Soil salinization is one of the primary factors contributing to land degradation in arid areas, severely restricting the sustainable development of agriculture and the economy. Satellite remote sensing is essential for real-time, large-scale soil salinity content (SSC) evaluation. However, some satellite images have low temporal resolution and are affected by weather conditions, leading to the absence of satellite images synchronized with ground observations. Additionally, some high-temporal-resolution satellite images have overly coarse spatial resolution compared to ground features. Therefore, the limitations of these spatiotemporal features may affect the accuracy of SSC evaluation. This study focuses on the arable land in the Manas River Basin, located in the arid areas of northwest China, to explore the potential of integrated spatiotemporal data fusion and deep learning algorithms for evaluating SSC. We used the flexible spatiotemporal data fusion (FSDAF) model to merge Landsat and MODIS images, obtaining satellite fused images synchronized with ground sampling times. Using support vector regression (SVR), random forest (RF), and convolutional neural network (CNN) models, we evaluated the differences in SSC evaluation results between synchronized and unsynchronized satellite images with ground sampling times. The results showed that the FSDAF model’s fused image was highly similar to the original image in spectral reflectance, with a coefficient of determination (R²) exceeding 0.8 and a root mean square error (RMSE) below 0.029. This model effectively compensates for the missing fine-resolution satellite images synchronized with ground sampling times. The optimal salinity indices for evaluating the SSC of arable land in arid areas are S3, S5, SI, SI1, SI3, SI4, and Int1. These indices show a high correlation with SSC based on both synchronized and unsynchronized satellite images with ground sampling times. SSC evaluation models based on synchronized satellite images with ground sampling times were more accurate than those based on unsynchronized images. This indicates that synchronizing satellite images with ground sampling times significantly impacts SSC evaluation accuracy. Among the three models, the CNN model demonstrates the highest predictive accuracy in SSC evaluation based on synchronized and unsynchronized satellite images with ground sampling times, indicating its significant potential in image prediction. The optimal evaluation scheme is the CNN model based on satellite image synchronized with ground sampling times, with an R² of 0.767 and an RMSE of 1.677 g·kg⁻¹. Therefore, we proposed a framework for integrated spatiotemporal data fusion and CNN algorithms for evaluating soil salinity, which improves the accuracy of soil salinity evaluation. The results provide a valuable reference for the real-time, rapid, and accurate evaluation of soil salinity of arable land in arid areas.

Oases in arid regions are crucial for sustaining agricultural production and ecological stability, yet few studies have simultaneously examined the coupled dynamics of land use/cover change (LUCC), carbon emissions, and ecosystem service value (ESV) at the oasis–agricultural scale. This gap limits our understanding of how different land use trajectories shape trade-offs between carbon processes and ecosystem services in fragile arid ecosystems. This study examines the spatiotemporal interactions between land use carbon emissions and ESV from 1990 to 2020 in the Wensu Oasis, Northwest China, and predicts their future trajectories under four development scenarios. Multi-period remote sensing data, combined with the carbon emission coefficient method, modified equivalent factor method, spatial autocorrelation analysis, the coupling coordination degree model, and the PLUS model, were employed to quantify LUCC patterns, carbon emission intensity, ESV, and its coupling relationships. The results indicated that (1) cultivated land, construction land, and unused land expanded continuously (by 974.56, 66.77, and 1899.36 km²), while grassland, forests, and water bodies declined (by 1363.93, 77.92, and 1498.83 km²), with the most pronounced changes occurring between 2000 and 2010; (2) carbon emission intensity increased steadily—from 23.90 × 10⁴ t in 1990 to 169.17 × 10⁴ t in 2020—primarily driven by construction land expansion—whereas total ESV declined by 46.37%, with water and grassland losses contributing substantially; (3) carbon emission intensity and ESV exhibited a significant negative spatial correlation, and the coupling coordination degree remained low, following a “high in the north, low in the south” distribution; and (4) scenario simulations for 2030–2050 suggested that this negative correlation and low coordination will persist, with only the ecological protection scenario (EPS) showing potential to enhance both carbon sequestration and ESV. Based on spatial clustering patterns and scenario outcomes, we recommend spatially differentiated land use regulation and prioritizing EPS measures, including glacier and wetland conservation, adoption of water-saving irrigation technologies, development of agroforestry systems, and renewable energy utilization on unused land. By explicitly linking LUCC-driven carbon–ESV interactions with scenario-based prediction and evaluation, this study provides new insights into oasis sustainability, offers a scientific basis for balancing agricultural production with ecological protection in the oasis of the arid region, and informs China’s dual-carbon strategy, as well as the Sustainable Development Goals.

This paper addresses the dual challenges of food security and sustainable development by examining the balance between arable land quality and economic development. Coordinating and optimizing development models is essential for achieving sustainable agricultural and economic progress. The North Slope Economic Belt of Tianshan Mountain (UANST), a semi-arid agriculturalpastoral transition zone in northwest China, exemplifies a coupled human environment system where global sustainability targets confront regional development imperatives. Focusing on seven cities and counties within the UANST, this study employs information sensitivity indicators to quantitatively select evaluation metrics. It provides a comprehensive analysis of the current state of the coupling and coordination degree (CCD) between arable land quality and economic development in the region. Using a system dynamics model (SDM), four scenario models were developed to predict and analyze the interaction between cultivated land quality and economic development on the North Slope of Tianshan. The study proposes a model to improve coordination between cultivated land quality and economic development. The key findings are as follows: (1) “preliminary screening + information sensitivity analysis” method identified 12 arable land quality evaluation indicators and 11 economic development evaluation indicators for the North Slope Economic Belt of Tianshan. (2) The coupling coordination between arable land quality and economic development in the seven counties and cities improved from 0.469 to 0.663, reflecting a transition from “marginal imbalance” to “primary coordination”. By 2021, all regions had reached the initial stage of coordinated development. (3) Among the development models analyzed, the coordinated development model achieved the highest coupling coordination score (0.9136). This model also demonstrated lower carbon dioxide emissions and reduced water resource consumption, alleviating environmental pressures and offering an optimal solution for regional coordinated development.

Aphis gossypii Glover is an important pest in cotton plantations. Medicago sativa L. (alfalfa) is a host plant for the aphid Aphis craccivora Koch and may prove to be an important reservoir of natural enemies to combat this pest. The objective of this study was to analyze the impact of different mowing frequencies of alfalfa traps on A. gossypii and their natural enemies, using both ground survey data and UAV remote sensing data. The alfalfa was mowed twice to facilitate the transfer of this primary natural enemy to the cotton fields. Ground surveys were carried out every five days to gather data, while temporal niche and niche overlap methods were used for further analysis. Findings collected over a period ranging from day 31 to day 91 indicated that compared to their counterparts with no alfalfa traps, the cotton fields containing these pest control measures demonstrated a reduction in the A. gossypii population of approximately 16%. A survey conducted 5 days after mowing the alfalfa on days 61 and 71 found that the cotton fields with alfalfa traps experienced a 24.14% and 26.09% reduction in A. gossypii numbers. In contrast, the cotton fields without alfalfa traps experienced a 76.92% and 55.08% increase in cotton aphid numbers during the same period. It is noteworthy that the cotton fields with alfalfa traps showed a delayed onset of cotton aphid damage of approximately 5 days compared to the fields without alfalfa traps. This discovery has significant implications for understanding the ecological control mechanism of A. gossypii within alfalfa traps. Planting alfalfa traps around fields in Xinjiang could be promoted as a method to prevent and control aphid damage.

The low water and fertiliser utilisation efficiency and soil quality degradation caused by high water and fertiliser inputs are the primary challenges facing goji berry cultivation in arid regions. A two-year field experiment was conducted from 2021 to 2022. The experiment included three irrigation rates (I₁, I₂, I₃) of 2160, 2565, and 2970 m³·hm⁻² and three nitrogen application rates (N₁, N₂, N₃) of 165, 225, and 285 kg·hm⁻² to quantify their impacts on soil nutrients, enzyme activity, and goji berry yield in the root zone. Results indicate that the indicators of soil nutrients decrease with increasing soil depth, with depths of 0–20 cm accounting for 24.80–72.48% of total content. With fertility period progression, soil organic matter at depths of 0–80 cm exhibits a “folded-line” trend, while total nitrogen, nitrate nitrogen, and available phosphorus show an “M”-type trend. At depths of 0–40 cm, the proportions of urease, sucrase, and alkaline phosphatase activities all exceeded 70%. At I₁ irrigation rate, enzyme activities gradually increased with rising nitrogen application rates. At I₂ and I₃ irrigation rates, enzyme activities first increased, then decreased with increasing nitrogen application. The highest yields of both fresh and dried fruits were achieved at I₂N₂ treatment, increasing by 14.17% and 14.78%, respectively, compared to conventional management (CK). Analysis of the random forest model indicates that the soil-driven factors influencing yield formation include SA, UA, APA, HPA, SOM, NH₄⁺-N, and TP. Analysis of SQI and yield fitted data indicates that water–nitrogen coupling significantly influences wolfberry yield by regulating soil quality. Partial least squares (PLS-PM) showed that N application and irrigation of soil nutrients did not cause a significant indirect impact on goji berry yield, but a significant positive effect on goji berry yield occurred through enzyme activity.