Freshwater scarcity severely limits sustainable cotton production in arid regions. This study aimed to establish the optimal salinity threshold for staged saline water supplemental irrigation (SWSI) and elucidate its canopy-level mechanisms in optimizing within-boll yield components and fiber quality. A two-year field trial (2023–2024) was conducted in Awati County, Xinjiang, using mulched drip irrigation at five SWSI levels (3.5–9.5 g L⁻¹) and a freshwater control (CK). Compared with CK, 3.5 g L⁻¹ treatment significantly increased lint yield by 31.4%, boll number per plant by 22.45%, and fibers per seed by 6.01–10.59%, while fiber length and strength rose by 6.98–10.38% and 2.69–6.00%, respectively. When salinity reached 8.0 g L⁻¹, yield declined by 8.5%, and a salinity of 9.5 g L⁻¹ reduced yield by 24.52%. Spatially, mid-fruiting branches (nodes 4–6) remained stable, maintaining high lint mass per seed even under high salinity, whereas upper branches (≥node 7) were most sensitive; at 9.5 g L⁻¹, the boll number (0.36) was 56.6% lower than at 3.5 g L⁻¹ (0.83), and the Q-score decreased by 6.7%. These results demonstrate that SWSI with ≤5.0 g L⁻¹ salinity (optimum 3.5 g L⁻¹) simultaneously enhances lint yield and fiber quality, providing a practical strategy for efficient saline water use in arid cotton regions.

Cotton above-ground biomass (AGB) is a key indicator of crop growth and yield potential. Traditional monitoring methods are labor-intensive and destructive, limiting their suitability for precision agriculture. This study developed a high-precision, non-destructive model for estimating cotton AGB by integrating spectral and texture features derived from UAV multispectral and RGB images. UAV data were collected at major growth stages in 2024. Eight vegetation indices (VIs) and eight texture features (TFs) were extracted. Four machine learning algorithms—support vector regression (SVR), random forest regression (RFR), partial least squares regression (PLSR), and extreme gradient boosting (XGB)—were evaluated using independent validation data. Models based on fused spectral and texture features outperformed single-feature models. RFR achieved the best performance (R² = 0.811; RMSE = 2.931 t ha⁻¹). Texture features alone also showed strong predictive capability (R² = 0.789), highlighting their value in capturing canopy structural information. These results demonstrate that spectral–texture fusion significantly improves cotton AGB estimation and that RFR provides a robust modeling framework for UAV-based crop monitoring.

Long-term continuous cropping in cotton fields of Southern Xinjiang has limited crop productivity. To investigate how subsoiling depth regulates ecosystem-level water partitioning and thereby enhances water productivity mechanisms, a two-year field experiment was conducted in a mulched drip irrigation cotton field in Southern Xinjiang. Using a non-subsoiled field in the current season (CT) as the control, three subsoiling depths were established: subsoiling at 30 cm (ST1), 40 cm (ST2), and 50 cm (ST3). Changes in evapotranspiration partitioning and water use efficiency were analyzed. The results showed that subsoiling enhanced the utilization of deep soil water. Compared with CT, the ST2 and ST3 treatments significantly reduced soil water storage in the 0–60 cm layer during the flower opening to boll-setting stages, while soil water consumption increased by 26.4 mm and 28.8 mm, respectively. We demonstrate that subsoiling depth exerts a predominant control on the partitioning of evapotranspiration. Increasing subsoiling depth systematically shifted water loss from non-productive soil evaporation to productive plant transpiration, with the ST2 and ST3 treatments decreasing seasonal soil evaporation by 24.1% and 25.1%, respectively, and increasing plant transpiration by 21.9% and 22.8%, and lowering the Es/ET (where Es is soil evaporation and ET is evapotranspiration) ratio by 22.1% and 27.1%. However, this maximal physiological water-saving did not yield the optimal agronomic return. We established a non-linear relationship in which the ST2 treatment uniquely achieved the maximum seed cotton yield, WUE (water use efficiency), and IWUE (irrigation water use efficiency) (increasing by up to 34.4%, 17.2%, and 23.4%, respectively). This optimal depth better balances water resource allocation and reproductive growth. We conclude that under sandy loam soil conditions in typical mulched drip-irrigated cotton areas of Southern Xinjiang, implementing an optimal subsoiling depth (40 cm) can engineer a more resilient soil–plant–water continuum, providing a feasible pathway toward enhancing water and crop production sustainability.