Reducing irrigation and nitrogen fertilizer application while maintaining crop yields is crucial for sustainable agriculture in the North China Plain. To investigate the effects of irrigation and nitrogen application on above-ground nitrogen accumulation (ANA), yield, water consumption, and the water and nitrogen use efficiency of drip-irrigated winter wheat, a three-season field experiment was conducted with four levels of nitrogen fertilizer application (250, 167, 83, and 0 kg hm⁻², referred to as N3, N2, N1, and N0, respectively) and three levels of irrigation (80, 60, and 40 mm per irrigation event, referred to as W3, W1, and W1, respectively). Additionally, a control treatment (CK) was set up with almost no irrigation (only 10 mm of fertilizer water for topdressing was applied) at the N3 application level. The results indicated that over the three seasons, the average yield of irrigation treatments was 35.3% higher than that of the CK treatment under the N3 condition. Both irrigation and nitrogen application improved wheat yield and ANA; however, when irrigation exceeded W2 or nitrogen application exceeded N2, their positive effects were negligible. Due to the seasonal depletion of soil nitrogen by low-nitrogen treatments (N1 and N0), along with their other negative effects on soil health, the yields and ANA of N1 and N0 treatments gradually declined over seasons. Increased irrigation promoted evapotranspiration (ET), and when nitrogen application did not exceed N2, higher nitrogen levels also enhanced ET and soil water consumption within ET. Moderately reducing irrigation can enhance water use efficiency (WUE); however, extreme water scarcity can also decrease WUE. Compared to higher irrigation and nitrogen application treatments, the W2N2 treatment showed no significant decrease in either yield or WUE, along with an increase in NPE. Moreover, the NPFP of the N2 treatment was higher than that of the N3 treatment. Consequently, the W2N2 treatment is recommend as the optimal irrigation and nitrogen management strategy under the experimental conditions.

Increasing planting density generally increases total water use by maize (Zea mays L.), but there are contrasting conclusions as well. To determine whether increasing planting density would increase total water use by maize, a 3-year field experiment was conducted in the North China Plain. In 2018, maize was planted at the four densities of 57,100, 66,700, 80,000, and 100,000 plants ha⁻¹. In 2019 and 2020, another four planting densities of 27,800, 41,700, 66,700, and 111,100 plants ha⁻¹ were selected. The results showed that increasing planting density increased leaf area index but decreased leaf stomatal conductance; maize grain yield reached the maximum at about 80,000 plants ha⁻¹. At the VE-V6 and R3-R6 stage, soil water use occurred mainly in the 0–60 cm soil layer, and planting density showed no effect on total soil water use by maize. At the V6-R3 stage, when soil water in the 0–60 cm soil layer was sufficient to meet the evapotranspiration demand, soil water use occurred mainly in the 0–60 cm soil layer; increasing planting density did not increase total soil water use. When soil water in the 0–60 cm soil layer was insufficient and could not meet the demand of evapotranspiration, soil water use in the 60–100 cm soil layer increased greatly and kept rising with increased planting density, resulting in elevated total soil water use. Therefore, we conclude that the effect of planting density on water use by maize varies with soil water content in the 0–60 cm soil layer in the North China Plain.

Surface soil moisture (SSM) is a critical indicator of crop growth conditions, and its accurate retrieval is essential for agricultural monitoring. Integrating multispectral and microwave remote sensing data can enhance SSM estimation, but discrepancies among platforms often reduce accuracy at local scales. In this study, we fused Sentinel-2 and UAV multispectral images through resampling to generate fusion data, which were then combined with miniature synthetic aperture radar (MiniSAR) data. A modified water cloud model (WCM) was applied to mitigate vegetation effects on radar backscattering coefficients. Three machine learning algorithms—random forest (RF), extreme gradient boosting (XGBoost), and extreme learning machine (ELM)—were employed to retrieve SSM. Field measurements at two depths (0–10 cm and 0–20 cm) over winter wheat fields in Xunxian, Hebi City, Henan Province, China, were used for validation. Results showed the following: (1) Fused multispectral data improved retrieval accuracy compared with single-satellite data, with the best configuration (fused data + VV + RF) achieving an R² of 0.85 and an RMSE of 1.51% at 0–10 cm. (2) At 0–20 cm, the fused data combined with VV polarization and XGBoost achieved the best performance (R² = 0.67, RMSE = 2.61%). (3) ELM exhibited the largest accuracy improvement after incorporating fused data, with R² increases up to 0.40 and RMSE reductions up to 18.24%. These results demonstrate the strong potential of multi-platform multispectral fusion combined with MiniSAR data for improving field-scale SSM retrieval in winter wheat regions.

Water is a crucial and fundamental resource. It is well known that agricultural cultivation, industrial production, and human daily life are not possible without water. Efficiently utilizing water resources is of great significance for achieving global Sustainable Development Goals (SDGs). In order to improve water use efficiency in various industries and promote water-saving development, China has been implementing water-saving society construction since 2002. Henan Province is the main grain-producing area in China, with wheat production accounting for a quarter of the country’s total. As the core area of “Central Plains Agricultural Valley” in Henan Province, Xinxiang City plays an important role in agricultural technology innovation and agricultural production. However, Xinxiang City is facing problems of water scarcity and pollution, which constrain the sustainability of agricultural production. Therefore, building a water-saving society can solve the current water problems faced by Xinxiang City and ensure the sustainable development of the economy and society. This study built an evaluation index system for water-saving society construction in Xinxiang, Henan Province, China. The proposed evaluation index system includes 20 evaluation indices from six aspects—integrated, agricultural water, industrial water, domestic water, water ecology and environment, and water-saving management—and then divides its development level into several stages. The Analytical Hierarchy Process (AHP) was adopted to calculate the index weight. Then, a comprehensive evaluation model for water-saving society construction in Xinxiang City was established by combining it with grey relative analysis (GRA). The results showed that the overall level of water-saving society construction in Xinxiang City is in the excellent stage, whereas water consumption per CNY 10,000 of GDP, the effective utilization coefficient of irrigation water, the reuse rate of industrial water, and the leakage rate of urban water supply network are all in the good stage. However, the urban recycled water utilization rate is still in the poor stage. These research results can effectively and reasonably reflect the development level of water-saving society construction in Xinxiang City and guide the continued implementation of water-saving society construction. At the same time, the comprehensive evaluation of water-saving society construction helps to formulate and adjust water resource management policies and measures; it also holds significant value for sustainable water management and combating water scarcity.

Retrieving farmland soil water content with both high accuracy and physical interpretability remains a significant challenge, particularly for winter wheat. To bridge the gap between purely empirical data-driven approaches and mechanistic scattering models, this study proposed a novel hybrid framework that integrates an improved water cloud model (IWCM) with machine learning algorithms. Multi-modal unmanned aerial vehicle (UAV) experiments were conducted during the heading stage of winter wheat over two consecutive years (2024–2025) using a synchronized system equipped with a miniature synthetic aperture radar (MiniSAR) and a multi-spectral sensor. The core innovation of the proposed framework lies in the IWCM, which explicitly decouples vegetation and soil scattering contributions by incorporating fractional vegetation cover, thereby deriving physically meaningful soil backscatter coefficients from complex microwave signals. Unlike traditional methods that treat remote sensing variables as black box inputs, our approach employed these physics-derived features to guide data-driven modeling. Four feature input schemes including spectral reflectance, vegetation indices, MiniSAR polarimetric parameters, and their multi-source fusion were systematically evaluated using back propagation neural network (BPNN) and random forest (RF) regressors. The results demonstrated that the proposed framework significantly enhances retrieval performance. Notably, the RF model driven by spectral band reflectance within this physically constrained architecture achieved optimal accuracy, with a coefficient of determination (R²) of 0.865, a mean absolute error (MAE) of 0.0152, and a root mean square error (RMSE) of 0.0197. Compared to purely empirical approaches, the IWCM significantly improved the physical interpretability of microwave polarimetric characteristics, enabling the multi-source data fusion to better represent the interactions among vegetation, soil, and microwave scattering. This study demonstrated that integrating mechanistic models with multi-source UAV remote sensing data not only improves soil water content retrieval accuracy in winter wheat fields but also provides a valuable reference for developing operationally applicable and physically interpretable farmland soil water content monitoring systems.

Understanding how gate-opening variations affect the upstream water level is essential for quantitative water allocation and automation in irrigation canals. Using an indoor recirculating rectangular open-channel facility equipped with a standard flat sluice gate, we deployed five upstream water-level gauges (Points 1#D–5#H) and conducted step response tests and pseudo-random binary sequence (PRBS) tests under four representative operating conditions (Q ≈ 30–85 m³/h). For step tests, the upstream water-level dynamics were well approximated by a first-order plus dead-time (FOPDT) model. Under low flow (Condition A, Q ≈ 29.5 m³/h) with a 1.5 → 2.0 cm opening step, the identified parameters were K ≈ −15.4 mm/mm, L ≈ 4.5–5.7 s, and T ≈ 71 s, and the five points exhibited strong spatial consistency. Under higher flow (Condition B, Q ≈ 72.5 m³/h) with a 3.0 → 3.5 cm step, the gain magnitude decreased (K ≈ −10.6 mm/mm), the dead time increased moderately (L ≈ 8.0–10.3 s), and the time constant became smaller (T ≈ 41–43 s), indicating a faster response but weaker sensitivity to gate-opening changes. For PRBS tests, a discrete-time ARX (2,2,1) model was identified between gate opening and the upstream level deviation at Point 3#F. The identified ARX models achieved R² of 0.992 (Condition C) and 0.946 (Condition D), with MAE and RMSE within 0.65–1.85 mm, and residual diagnostics supported the adequacy of the selected model structure. Finally, steady-state gains derived from dynamic identification were consistent with static water-level–flow–opening relations obtained from quasi-steady experiments, providing a physical basis for the models. The proposed simplified models offer a unified and engineering-friendly plant description for designing and comparing controllers such as PID, fuzzy control, and reinforcement learning-based approaches.