Field experiments were conducted to analyze the effectiveness of the crop stress index (CWSI) obtained by infrared thermal imaging to indicate crop water status, and to determine the appropriate CWSI threshold range for wheat at different growth stages. The results showed that the sensitivity of plant physiological parameters to soil water was different at different growth stages. The sensitivity of stomatal conductance (Gs) and transpiration rate (Tr) to soil water was higher than that of leaf relative water content (LRWC) and photosynthetic rate (Pn). The characteristics of plant physiology and biomass (yield) at each growth stage showed that the plant production would not suffer from drought stress as long as the soil water content (SWC) was maintained above 57.0% of the field water capacity (FWC) during the jointing stage, 63.0% of the FWC during the flowering stage and 60.0% of the FWC during the filling stage. Correlation analysis showed that the correlation of CWSI with Gs, Tr and Pn was lower than that with LRWC and SWC at the jointing stage. CWSI was extremely significantly negatively correlated with SWC and LRWC (p < 0.01), but significantly negatively correlated with Gs, Tr and Pn (p < 0.05). At the flowering stage, CWSI was extremely significantly negatively correlated with all physiological and soil parameters (p < 0.01). The regression analysis showed that the CWSI of winter wheat was correlated with biomass (grain yield) in a curvilinear relationship at each growth stage. When the CWSI increased to a certain extent, the biomass and yield showed a decreasing trend with the increase in CWSI. Comprehensive analysis of all indexes showed that CWSI can be used as a decision-making index to guide the water-saving irrigation of winter wheat, as long as the CWSI threshold of plants was maintained at 0.26–0.38 during the jointing stage, 0.27–0.32 during the flowering stage and 0.30–0.36 during the filling stage, which could not only avoid the adverse effects of water stress on crop production, but also achieve the purpose of water saving.

The accurate monitoring of crop water status is critical for optimizing irrigation strategies in winter wheat. Compared with satellite remote sensing, unmanned aerial vehicle (UAV) technology offers superior spatial resolution, temporal flexibility, and controllable data acquisition, making it an ideal choice for the small-scale monitoring of crop water status. During 2023–2025, field experiments were conducted to predict crop water status using UAV images in the North China Plain (NCP). Thirteen vegetation indices were calculated and their correlations with observed crop water content (CWC) and equivalent water thickness (EWT) were analyzed. Four machine learning (ML) models, namely, random forest (RF), decision tree (DT), LightGBM, and CatBoost, were evaluated for their inversion accuracy with regard to CWC and EWT in the 2024–2025 growing season of winter wheat. The results show that the ratio vegetation index (RVI, NIR/R) exhibited the strongest correlation with CWC (R = 0.97) during critical growth stages. Among the ML models, CatBoost demonstrated superior performance, achieving R² values of 0.992 (CWC) and 0.962 (EWT) in training datasets, with corresponding RMSE values of 0.012% and 0.1907 g cm⁻², respectively. The model maintained robust performance in testing (R² = 0.893 for CWC, and R² = 0.961 for EWT), outperforming conventional approaches like RF and DT. High-resolution (5 cm) inversion maps successfully identified spatial variability in crop water status across experimental plots. The CatBoost-RVI framework proved particularly effective during the booting and flowering stages, providing reliable references for precision irrigation management in the NCP.

A long-term positioning experiment was conducted from 2014 to 2021 to determine the appropriate tillage method for rapidly improving soil quality in reclaimed land. Four tillage methods were arranged before winter wheat sowing: deep tillage (DT), shallow tillage (ST), DT-ST alternate rotation (DST) and no tillage (NT). The results showed that: (1) with increasing reclamation years, ST, DT and DST had lower soil bulk density (SBD) and higher soil total porosity (STP) and soil capillary porosity (SCP) compared to NT. In the early stage of reclamation, ST had the lowest SBD and the highest STP and soil non-capillary porosity (NCP) in 0–20 cm soil layer, DT had the highest SCP and lowest NCP. In the 20–40 cm soil layer, DT has the lowest SBD and highest STP and SCP, followed by DST. In the late stage, SBD of each soil layer was NT > ST > DT > DST, while STP and SCP were NT < ST < DT < DST. (2) Different tillage methods influenced soil organic carbon (SOC) accumulation by affecting carbon sequestration rate (CSR). As opposed to NT, DT rapidly increased SOC of 0–40 cm soil layer in the early stages of reclamation, whereas DST facilitates maintaining higher SOC in the later stages. As compared to DT and DST, ST contributed more to SOC accumulation in surface soil, but less to SOC accumulation in deep soil. (3) Different tillage methods had various influences on SOC stratification ratio (SR). During the initial reclamation stage, NT had the lowest SR. Nevertheless, NT and ST maintained their high SR in the subsequent stage, whereas the SR of DT and DST experienced a notable decline due to the increase in SOC in deep soil. (4) It was observed that ST, DT and DST had higher grain yields compared with NT. The correlation analysis showed that DT improved soil properties by promoting SOC accumulation, increasing SCP and reducing NCP, thus increasing grain yield in the early stage of reclamation, while in the later stage of reclamation, DST can maintain better soil quality by reducing SBD and maintaining higher STP, SCP and SOC, and balanced the reasonable distribution of soil nutrients between the upper and lower soil layers by reducing SR of SOC, which helps the crop to maintain higher grain yields over time.