Ecosystems in arid areas are under pressure from human activities and the natural environment. Long-term monitoring and evaluation of arid ecosystems are essential for achieving the goal of sustainable development. The Xinjiang Uygur Autonomous Region (Xinjiang) is a typical arid region located in Northwest China with a relatively sensitive ecosystem. Under the support of the Google Earth Engine (GEE) cloud platform’s massive data collection, the remote sensing ecological index (RSEI) from 2000 to 2020, both in summer and spring, is established, and the variation trend of the ecological quality in Xinjiang is evaluated by coefficient of variation (CV), Sen’s slope analysis, Mann–Kendall trend test (M–K test) and Hurst index. In addition, a partial correlation analysis is processed between RSEI and selected climatic factors, including precipitation and temperature, to find out the mode of correlation between ecological quality and the natural climate. In the last two decades the following has become apparent: (1) The RSEI values of Xinjiang have been relatively low and unstable both in summer and spring, with a trend toward increasing; (2) The distribution characteristics of RSEI levels both in summer and spring have been similar; low levels were concentrated in the desert and wilderness, while high levels were concentrated around the oasis; (3) The ecological quality in Xinjiang has been relatively stable, with a trend of sustained increase both in summer and spring. There was also a small area of sustained decrease around the Junggar Basin and Turpan Basin in summer and a small area of significant decrease in the center of the Taklamakan Desert in spring; (4) In summer, the precipitation has obviously positively correlated in the Southwest. The temperature has obviously positively correlated in the northwestern part; in spring, the precipitation has obviously positively correlated in the Western part; the temperature has obviously positively correlated in the oasis around the Yili River Basin and Tarim Basin.

Landslides have become a common global concern because of their widespread nature and destructive power. The Gaizi Valley section of the Karakorum Highway is located in an alpine mountainous area with a high degree of geological structure development, steep terrain, and severe regional soil erosion, and landslide disasters occur frequently along this section, which severely affects the smooth flow of traffic through the China-Pakistan Economic Corridor (CPEC). In this study, 118 views of Sentinel-1 ascending- and descending-orbit data of this highway section are collected, and two time-series interferometric synthetic aperture radar (TS-InSAR) methods, distributed scatter InSAR (DS-InSAR) and small baseline subset InSAR (SBAS-InSAR), are used to jointly determine the surface deformation in this section and identify unstable slopes from 2021 to 2023. Combining these data with data on sites of historical landslide hazards in this section from 1970 to 2020, we constructed 13 disaster-inducing factors affecting the occurrence of landslides as evaluation indices of susceptibility, carried out an evaluation of regional landslide susceptibility, and identified high-susceptibility unstable slopes (i.e., potential landslides). The results show that DS-InSAR and SBAS-InSAR have good agreement in terms of deformation distribution and deformation magnitude and that compared with single-orbit data, double-track SAR data can better identify unstable slopes in steep mountainous areas, providing a spatial advantage. The landslide susceptibility results show that the area under the curve (AUC) value of the artificial neural network (ANN) model (0.987) is larger than that of the logistic regression (LR) model (0.883) and that the ANN model has a higher classification accuracy than the LR model. A total of 116 unstable slopes were identified in the study, 14 of which were determined to be potential landslides after the landslide susceptibility results were combined with optical images and field surveys. These 14 potential landslides were mapped in detail, and the effects of regional natural disturbances (e.g., snowmelt) and anthropogenic disturbances (e.g., mining projects) on the identification of potential landslides using only SAR data were assessed. The results of this research can be directly applied to landslide hazard mitigation and prevention in the Gaizi Valley section of the Karakorum Highway. In addition, our proposed method can also be used to map potential landslides in other areas with the same complex topography and harsh environment.

Temperature and precipitation are crucial indicators for investigating climate changes, necessitating precise measurements for rigorous scientific inquiry. While the Fifth Generation of European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis (ERA5), ERA5 of the Land Surface (ERA5-Land), and China Meteorological Forcing Dataset (CMFD) temperature and precipitation products are widely used worldwide, their suitability for the Altay region of arid and semi-arid areas has received limited attention. Here, we used the Altay region as the study area, utilizing meteorological station data and implementing the residual revision method for temperature and the coefficient revision method for precipitation to rectify inaccuracies in monthly temperature and precipitation records from ERA5-Land, ERA5, and CMFD. We evaluate the accuracy of these datasets before and after correction using bias, Taylor diagrams, and root-mean-square error (RMSE) metrics. Additionally, we employ Tropical Rainfall Measuring Mission satellite precipitation data (TRMM) as a benchmark to assess the performance of ERA5-Land, ERA5, and CMFD monthly precipitation before and after correction. The results revealed significant differences in the temperature and precipitation capture capabilities of ERA5-Land, ERA5, and CMFD in the Altay region. Overall, these data exhibit substantial errors and are not directly suitable for scientific research. However, we applied residual and coefficient revision methods. After this revision, ERA5-Land, ERA5, and CMFD showed significantly improved temperature and precipitation capture capabilities, especially for ERA5-Land. In terms of temperature, post-revision-CMFD (CMFD_PR) demonstrated better temperature capture capabilities. All three datasets showed weaker performance in mountainous regions compared to plains. Notably, post-revision-ERA5 (ERA5_PR) seemed unsuitable for capturing temperature in the Altay region. Concerning rain, CMFD_PR, post-revision-ERA5-Land (ERA5-Land_PR) and ERA5_PR outperformed TRMM in capturing precipitation. CMFD_PR and ERA5-Land_PR both outperform ERA5_PR. In summary, the revision datasets effectively compensated for the sparse distribution of meteorological stations in the Altay region, providing reliable data support for studying climate change in arid and semi-arid areas.