Farmland shelterbelts are crucial elements in safeguarding agricultural ecological security and sustainable development, with their precise extraction being vital for regional ecological monitoring and precision agriculture management. However, constrained by their narrow linear distribution, complex farmland backgrounds, and spectral confusion issues, traditional remote sensing methods encounter significant challenges in terms of accuracy and generalization capability. In this study, six representative deep learning semantic segmentation models—U-Net, Attention U-Net (AttU_Net), ResU-Net, U²-Net, SwinUNet, and TransUNet—were systematically evaluated for farmland shelterbelt extraction using high-resolution Gaofen-6 imagery. Model performance was assessed through four-fold cross-validation and independent test set validation. The results indicate that convolutional neural network (CNN)-based models show overall better performance than Transformer-based architectures; on the independent test set, the best-performing CNN model (U-Net) achieved a Dice Similarity Coefficient (DSC) of 91.45%, while the lowest DSC (88.86%) was obtained by the Transformer-based TransUNet model. Among the evaluated models, U-Net demonstrated a favorable balance between accuracy, stability, and computational efficiency. The trained U-Net was applied to large-scale farmland shelterbelt mapping in the study area (Alar City, Xinjiang), achieving a belt-level visual accuracy of 95.58% based on 385 manually interpreted samples. Qualitative demonstrations in Aksu City and Shaya County illustrated model transferability. This study provides empirical guidance for model selection in high-resolution agricultural remote sensing and offers a feasible technical solution for large-scale and precise farmland shelterbelt extraction.

The APETALA2/ethylene response factor (AP2/ERF) is a class of plant-specific transcription factors, among which the dehydration-responsive element-binding protein (DREB) subfamily has been widely reported to enhance plant resistance to abiotic stresses. A high-temperature-related gene, Apium graveolens DREBA6b (AgDREBA6b; accession number: OR727346), was previously cloned from a heat-tolerant celery variety. In this study, we transformed this gene into Arabidopsis thaliana using an Agrobacterium rhizogenes-mediated method to explore its function. The results showed that overexpressing AgDREBA6b in Arabidopsis thaliana significantly improved plant growth under high-temperature stress (38 °C) compared to the dreb mutant and wild-type (WT) plants. The anatomical structure of the leaves revealed that the number and degree of stomatal openings in the overexpressed plants were significantly higher than those in the WT and dreb plants, suggesting that AgDREBA6b enhances stomatal opening. Additionally, the chlorophyll content, chlorophyll fluorescence properties, proline (Pro), malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD) activities were higher in the transgenic plants, indicating better stress tolerance. qPCR analysis showed that four heat tolerance-related genes (AtHSP98.7, AtHSP70-1, AtAPX1, and AtGOLS1) were upregulated in the transgenic plants, with higher expression levels than in WT and mutant plants. This study provides valuable genetic resources for understanding the molecular mechanisms of celery’s heat tolerance and offers insights for breeding heat-tolerant celery varieties.

Roadside green spaces function as critical ecological barriers in urban environments, and their plant communities play a key role in improving regional air quality. This study investigates typical roadside plant communities in southern Xinjiang, a region characterized by extreme aridity and frequent dust storms. By quantifying indicators such as dust retention capacity at both individual and community levels, together with leaf surface microstructural characteristics, we evaluate the comprehensive dust retention performance of different community configuration patterns. The results show that: (1) Among the studied species, Juniperus chinensis ‘Kaizuca’ exhibited the highest dust retention capacity per unit leaf area, followed by Juniperus chinensis L. and Rosa rugosa Thunb. Among trees, Platanus acerifolia (Aiton) Willd showed the greatest dust retention capacity per individual plant; among shrubs, Rosa rugosa Thunb. performed strongly, and among herbaceous species, Lolium perenne L. exhibited relatively high dust retention capacity. (2) Leaf dust retention is governed by the synergistic effects of multiple traits, including leaf aspect ratio, stomatal aspect ratio, stomatal protrusion, stomatal density, wax layer characteristics, and surface roughness. Leaf aspect ratio exerts a significant positive direct effect on dust retention, whereas stomatal aspect ratio shows a significant negative direct effect. (3) At the community level, the multi-layered tree–shrub–herbaceous configuration dominated by Platanus acerifolia (Aiton) Willd exhibited the strongest dust retention capacity, making it the most effective configuration for roadside green spaces. Overall, this study provides a robust theoretical framework and empirical evidence for the scientific selection and optimized configuration of roadside vegetation in arid regions, thereby supporting the sustainable improvement of urban roadside air quality in southern Xinjiang.

Arid and semi-arid shelterbelts must provide long-term ecological protection under chronic water scarcity, high evaporative demand, and rising salinization risk, yet management still lacks an integrated framework linking irrigation, root-zone salt dynamics, and woody plant performance. Here, we synthesize evidence on water–salt–root linkages in drip-irrigated shelterbelts and related dryland woody systems from a structured Web of Science Core Collection search (1 January 2000–1 January 2026). The evidence shows that shelterbelt performance is governed not by water or salinity alone, but by a coupled root-zone system: localized irrigation creates moisture–salt heterogeneity, salts accumulate near evaporative fronts and emitter margins, and roots redistribute depth, density, and uptake zones. In hyper-arid saline-drip systems, precipitation may be only ~24.6 to 3000–3639 mm yr⁻¹, groundwater salinity 2.8–29.7 g L⁻¹, active roots 20–80 cm, and salt mainly in the 0–20 cm surface layer. Irrigation thus acts as both the basis of establishment and a source of long-term vulnerability, particularly where saline groundwater or other non-conventional water sources are used. Management options can improve root-zone habitability, but shelterbelt-specific thresholds and integrated indicators remain limited. This review proposes a root-zone-centered framework supporting predictive regulation.

Populus euphratica (P. euphratica) is a dominant tree species in the arid and semi-arid regions along the main stem of the Tarim River. This study aims to explore the response of microbial communities in the rhizosphere soil of P. euphratica to varying groundwater depths (GWD) and to elucidate the ecological functions of key microbial groups in drought resistance. We established three groundwater depth levels (3.8 m, 5.4 m, and 7.35 m) and employed metagenomic sequencing technology to systematically analyze the topological characteristics of functional microbial community networks, as well as the types and quantities of key microbial groups in the rhizosphere soil of P. euphratica under different GWD conditions. The results indicate that compared to GWDs of 3.8 m and 7.35 m, the average degree and graph density of microbial communities in the rhizosphere soil of P. euphratica at a depth of 5.4 m are the highest. This suggests that at a GWD of 5.4 m, the connectivity and stability of the microbial network structure in the rhizosphere soil of P. euphratica are significantly enhanced. Analysis of the Zi-Pi values within the microbial network structure reveals that, compared to GWDs of 3.8 m and 7.35 m, a depth of 5.4 m supports the greatest variety and quantity of key microbial species in the rhizosphere soil of P. euphratica. The four connecting nodes identified are Actinophytocola, Haladaptatus, Devosia and Pseudonocardia. Spearman correlation analysis demonstrates that the relative abundance of the key bacterial genus Mesorhizobium in the rhizosphere soil of P. euphratica at different GWD is significantly positively correlated with soil catalase (CAT) and urease (UE) activity. Furthermore, the relative abundance of the key bacterial genus Pseudonocardia shows a significant positive correlation with soil total nitrogen (TN) and ammonium nitrogen (NH₄⁺-N) (p < 0.05). The relative abundance of the key bacterial genus Devosia exhibits a highly significant positive correlation with soil water content (SWC) (p < 0.01) and a significant negative correlation with soil NH₄⁺-N (p < 0.05). Additionally, the relative abundance of Devosia is significantly positively correlated with soil CAT (p < 0.05). This study provides a theoretical foundation for the conservation of desert poplar forests in arid regions and for the identification and cultivation of specific key microbial communities in the rhizosphere soil of P. euphratica.

Roadside green spaces function as critical ecological barriers in urban environments, and their plant communities play a key role in improving regional air quality. This study investigates typical roadside plant communities in southern Xinjiang, a region characterized by extreme aridity and frequent dust storms. By quantifying indicators such as dust retention capacity at both individual and community levels, together with leaf surface microstructural characteristics, we evaluate the comprehensive dust retention performance of different community configuration patterns. The results show that: (1) Among the studied species, Juniperus chinensis ‘Kaizuca’ exhibited the highest dust retention capacity per unit leaf area, followed by Juniperus chinensis L. and Rosa rugosa Thunb. Among trees, Platanus acerifolia (Aiton) Willd showed the greatest dust retention capacity per individual plant; among shrubs, Rosa rugosa Thunb. performed strongly; and among herbaceous species, Lolium perenne L. exhibited relatively high dust retention capacity. (2) Leaf dust retention is governed by the synergistic effects of multiple traits, including leaf aspect ratio, stomatal aspect ratio, stomatal protrusion, stomatal density, wax layer characteristics, and surface roughness. Leaf aspect ratio exerts a significant positive direct effect on dust retention, whereas stomatal aspect ratio shows a significant negative direct effect. (3) At the community level, the multi-layered tree–shrub–herbaceous configuration dominated by Platanus acerifolia (Aiton) Willd exhibited the strongest dust retention capacity, making it the most effective configuration for roadside green spaces. Overall, this study provides a robust theoretical framework and empirical evidence for the scientific selection and optimized configuration of roadside vegetation in arid regions, thereby supporting the sustainable improvement of urban roadside air quality in southern Xinjiang.