Agricultural irrigation accounts for nearly 70% of global freshwater withdrawals, making sustainable water management crucial for food security and ecological stability—particularly in arid and semi-arid regions. However, dynamic water-saving thresholds at both inter-annual and intra-annual scales remain insufficiently quantified in current research. To address this gap, this study developed an integrated SWAT-MODFLOW model for the Hetao Irrigation District and quantified dynamic water-saving thresholds by simulating crop yield responses under a range of irrigation scenarios. The model was calibrated (2008–2014) and validated (2014–2016), demonstrating reliable performance (R² = 0.75, NSE = 0.74) in capturing local hydrological processes. Inter-annual scenarios assessed water-saving levels of 5%, 10%, 20%, and 30% under wet, normal, and dry years, while intra-annual scenarios adjusted seasonal irrigation volumes in spring, summer, and autumn with reduction gradients of 33%, 50%, and 100%. Results show that wet and normal years could achieve a water-saving threshold of up to 20%, whereas dry years were limited to 5%. Intra-annually, autumn irrigation offered the greatest saving potential (33–100%), followed by spring (33–50%). Spatially, crop responses varied substantially: the western part of the region proved particularly sensitive, with even the optimal district-wide strategy reducing local crop yields by 10–20%. This study quantifies dynamic water-saving thresholds and incorporates spatial heterogeneity into scenario assessment. The resulting framework is transferable and provides a basis for sustainable water management in water-limited agricultural regions.

Soil salinization poses a major threat to agricultural sustainability in arid regions worldwide, where it is intrinsically linked to irrigated agriculture. In these water-scarce environments, the equilibrium of the water and salt balance is easily disrupted, causing salts to accumulate in the root zone and directly constraining crop growth, thereby creating an urgent need for precise water and salt management strategies. While precise water and salt transport models are essential for prediction and control, their accuracy is often compromised by parameter uncertainty. To address this, we developed a lumped water–salt balance model for the Hetao Irrigation District (HID) in China, integrating farmland and non-farmland areas and vertically structured into root zone, transition layer, and aquifer. A novel calibration approach, combining random sampling with Kernel Density Estimation (KDE), was introduced to identify optimal parameter ranges rather than single values, thereby enhancing model robustness. The model was calibrated and validated using data from the Yichang sub-district. Results showed that the water balance module performed satisfactorily in simulating groundwater depth (R² = 0.79 for calibration, 0.65 for validation). The salt balance module effectively replicated the general trends of soil salinity dynamics, albeit with lower R² values, which reflects the challenges of high spatial variability and data scarcity. This method innovatively addresses the common challenge of parameter uncertainty in the model, narrows the parameter value ranges, enhances model reliability, and incorporates sensitivity analysis (SA) to identify key parameters in the water–salt model. This study not only provides a practical tool for managing water and salt dynamics in HID but also offers a methodological reference for addressing parameter uncertainty in hydrological modeling of other data-scarce regions.

Persistent cloud cover during the growing season and mosaic cropping patterns introduce temporal gaps and mixed pixels, undermining the reliability of large-scale crop identification and acreage statistics. To address these issues, we develop a high spatiotemporal-resolution remote-sensing approach tailored to heterogeneous farmlands. First, an improved Spatiotemporal Adaptive Reflectance Fusion Model (STARFM) is used to fuse Landsat, Sentinel-2, and MODIS observations, reconstructing a continuous Normalized Difference Vegetation Index (NDVI) time series at 30 m spatial and 8-day temporal resolution. Second, at the field scale, we derive phenological descriptors from the reconstructed series—key phenophase timing, amplitude, temporal trend, and growth rate—and use a Random Forest (RF) classifier for detailed crop discrimination. We further integrate SHapley Additive exPlanations (SHAP) to quantify each feature’s class-discriminative contribution and signed effect, thereby guiding feature-set optimization and threshold refinement. Finally, we generate a 2024 crop distribution map and conduct comparative evaluations. Relative to baselines without fusion or without phenological variables, the fused series mitigates single-sensor limitations under frequent cloud/rain and irregular acquisitions, enhances NDVI continuity and robustness, and reveals inter-crop temporal phase shifts that, when jointly exploited, reduce early-season confusion and improve identification accuracy. Independent validation yields an overall accuracy (OA) of 90.78% and a Cohen’s kappa(κ) coefficient of 0.882. Coupling dense NDVI reconstruction with phenology-aware constraints and SHAP-based interpretability demonstrably improves the accuracy and reliability of cropping-structure extraction in complex agricultural regions and provides a reusable pathway for regional-scale precision agricultural monitoring.