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.

It has become undeniable that global land surface temperature (LST) has continued to rise in recent years. The threat of extreme heat to humans has become self-evident, especially in arid regions. Many studies have clarified the temperature rise/fall mechanism of LST from the perspective of influencing factors. However, there are few studies on mitigating LST from the standpoint of regional networks. This paper first combines Landsat 8 with Sentinel-2 imagery for LST downscaling based on the Google Earth engine as a way to match local climate zone (LCZ) with 17 classification types. Then, the thermal environment resistance surface is constructed according to LCZ, and the essential cold sources are identified using morphological spatial pattern analysis (MSPA) and circuit theory to form the thermal environment green corridor and obtain the pinch point and barrier point areas. The results show that (1) The downscaling of LST based on random forest (RF) for the Urumqi–Changji–Wujiaqu metropolitan area has an R² of 0.860 and an RMSE of 3.23, with high downscaling accuracy. (2) High temperature (HT), medium temperature (MT), and low temperature (LT) have the largest proportions in the study area; HT dominates in Urumqi, LT in Changji, and MT in Wujiaqu. (3) The natural types (LCZ-D, LCZ-C, and LCZ-F) in the LCZ classification occupy a large area, and the building types are mainly concentrated in Urumqi; LCZ-D, LCZ-G, and LCZ-A contribute the most to the cooling of LST, and LCZ-F, LCZ-C, and LCZ-10 contribute the most to the warming of LST. (4) After identifying critical cold source patches according to MSPA to arrive at 253 green corridors, subsensitive corridors and sensitive corridors need to take certain measures to prevent corridor blockage; pinch point areas, as well as barrier point areas, need to be protected and repaired according to their respective characteristics. In summary, corresponding cooling measures to specific areas can improve the connectivity between cooling sources and slow down the temperature increase of the whole area. This study and experimental approach can provide new insights for urban planners and climate researchers.

An in-depth study of the influence mechanism of oasis land surface temperature (LST) in arid regions is essential to promote the stable development of the ecological environment in dry areas. Based on MODIS, MYD11A2 long time series data from 2003 to 2020, the Mann–Kendall nonparametric test, the Sen slope, combined with the Hurst index, were used to analyze and predict the trend of LST changes in the urban agglomeration on the northern slopes of the Tianshan Mountains. This paper selected nine influencing factors of the slope, aspect, air temperature, normalized vegetation index (NDVI), precipitation (P), nighttime light index (NTL), patch density (PD), mean patch area (AREA_MN), and aggregation index (AI) to analyze the spatial heterogeneity of LST from global and local perspectives using the geodetector (GD) model and multi-scale geo-weighted regression (MGWR) model. The results showed that the average LSTs of the urban agglomeration on the northern slopes of the Tianshan Mountains in spring, summer, autumn, and winter were 31.53 °C, 47.29 °C, 22.38 °C, and −5.20 °C in the four seasons from 2003 to 2020, respectively. Except for autumn, the LST of all seasons showed an increasing trend, bare soil and grass land had a warming effect, and agricultural land had a cooling effect. The results of factor detection showed that air temperature, P, and NDVI were the dominant factors affecting the spatial variation of LST. The interaction detection results showed that the interaction between air temperature and NDVI was the most significant, and the two-factor interaction was more effective than the single-factor effect on LST. The MGWR model results showed that the effects of PD on LST were positively correlated, and the impact of AREA_MN and AI on LST were negatively correlated, indicating that the dense landscape of patches has a cooling effect on LST. Overall, this study provides information for managers to carry out more targeted ecological stability regulations in arid zone oases and facilitates the development of regulatory measures to maintain the cold island effect and improve the environment.

Changes in land surface temperature (LST) can have serious impacts on the water cycle and ecological environment evolution, which in turn threaten the sustainability of ecosystems. The urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM) is located in the arid and semi-arid regions of northwest China, with an extremely fragile ecological environment and sensitive to climate change. However, studies on the LST of the UANSTM have not received much attention. Therefore, this study explored the spatial distribution pattern, fluctuation characteristics, and influencing factors of the LST of the UANSTM from 2005 to 2021 based on MODIS time series LST data and the geo-detector model with optimal parameters. The results show that the UANSTM is dominated by medium- and high-temperature classes, with high- and extremely high-temperature classes clustered in Turpan City. The daytime and nighttime LST patterns are significantly different, with a typical “daytime cold island and nighttime heat island” feature in the oasis region. During 2005–2021, LST fluctuated greatly in the northwestern part of the UANSTM, with LST showing an increasing trend during both daytime and nighttime, and the warming rate was more intense during daytime than nighttime. The increasing trend of LST in Urumqi, Changji Hui Autonomous Prefecture, Shihezi, and Wujiaqu is very significant and will remain consistent in the future. Precipitation, DEM, and AOD are the most important influencing factors of LST in the UANSTM, where precipitation and DEM are negatively correlated with LST, and AOD is positively correlated with LST. Land cover factors (LULC, NDVI,, and NDBSI) are the next most influential, and socioeconomic factors (NTL, GDP, and POP) are the least influential. The results of this study can provide a scientific reference for the conservation and sustainable development of the ecological environment of the UANSTM.

The land surface temperature (LST) is an important indicator reflecting the ecological environment condition. As a sensitive area to climate change, mastering the spatial and temporal changes of summer LST in the Bosten Lake basin (BLB) helps gain insight into the evolution of the thermal environment in the Bosten Lake basin and for long-term monitoring of the basic ecological changes in the basin. Based on MOD11A1 data from 2005 to 2020, this paper investigates the diurnal LST spatiotemporal series variation and its influencing factors in the Bosten Lake basin by using surface temperature class classification, trending analysis, the Hurst index, and geographic probes. The results show that (1) the wetland grasslands in and around the Bayinbruck steppe in the northwestern part of the study area exhibit a heat island effect during the day, while the opposite is true at night. In terms of temporal changes, LST changes in the BLB fluctuate widely, having a general rising and then decreasing trend. (2) The decreasing trend of LST from 2005 to 2020 is significant during the daytime and vice versa at night, and the change at night is greater than during the day. The areas with significantly higher diurnal LST in the future have all expanded compared to the area occupied by them now, with an overall trend of a steady increase. (3) The dominant factor of LST variation has the strongest explanatory power when altitude and NDVI are combined during the daytime and the strongest explanatory power when NPP and temperature are combined at night.