Phenological change is an emerging hot topic in ecology and climate change research. Existing phenological studies in the Qinghai–Tibet Plateau (QTP) have focused on overall changes, while ignoring the different characteristics of changes in different regions. Here, we use the Global Inventory Modeling and Mapping Studies (GIMMS3g) normalized difference vegetation index (NDVI) dataset as a basis to discuss the temporal and spatial changes in vegetation phenology in the Qinghai–Tibet Plateau from 1982 to 2015. We also analyze the response mechanisms of pre-season climate factor and vegetation phenology and reveal the driving forces of the changes in vegetation phenology. The results show that: (1) the start of the growing season (SOS) and the length of the growing season (LOS) in the QTP fluctuate greatly year by year; (2) in the study area, the change in pre-season precipitation significantly affects the SOS in the northeast (p < 0.05), while, the delay in the end of the growing season (EOS) in the northeast is determined by pre-season air temperature and precipitation; (3) pre-season precipitation in April or May is the main driving force of the SOS of different vegetation, while air temperature and precipitation in the pre-season jointly affect the EOS of different vegetation. The differences in and the diversity of vegetation types together lead to complex changes in vegetation phenology across different regions within the QTP. Therefore, addressing the characteristics and impacts of changes in vegetation phenology across different regions plays an important role in ecological protection in this region.

Forest aboveground biomass (AGB) is a key indicator reflecting the function and quality of forest ecosystems, and accurate large-scale estimations of forest AGB are essential for effective forest ecosystem management. However, owing to limitations in forest AGB mapping methods and the availability of ground-based survey data, Xinjiang still lacks provincial-level forest AGB distribution maps. In this study, we focused on four major forest types in Xinjiang: Evergreen Needleleaf Forest (ENF), Deciduous Needleleaf Forest (DNF), Deciduous Broadleaf Forest (DBF), and Mixed Forest (MF). Using topographic and meteorological data and satellite imagery from Landsat and MODIS as the main data source, we applied the Boruta algorithm for feature variable screening. We then combined these features with Xinjiang forest inventory data to construct support vector machine (SVM), extreme gradient boosting (XGBoost), and random forest (RF) models for estimating the biomass of different forest types for AGB mapping of forests at the provincial scale in Xinjiang. Based on this, the regional distribution patterns of forest biomass were analysed. The findings indicated that climate, topography, and texture factors significantly influenced the selection of characteristic variables in the development of large-scale biomass inversion models. The RF model, combined with different forest types, significantly improved the estimation accuracy of forest AGB. Among the three machine learning estimation models, RF demonstrated the highest estimation accuracy, with R² values greater than 0.65, root mean square error (RMSE) between 24.42 and 41.75 Mg/hm² and mean absolute error (MAE) between 30.59 and 60.46 Mg/hm² for the four forest types. These results were more accurate than the estimated results for all forest sample plots. (3) The geographical distribution map of forest biomass, calculated using the optimum model, revealed that regions with high AGB values were mostly located in the three major mountain rims, followed by the Ili Valley and areas near the Tarim River Basin, with significant spatial heterogeneity. The findings of this study enhance the accuracy of forest AGB estimation in Xinjiang, providing a basis for variable selection in forest biomass estimation models and offering theoretical references and technological assistance for the remote sensing estimation of forest AGB across large areas.

Surface temperature is one of the most basic requirements for the biosphere's existence on earth. Hence, to evaluate urban ecoenvironmental quality, investigating the characteristics of the spatial and temporal surface temperature is essential. In this study, multisource spatial data were used to invert Urumqi's surface temperature over a large time span. Characteristics of spatial pattern of the surface temperature were quantitatively analyzed using a spatial autocorrelation and standard deviation ellipse. A geo-detector model was used to determine the correlation between the surface temperature and 10 influencing factors. Urumqi's surface temperature exhibited a significant geographical heterogeneity from 2000 to 2019, such that a clear heat island effect was observed in the city. From 2000 to 2010, sub-high temperature, high temperature, and severe high-temperature centroids changed 30° to the northwest; whereas from 2010 to 2019, the temperature centroid shifted 13° to the southwest. Among all the factors, the impervious surface has greatest impact on the urban heat island effect in Urumqi. The relationship between the impervious surface and other parameters was the most visible in the interaction detection. In the different land use categories, building land exhibited the highest average surface temperature. Compared with other studies, this study considers the influence of natural and human factors on urban heat island effect more comprehensively, and also quantitatively analyzes the difference of different land use types on urban heat island effect, which makes up for the defects of previous qualitative analysis.

With rapid urbanization and increasingly prominent environmental issues, objective evaluation of the quality of the ecological environment is crucial for environmental protection and sustainable development. Most remote sensing ecological indices (RSEI) used for ecological environmental quality evaluation include only four indicators (greenness, humidity, heat, and dryness), and many studies have ignored the impact of air quality on urban ecological environmental quality in arid areas. This study used the urban agglomeration on the northern slope of the Tianshan Mountains (UANSTM), China, as the research area based on the Google Earth Engine platform via Landsat remote sensing images and NPP/VIIRS data to establish a new remote sensing ecological index (RSEI_new) and compounded night light index of urbanization level. The coupling coordination degree model was used to quantitatively analyze the characteristics of the coordinated development of the ecological environment and urbanization in UANSTM and major cities from 2015 to 2020. The results showed that: (1) compared to RSEI, RSEI_new is more suitable for assessing the ecological quality of arid zones because it accounts for air quality; (2) the RSEI_new value for the eco-environmental quality of UANSTM from 2015 to 2020 improved and then deteriorated with an overall declining trend. The variation in the RSEI_new rating was between “strongly bad” and “neutral,” and there were differences in the quality of the ecological environments among cities; (3) the level of urbanization in the economic zone of UANSTM from 2015 to 2020 increased significantly, and the degree of coordination between urbanization and ecological environmental quality coupling steadily increased but remained moderately imbalanced. The results of this study provide a scientific reference for the economic development and ecological environmental protection of the study area.

Vegetation dynamics in arid and semi-arid regions have an important impact on carbon cycle, water cycle, and energy exchange at local, regional, and global scales. Therefore, it is of great significance for scientists to grasp the changes of vegetation cover in arid and semi-arid regions timely and accurately. Based on this, the applicability of ESTARFM model in the complex terrain area of arid and semi-arid Xinjiang was explored using Landsat and MODIS data fusion, and the overall change characteristics of vegetation cover (FVC) and the distribution and change patterns of different terrains in the study area in the past 15 years were analyzed by combining the dimidiate pixel model, unary linear regression and digital elevation model. The results show that: (1) the NDVI data fused by ESTARFM Model has high consistency with the real NDVI data, and it can be used for subsequent FVC estimation. (2) From 2006 to 2020, the inter FVC was at a high level as a whole, and the average annual FVC showed a weak increasing trend in fluctuation; there are obvious differences in spatial distribution, which is characterized by high distribution in the north and low in the south. (3) The improved area of vegetation cover in the study area is greater than the degraded area, accounting for 52.3% and 47.7% respectively; (4) In the elevation range of 2000 to 3500 m, the FVC showed a slight degradation trend on 25° to 45° slopes and south and southeast slopes, and the rest showed a slight improvement trend. ESTARFM-based model enables monitoring of vegetation cover changes in complex terrain areas of the arid and semi-arid regions in Xinjiang over a long time series. The overall FVC level in the study area is high, and there both are serious degradation and improvement phenomena.

Ecosystem water use efficiency (WUE) is an important measure of the degree of water–hydrogen coupling and an important indicator for assessing ecosystem responses to climate change. Drought adversely affects ecosystem security, particularly in irrigated agricultural areas; therefore, understanding the relationship between WUE and drought is important. This study revealed the spatial and temporal characteristics of drought in the Manas River Basin, Xinjiang, China, from 2001 to 2020 through multi-source data using standardised anomaly indices and mutation detection. It also quantitatively analysed the hysteresis effect and resilience characteristics of drought for different vegetation types in the study area. The results showed that droughts at a severe level occurred less frequently in most of the study area on average from 2001 to 2020, and that droughts in the vegetation growing season occurred more frequently, particularly in grasslands; the frequency of droughts in woodlands was low. Furthermore, the lag in WUE to drought occurred on a 3-month scale and accounted for 64.0% of the total watershed area. Finally, 38.16% of the regional vegetation ecosystems in the Manas River Basin exhibited drought resistance. In conclusion, our results provide novel insights into the water-use strategies of plants in the study area and will help facilitate WUE optimisation.