Each of the NDVI, EVI, NIRv, and kNDVI has varying strengths and weaknesses in terms of representing vegetation dynamics. Identifying the comparative advantages of these indices is crucial to objectively determine the dynamics of vegetation in dryland. In this study, Central Asia was selected as the research area, which is a typical drought-sensitive and ecologically fragile region. The Mann–Kendall trend test, coefficient of variation, and partial correlation analyses were used to compare the ability of these indices to express the spatiotemporal dynamics of vegetation, its heterogeneity, and its relationships with temperature and precipitation. Moreover, the composite vegetation index (CVI) was constructed by using the entropy weighting method and its relative advantage was identified. The results showed that the kNDVI exhibited a stronger capacity to express the relationship between the vegetation and the temperature and precipitation, compared with the other three indices. The NIRv best represented the spatiotemporal heterogeneity of vegetation in areas with a high vegetation coverage, while the kNDVI had the strongest expressive capability in areas with a low vegetation coverage. The critical value for distinguishing between areas with a high and low vegetation coverage was NDVI = 0.54 for temporal heterogeneity and NDVI = 0.50 for spatial heterogeneity. The CVI had no apparent comparative advantage over the other four indices in expressing the trends of changes in vegetation coverage and their correlations with the temperature and precipitation. However, it enjoyed a prominent advantage over these indices in terms of expressing the spatiotemporal heterogeneity of vegetation coverage in Central Asia.

Real-time monitoring of cotton moisture status and determination of appropriate irrigation thresholds are essential for achieving precision irrigation. Currently employed diagnostic methods based on physiological indicators, remote sensing, or soil moisture measurements typically present limitations including cumbersome procedures, high labor intensity, requirements for specialized technical expertise, and delayed results. To address these challenges, this study investigated the relationship between plant water content and leaf RGB color values (red, green, and blue color values measured using LScolor technology) during the bud, flowering, and boll development stages, with the objective of establishing a predictive model for rapid, real-time moisture status monitoring. Given that leaf position and color values (R, G, and B) of different functional leaves may influence the relationship between leaf color and plant water content, and this relationship varies across different temporal periods, a two-year experiment was conducted. In 2023, leaf color data from the top five functional leaves were measured at five time points daily throughout the irrigation cycle. In 2024, the following four irrigation treatments were established: one conventional irrigation control treatment (CK) and three irrigation treatments at 72% (T1), 70% (T2), and 68% (T3) plant water content thresholds. Results demonstrated that the following: (1) plant water content initially declined during the day and subsequently showed slight recovery, indicating cotton’s particular susceptibility to water stress between 2:30 p.m. and 7:00 p.m.; (2) plant water content continuously decreased across five measurement periods following irrigation during the bud, flowering, and boll development stages, with R and G color values of the five functional leaves showing declining trends between 2:30 p.m. and 7:00 p.m., while B color values exhibited no consistent pattern; (3) correlation analysis revealed significant positive correlations between plant water content and R and G color values of the five functional leaves during the 2:30 p.m. to 5:00 p.m. period, with highly significant correlations observed for the third and fourth leaves from the apex; (4) univariate and bivariate linear regression models were successfully established between cotton water content and R and G color values of the third and fourth leaves from the top; and (5) under 72% plant water content conditions, cotton achieved the highest yield and Irrigation Water Use Efficiency, indicating that 72% represents the optimal irrigation threshold. In conclusion, integrating leaf color–plant water content relationships with the 72% irrigation threshold enables rapid, non-destructive, large-scale diagnosis of cotton moisture status, providing a robust foundation for implementing effective precision irrigation strategies.

Ecological network construction has been widely accepted and applied to guide regional ecological conservation and restoration. For arid regions, ecological networks proposed based on ecological risk assessments are better aligned with the sensitive and fragile characteristics of local ecosystems. This study assesses landscape ecological risk in Wensu County, located on the southern slope of the Tianshan Mountains in the arid region of northwestern China, and it further proposes an optimized ecological network. A multidimensional framework composed of the natural environment, human society, and landscape patterns was employed to construct an ecological risk assessment system. Spatial principal component analysis (SPCA) was applied to identify the spatial pattern of ecological risk. Morphological spatial pattern analysis (MSPA) and a minimum cumulative resistance (MCR) model integrated with circuit theory were used to extract the ecological sources and delineate the ecological corridors. The results reveal significant spatial heterogeneity in terms of ecological risk: Low-risk zones (16.26%) are concentrated in the southwestern forest and water areas. In comparison, high-risk zones (28.27%) are mainly distributed in the northern mountainous mining region. A total of 24 ecological source patches (4105.24 km²), 44 ecological corridors (313.6 km), 39 ecological pinch points, and 38 ecological barriers were identified. Following optimization, the Integral Index of Connectivity (IIC) increased by 89.04%, and the Landscape Coherence Probability (LCP) rose by 105.23%, indicating markedly enhanced ecological connectivity. The current ecological network exhibits weak connectivity in the south and fragmentation in the central region. Targeted restoration of critical nodes, optimization of corridor configurations, and expansion of ecological sources are recommended to improve landscape connectivity and promote biodiversity conservation.

To explore the effects of micro-nano aeration and oxygenation irrigation on soil characteristics and cotton growth in cotton fields in arid areas, this study was conducted at the National Soil Quality Aksu Observation and Experiment Station in Baicheng County, Xinjiang. “Xinluzao 78” cotton was used as the experimental material, and the soil column cultivation method was adopted. Four nitrogen concentration gradients (N0: 0 kg·hm⁻², NL: 112.5 kg·hm⁻², NM: 225 kg·hm⁻², and NH: 337.5 kg·hm⁻²) and two irrigation methods (micro-nano aeration and oxygenation irrigation Y: DO15 mg/L, conventional irrigation C: DO7.6 mg/L) were set up to systematically analyze the total nitrogen content of the soil, enzyme activity, microbial community structure, and the response characteristics of cotton growth and yield. The results show that aeration treatment significantly increases the total nitrogen content in the soil. The total nitrogen content in the 0–15 cm and 15–30 cm soil layers treated with YNM (aeration + local conventional nitrogen application rate) increased by 9.14% and 8.53%, respectively, compared with CNM. YNM treatment significantly increased the activities of soil urease, sucrase, and β-glucosidase, among which total nitrogen had the strongest correlation with the activity of β-glucosidase. Oxygenation significantly increased the richness of soil microorganisms. The Chao1 index of YNM-treated bacteria was 75.7% higher than that of CNM-treated bacteria. YNM treatment increased cotton yield by 26.73% compared with CNM treatment. Moreover, the number of bells formed per plant and the weight of the bells increased by 44.44% and 29.6%, respectively. In conclusion, micro-nano aeration and oxygenation irrigation effectively increase cotton yield. By optimizing the activities of soil enzymes and microorganisms, micro-nano aeration and oxygenation irrigation enhance the ability of cotton to utilize and transform nitrogen, and alleviate the impact of insufficient nitrogen utilization by cotton in arid areas.