As a predominant ecosystem-providing area and distinctive landscape in arid regions, an oasis plays an important role in maintaining land stability, human production, and daily activities. Studying the dynamics of oasis and its driving factors is vital to supporting arid regions’ sustainable development. As a typical mountain–desert–oasis landscape, the Turpan–Hami (Tuha) Basin, located in Xinjiang, China, includes complex interactions among different land types. For this study, we revealed the spatio-temporal patterns and transition processes of the oasis using a complex network method between 1990 and 2020 in the Tuha Basin. In the oasis transition network, the degree value, betweenness centrality, and average path length were calculated to express the transition relationship, key oasis type, and oasis structural stability, respectively. Six factors related to climate change and human actives were selected to investigate the driving forces behind oasis transitions, including the average temperature and precipitation in the growing season, the total power of agricultural machinery (TAMP), the production of raw coal (PRC), the total output value of the plantation industry (TPI), and the population (Pop). Our results show that the oasis area of the Tuha Basin, including the natural oasis and artificial oasis, all grew from 1990 to 2020, with the natural oasis expanding more than the artificial oasis. The transitions between oasis types became more frequent as the area of oasis land types increased throughout the study period. Grassland acted as the most important oasis type in the network, with the highest betweenness centrality, but its importance declined due to the increasing complexity of the oasis transition network from 1990 to 2020. The transitions between oasis types became simpler, and the oasis structural stability decreased. Through driving force analysis, the oasis changes showed a positive correlation with the temperature (p-value < 0.05, r = 0.88), and urbanization and industrialization factors prompted transitions to built-up areas and cropland from grassland and shrubland. In summary, our results suggest that to create a harmonious symbiotic relationship between the natural environment in dryland and human activities, preventing grassland degradation and excessive reclamation of land cover is an available way. Meanwhile, the protection of shrubland and water resources is also important. This study provided reference and theory support for promoting the sustainable development of oases.

The arid region of Northwest China (ARNWC) faces severe desertification, posing a major threat to ecological sustainability and socio-economic development. However, systematic evaluation of desertification across the entire northwestern arid zone remains limited. To address the uncertainty caused by mixed pixels in sparsely vegetated drylands, this study innovatively integrates vegetation and soil indices to develop a robust machine learning-based system for classifying desertification levels in the ARNWC over three decades. In addition, the geographical detector method is employed to quantify the driving factors influencing desertification. The key findings are as follows: (1) Desertification expansion predominantly occurred between 1990 and 1995, followed by a gradual improvement from 1995 to 2020. Transitions between severe and moderate desertification were the most frequent, with approximately 15 × 10⁴ km² shifting from severe to moderate desertification. (2) Physiographic factors were the primary drivers of changes in desertification level, followed by climatic factors. Fractional Vegetation Cover (FVC) had the strongest influence, with an average q-value of 0.72. (3) The explanatory power of the drivers increased significantly through interactions, with the combination of FVC and evaporation (EVA) showing the most pronounced effect. Overall, the methods and findings of this study provide critical insights for targeted desertification control and ecological restoration strategies in arid regions. Although this approach primarily captures desertification symptoms related to surface cover, it offers a valuable long-term perspective on surface cover dynamics.