Irrigation water management and real-time monitoring of crop water stress status can enhance agricultural water use efficiency, crop yield, and crop quality. The aim of this study was to simplify the calculation of the crop water stress index (CWSI) and improve its diagnostic accuracy. Simplified CWSI (CWSIsi) was used to diagnose water stress for cotton that has received four different irrigation treatments (no stress, mild stress, moderate stress, and severe stress) at the flowering and boll stage. High resolution thermal infrared and multispectral images were taken using an Unmanned Aerial Vehicle remote sensing platform at midday (local time 13:00), and stomatal conductance (gs), transpiration rate (tr), and cotton root zone soil volumetric water content (θ) were concurrently measured. The soil background pixels of thermal images were eliminated using the Canny edge detection to obtain a unimodal histogram of pure canopy temperatures. Then the wet reference temperature (T_wet), dry reference temperature (T_dry), and mean canopy temperature (T_l) were obtained from the canopy temperature histogram to calculate CWSIsi. The other two methods of CWSI evaluation were empirical CWSI (CWSIe), in which the temperature parameters were determined by measuring natural reference cotton leaves, and statistical CWSI (CWSIs), in which T_wet was the mean of the lowest 5% of canopy temperatures and T_dry was the air temperature (T_air) + 5 °C. Compared with CWSIe, CWSIs and spectral indices (NDVI, TCARI, OSAVI, TCARI/OSAVI), CWSIsi has higher correlation with gs (R² = 0.660) and tr (R² = 0.592). The correlation coefficient (R) for θ (0–45 cm) and CWSIsi is also high (0.812). The plotted high-resolution map of CWSIsi shows the different distribution of cotton water stress in different irrigation treatments. These findings demonstrate that CWSIsi, which only requires parameters from a canopy temperature histogram, may potentially be applied to precision irrigation management.

Accurately acquiring soil moisture content (SMC) and its spatial distribution is of great significance for water-saving irrigation and sustainable agricultural development in arid regions. However, the complex soil background noise and weak moisture absorption features in the Xinjiang region severely restrict the accuracy and reliability of remote sensing inversion for SMC. To address the challenges of difficult feature capture and low estimation accuracy in soil moisture monitoring, this study utilized GF-5 satellite hyperspectral data and ground-measured SMC data. First, the effects of Fractional-Order Differentiation (FOD) at orders 0–2 (with a 0.2 step size) on spectral moisture response were systematically evaluated. Next, 60 dual-band spectral indices (DBIs) were constructed from full-band combinations under the optimal differentiation orders, and highly correlated indices were selected as candidate features. Finally, three variable screening methods were coupled with three machine learning models to construct nine SMC inversion schemes, and the optimal model combination was employed to map the spatial distribution of SMC in the study area. Results showed that FOD at orders 0.8–1.2 effectively enhanced spectral responses at soil moisture absorption bands, the introduction of DBI concentrated high-correlation band combinations in moisture-sensitive regions, and the optimal scheme (BSS-PLSR) demonstrated good predictive performance and stability. These findings provide data support for precision irrigation decision-making and soil moisture management in arid farmlands.

As climate warming intensifies extreme weather, efficient agricultural water resource management is crucial. The unit crop water footprint (CWF), defined as the total volume of freshwater consumed and contaminated per unit harvested crop yield, serves as a key metric for evaluating agricultural freshwater appropriation. However, its accurate quantification at regional scales remains challenging due to scale effects, spatial heterogeneity, and limitations inherent in existing approaches. This study quantifies the regional winter wheat CWF in Shijiazhuang, a representative winter wheat–dominated agricultural region of the Huang–Huai–Hai Plain in North China, using a parameter optimization–based data assimilation framework that integrates multi-source optical and radar remote sensing with a process-based crop growth model. Specifically, the CERES-Wheat model is constrained through multi-variable parameter optimization using time series of Leaf Area Index (LAI) and Surface Soil Moisture (SSM), with parameters optimized via the Shuffled Complex Evolution–University of Arizona (SCE-UA) algorithm, to improve grid-scale estimates of winter wheat CWF. This approach overcomes the limitations of single-method frameworks, enhances spatial resolution, and captures regional heterogeneity in water footprint patterns. Results indicated that the winter wheat CWF ranged from 0.52 to 1.21 m³/kg, with higher values observed in the western and central parts of the study area and lower values in the northeastern and southeastern regions. Blue and green water footprints exhibited contrasting spatial distributions, with blue water predominating in the southern areas and green water dominating in the northern areas. This study reveals the spatial patterns and heterogeneity of the regional winter wheat water footprint and provides a robust methodological basis for advancing the understanding of agricultural water use during crop growth.