat were investigated. The results showed that under the same water treatment, the leaf area index (LAI), SPAD value, and stem diameter (SD) significantly decreased with the reduction in irrigation amount (p < 0.05), while plant height (HC) was less affected. FD performed optimally under the W1 treatment: its yield reached 11,868.93 kg·ha⁻¹, which was 54.88% and 38.72% higher than that of ND and MD, respectively; and its WUE reached 4.36 kg/m³, which was 123.19% and 100.83% higher than that of ND and MD, respectively. ND performed better under the CK treatment: its yield was 10,044.33 kg·ha⁻¹, which was 27.07% and 12.25% higher than that of FD and MD, respectively. Annual precipitation had a significant impact: when precipitation was 175 mm in 2023, ND showed an obvious advantage; when precipitation decreased to 110 mm in 2024, FD exhibited stronger stress resistance. The study concludes that FD is suitable for moderate to severe water stress, while ND is suitable for sufficient water conditions or mild stress. This can provide a basis for water-saving and the high-yield production of spring wheat in arid areas.