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A comprehensive understanding of soil salinity characteristics and the vertical and spatial distribution of particle sizes in lakes and wetlands within arid zones, as well as elucidating their interrelationship, is crucial for effective wetland soil salinization management. In this study, the typical salinized wetland, the Ebinur Lake wetland, was selected as the research object. A total of 50 sampling points were established along the edge of Ebinur Lake, resulting in the collection of 200 soil samples from depths of 0–60 cm. The particle size distribution (PSD) of the soil samples was obtained by laser particle sizer, and the fractal dimension of the soil structure was deduced by applying fractal theory. The soluble salt content (TSS) and salt ions content were measured by laboratory physicochemical experiments. Finally, Pearson correlation and other methods were used to explore the relationship between soil salinity and soil particle size. The results showed the following: (1) Soil salinization in the study area was severe, and the accumulation of surface salts was obvious, with a mean value of 46,410 mg/kg. The spatial distribution of TSS was predominantly influenced by Cl−, SO42−, Na+ + K+, Mg2+, and Ca2+. (2) Across various soil depths, silt and sand were the primary constituents, with soil fractal dimensions (Dsoil) ranging from 1.91 to 2.76, averaging 2.54, and a poor soil textural structure. The spatial distribution of Dsoil closely mirrored that of TSS. (3) According to the correlation analysis results, as TSS increased, Dsoil continued to rise, with an increasing content of clay, while the sand content decreased. Simultaneously, as the soil particles became finer, TSS and Dsoil also increased, suggesting that sandy loam to silty soils in the study area were more prone to salt accumulation.

Carbon use efficiency (CUE) and water use efficiency (WUE) are key metrics for quantifying the coupling between terrestrial ecosystem carbon and water cycles. The impacts of intensifying climate change and human activities on carbon and water fluxes in Central Asian vegetation remain unclear. In this study, the CUE and WUE in Central Asia from 2001 to 2022 were accurately estimated with the help of the Google Earth Engine (GEE) data platform; the Theil–Sen median slope estimation combined with the Manna–Kendall significance test and partial derivative analysis were used to investigate the CUE and WUE trends and their responses to climate change and human activities. CUE and WUE show overall declining trends with significant spatial variability. Among meteorological factors, vapor pressure deficit and temperature show the strongest correlation with CUE, while precipitation and temperature are most correlated with WUE. Compared to human activities, climate change has a greater impact on CUE and WUE, mainly exerting a negative influence. Human activities are the main drivers in regions with developed agriculture, such as oases, farmlands, and areas near rivers and lakes. This study provides scientific references for the optimization of water and soil resources and the integrated regional environmental management in Central Asia.

Xinjiang is a representative dry area in China characterized by oasis agriculture. In recent decades, the amount of farmland has increased considerably. For the regional objectives of “carbon peaking and carbon neutrality”, it is essential to investigate the carbon effects induced by large-scale changes in farmland. This research integrates the PLUS and InVEST models to calculate the carbon effects resulting from the spatiotemporal changes in farmland distribution in Xinjiang. It quantitatively assesses the changes in land-use patterns and carbon storage under four scenarios for 2035—natural development (ND), economic development (ED), ecological protection (EP), and farmland protection (FP)—and explores the spatial agglomeration degree of the carbon effect of cultivated land area change. The analysis reveals the following: (1) From 1990 to 2020, the farmland area in Xinjiang showed a trend of first decreasing and then increasing, resulting in a total increase of 33,328.53 km2 over the 30-year period. The newly added farmland primarily came from grassland and unused land. (2) The terrestrial ecosystem carbon storage in Xinjiang showed a trend of decreasing first and then increasing, with an increase of 57.49 Tg in 30 years. The centroid of carbon storage was located in the northwestern part of the Bayingolin Mongol Autonomous Prefecture, showing an overall southwestward shift. Changes in farmland area contributed to a regional carbon storage increase of 45.03 Tg. The contribution of farmland to carbon storage increased by 3.42%. (3) In 2035, the carbon storage value of different scenarios will increase compared with 2020, and the carbon sink of cultivated land will be the maximum under the cultivated land protection scenario. (4) There is a strong spatial positive correlation between the changes in carbon storage caused by the change in cultivated land area in Xinjiang, and there are more hot spots than cold spots. The carbon storage changes under farmland transformation have the characteristics of “high-high” clustering and “low-low” clustering. Future territorial spatial planning in Xinjiang should comprehensively coordinate ecological protection and farmland conservation measures, improve regional carbon sink capacity, and achieve green and sustainable development.

Research on the carrying capacity of land resources and their outputs to population size will be able to better serve the ecological governance of food security. This paper analyzes the population carrying capacity level of southern Xinjiang by using the population carrying capacity model of land resources, and ANOVA analyzes the significance difference between the newly added arable land and the original arable land. At the same time, the demand of cultivated land development is discussed. The results showed that the sown area of grain crops in all the prefectures showed an overall increase from 2009 to 2019. The population carrying scale is more optimistic under subsistence and well-off conditions. From 2009 to 2019, cropland in Aksu region increased the most, followed by Kashgar region, with both regions exceeding 300,000 ha. ANOVA results show that the new arable land is comprehensively lower than the original arable land in terms of quality grade and agricultural output, and salinization and desertification are more serious than on the original arable land. At the same time, the subsistence type requires the least amount of arable land development while the affluent type requires the most. Therefore, we should adapt to local conditions and develop modern agriculture scientifically and reasonably.

Accurate and reliable precipitation data are important for analyzing regional precipitation distribution, water resource management, and ecological environment construction. Due to the scarcity of meteorological stations in the Turpan–Hami region, precipitation observation conditions are limited, and it is difficult to obtain precipitation data. Firstly, the applicability of TRMM 3B43v7, GPM_3IMERGM 06, and CMORPH CDR satellite precipitation data for the Turpan–Hami Region was evaluated, and the products with better applicability were selected. Next, the Extreme Gradient Boosting Algorithm (XGBoost) and the Shapley Additive Explanations for Machine Learning (SHAP) model were combined to carry out a feature importance analysis on the climate factors affecting precipitation (mean temperature, actual evapotranspiration, wind speed, cloud cover), from which climate factors with a greater influence on precipitation were selected. Combined with climate factors, normalized difference vegetation index (NDVI), slope, aspect, and elevation as explanatory variables, a Multi-Scale Geographically Weighted Regression (MGWR) model was constructed to obtain the monthly precipitation data of 1 km spatial resolution in the Turpan–Hami area from 2001 to 2020. Finally, the spatiotemporal distribution characteristics and changing trend of precipitation in the Turpan–Hami region from 2001 to 2020 were analyzed. The results show that (1) GPM_3IMERGM 06 satellite precipitation data exhibits good applicability in the Turpan–Hami region. (2) The precision verification of the downscaling results from a monthly scale and an annual scale shows that the accuracy and spatial resolution of the data are improved after downscaling. (3) From 2001 to 2020, the precipitation in the Turpan–Hami region showed an insignificantly increasing trend.

Habitat quality has great significance in terms of regional ecological conservation and human welfare. In this study, we evaluated the spatial and temporal characteristics of land use and habitat quality in the temperate desert sub-region of the Ordos Plateau using patch-generating land use simulation (PLUS) and integrated valuation of ecosystem services and trade-offs (InVEST) models. From 2000 to 2020, the areas of grassland, cropland, and unused land in the study area increased significantly; the areas of water bodies and woodland increased slightly; and the area of wasteland decreased significantly. Moreover, the habitat quality in the temperate desert subzone of the Ordos Plateau showed a trend of initial increase and then decrease between 2000 and 2020. The areas of lower and low habitat quality first decreased and then increased, and the overall area decreased over time. Conversely, the areas of high and higher habitat quality initially increased and then decreased, and the overall area increased over time. The area of medium habitat quality first decreased and then increased, although the overall change was minimal. Based on the PLUS model, the predicted habitat quality of the study area in 2025 under the natural development scenario was compared to that predicted under the ecological conservation scenario. The comparison of results showed higher habitat quality and lower habitat degradation under the ecological conservation development scenario. These results can be used to provide a scientific basis and decision reference for the sustainable use of land resources and encouragement of high-quality socio-economic development in the temperate desert sub-region of the Ordos Plateau.

Ecological challenges resulting from soil salinization in the Tarim River Basin (TRB), exacerbated by climate change and human activities, have emphasized the need for a quick and accurate assessment of regional ecological environmental quality (EEQ) and driving mechanisms. To address this issue, this study has developed a remote-sensing ecological index with salinity adaptability (RSEISI) for EEQ assessment in the Tarim River Basin by integrating the comprehensive salinity index (CSI) into the remote-sensing ecological index (RSEI). The RSEISI enhances the sensitivity of soil salinity and characterizes the surface features of arid regions, thus expanding the applicability. Then, we used time-series analysis methods and a geodetector to quantify the spatial temporal trends and driving factors of EEQ in the TRB from 2000 to 2022. The results show that the RSEISI with salinity adaptation effectively monitors the EEQ of the TRB. The EEQ of the TRB displayed the situation of oasis expansion, desert deterioration, and glacier melting, and the multiyear average EEQ grades were dominated by medium and poor grades in desert and saline areas, while medium, good, and excellent grades were concentrated in oasis and mountainous areas. Looking at the trend of change in conjunction with land-use types, the EEQ of the TRB showed a mild degradation trend mainly in unused land, followed by a mild improvement trend in cropland and grassland. The Hurst index indicated that the EEQ of most areas of the TRB will improve in the future. Soil type, land use, precipitation, and temperature were considered to be key factors affecting the EEQ across the TRB, and changes in the EEQ were found to be the interaction of multiple factors. This study may provide innovative concepts and methodologies, scientific and technological support for ecological management, and green development models in the northwest arid zone.