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.

Interlayer soil is common in southern Xinjiang, because interlayer can reduce the infiltration rate of soil water. To simulate the interlayer soil in heavy saline–alkali cotton fields, this paper adopted a vertical one-dimensional infiltration test. T1 (315 mm), T2 (270 mm), and T3 (225 mm) and different interlayer positions (T5, 315 mm) and thicknesses of the interlayer (T6, 315 mm) with the same irrigation volume, as well as one perforation and sand filling treatment (T4, 315 mm), were set. The influence of different irrigation amounts, locations, and thicknesses of the interlayer and sand injection on water infiltration was analyzed. The analysis results showed that with the increase in irrigation amount, the water infiltration rate and the migration distance of the wet front increased, but did not penetrate to the bottom soil (90 cm). Under the same irrigation volume, the increase in interlayer thickness (T6) compared with the increase in interlayer position (T5), the change in soil moisture content in the upper and lower layers of the interlayer was greater, and the advance time of wetting front migration and cumulative infiltration were slightly higher. After tunneling and sand filling (T4), the infiltration rate of water was increased, the migration time of the wet front was reduced, and the profile water content of each soil layer was improved. The Kostiakov model could better simulate the water infiltration characteristics of interlayer soil with different profile configurations in heavily saline–alkali land. The results showed that in all of the treatments, only the wet front of the soil moisture reached 100 cm in the T4 treatment, and the maximum was only 87.8 cm in the other treatments, indicating that too little irrigation water or the upward movement and thickening of the interlayer were not conducive to water infiltration. For the interlayer soil area in the heavy saline–alkali land in southern Xinjiang, the appropriate irrigation water should be more than 315 mm. The treatment of drilling first and then filling sand can be used as a simple but effective measure to increase the water infiltration rate of the interlayer soil, and can thus be applied to the layered soil structure in the interlayer position of 60–80 cm.

Joint estimation of groundwater contaminant source characteristics and hydraulic conductivity is of great significance for contaminant transport models in heterogeneous subsurface media. As for accurate characterization of hydraulic conductivities, both geostatistical modeling and groundwater inverse modeling are alternative approaches. In this study, an iterative ensemble smoother and sequential gaussian simulation (SGSIM) in geostatistics modeling were combined to realize the simultaneous inversion of contaminant sources and hydraulic conductivities, by using directly measured hydraulic conductivities and indirect hydraulic head and concentration data. To alleviate the high computational cost caused by repetitive evaluations of complex, high-dimensional groundwater models, SGSIM with the pilot points method was used. Considering the characteristics of the proposed method, four scenarios with ten cases were set up in terms of ensemble number and iteration number that affect the performance of the iterative ensemble smoother, the number of pilot points, and the observation data, respectively. The results for the synthetic example indicate that the ensemble size of 2000 and the pilot point number of 80 is an ideal combination of parameters, and the proposed method can successfully recover contaminant source information simultaneously with hydraulic conductivity.

Flood irrigation is often applied in the arid regions of Northwest China to facilitate the leaching of salts accumulated in the soil during cotton growth in the previous season. This will, in turn, affect the temporal and spatial patterns of soil salinity, and thus cotton germination. To reveal the salinity of the two soil layers (0–20 cm and 20–60 cm), so as to determine the optimal cotton sowing timing, an electronic ground conductivity meter (EM38-MK2) was employed to measure the soil apparent electrical-conductivity (EC_a) on different days: 4 days prior to flood irrigation, and, respectively, 6, 10, 15, 20, and 45 days after flood irrigation. Moreover, geostatistical analysis and block kriging interpolation were employed to analyze the spatial-temporal variations of soil salinity introduced by flood irrigation. Our results indicate that: (1) soil salinity in the two layers on different days can be well inverted from binary first-order equations of EC_a at two coils (i.e., EC_a1.0 and EC_a0.5), demonstrating the feasibility of applying EM38-MK2 to estimate soil salinity in the field; and (2) soil salinity in the 0–20 cm layer significantly decreased during the first 15 days after flood irrigation with the greatest leaching rate of 88.37%, but tended to increase afterwards. However, the salinity in the 20–60 cm layer was persistently high before and after flood irrigation, with merely a brief decrease during the first 10 days after flood irrigation at the highest leaching rate of 40.74%. (3) The optimal semi-variance models illustrate that, after flood irrigation, the sill value (C₀ + C) in the 0–20 cm layer decreased sharply, but the 20–60 cm Range of the layer significantly increased, suggesting that flood irrigation not only reduces the spatial variability of surface soil salinity, but also enhances spatial dependence in the 20–60 cm layer. (4) The correlation of the soil salinity between the two soil layers was very poor before flood irrigation, but gradually enhanced during the first 15 days after flood irrigation. Overall, for the study year, the first 15 days after flood irrigation was an optimal timing for cotton sowing when the leaching effects during flood irrigation were most efficient, and overrode the effects of evaporation and microtopography. Although not directly applicable to other years or regions, the electromagnetic induction surveys and spatiotemporal analysis of soil salinity can provide a rapid and viable guide to help determine optimal cotton sowing timing.