Soil salinization is one of the most widespread soil degradation processes on Earth, especially in arid and semi-arid areas. The salinized soil in arid to semi-arid Xinjiang Uyghur Autonomous Region in China accounts for 31% of the area of cultivated land, and thus it is pivotal for the sustainable agricultural development of the area to identify reliable and cost-effective methodologies to monitor the spatial and temporal variations in soil salinity. This objective was accomplished over the study area (Keriya River Basin, northwestern China) by adopting technologies that heavily rely on, and integrate information contained in, a readily available suite of remote sensing datasets. The following procedures were conducted: (1) a selective principle component analysis (S-PCA) fusion image was generated using Phased Array Type L-band SAR (PALSAR) backscattering coefficient (σ°) and Landsat Enhanced Thematic Mapper Plus (ETM+) multispectral image of Keriya River Basin; and (2) a support vector machines (SVM) classification method was employed to classify land cover types with a focus on mapping salinized soils; (3) a cross-validation method was adopted to identify the optimum classification parameters, and obtain an optimal SVM classification model; (4) Radarsat-2 (C band) and PALSAR polarimetric images were used to analyze polarimetric backscattering behaviors in relation to the variation in soil salinization; (5) a decision tree (DT) scheme for multi-source optical and polarimetric SAR data integration was proposed to improve the estimation and monitoring accuracies of soil salinization; and (6) detailed field observations and ground truthing were used for validation of the adopted methodology, and quantity and allocation disagreement measures were applied to assess classification outcome. Results showed that the fusion of passive reflective and active microwave remote sensing data provided an effective tool in detecting soil salinization. Overall accuracy of the adopted SVM classifier with optimal parameters for fused image of ETM+ and PALSAR data was 91.25% with a Kappa coefficient of 0.89, which was further improved by the DT data integration and classification method yielding an accuracy of 93.01% with a Kappa coefficient of 0.92 and lower disagreement of quantity and allocation.

As an important ecosystem, the wild fruit forest in the Tianshan Mountains is one of the origins of many fruit trees in the world. The wild fruit forest in Emin County, Xinjiang, China, was taken as the research area, the spatial and temporal distribution of the wild fruit forest was inverted using random forest and PLUS models, and the 2027 distribution pattern of the wild fruit forest was simulated and predicted. From 2007 to 2013, damage to the wild fruit forest from tourism and overgrazing was very serious, and the area occupied by the wild fruit forest decreased rapidly from 9.59 km² to 7.66 km². From 2013 to 2020, suitable temperatures and reasonable tourism management provided strong conditions for the rejuvenation of wild fruit forests. The distance of the center of gravity of the wild fruit forest increased, and the density of distribution of the wild fruit forest in the northwest direction of the study area also increased. It is predicted that the wild fruit forest in the study area will show a steady and slowly increasing trend in places far away from tourist areas and with more complex terrain. It is suggested that non-permanent fences be set up as buffer zones between wild fruit forests, ensuring basic maintenance of wild fruit forests, limiting human disturbance such as overgrazing, and reducing the risk of soil erosion.

Soil water retention is a critical aspect of water conservation. To quantitatively assess the Soil Water-Holding Capacity (SWHC), this study focused on a typical wild fruit forest in Xinjiang, China. The spectral characteristics of the forest canopy were employed as a bridge to enhance the sensitivity between the SWHC and various vegetation indices using mathematical statistical methods. This study integrated hyperspectral technology with machine learning algorithms to model complex nonlinear relationships and to select the optimal SWHC model. The spatial distribution of SWHC in the wild fruit forests of Emin County was retrieved using Sentinel-2 imagery. The results revealed a significant negative correlation between SWHC and the smoothed leaf spectral reflectance, with the best correlation coefficient was r = − 0.59. The use of third-order derivatives and logarithmic second-order derivatives further enhanced this correlation, yielding optimal coefficients of r = − 0.78 and r = − 0.72, respectively. Moreover, uncertainty analysis demonstrated that the SWHC estimation model constructed using the Random Forest (RF) algorithm exhibited the highest stability, with a coefficient of determination R² = 0.73, RMSE = 0.158, and RPD = 1.90. The spatial inversion results indicated that SWHC values were relatively higher in areas with dense wild fruit forest coverage and valley terrain. This study is the first to jointly incorporate high-order spectral derivatives and uncertainty analysis into the modeling of SWHC in wild fruit forests, underscoring the advantages of spectral feature enhancement and variable perturbation analysis for improving model stability. The findings provide novel insights into SWHC inversion and offer valuable references for ecological restoration, enhancing the water conservation function of wild fruit forests, and formulating targeted management strategies.

This study systematically investigates cadmium (Cd) accumulation and translocation mechanisms in woody plants through integrated analysis of 16 species. Roots consistently exhibited the highest Cd concentrations (0.26 ± 0.13 mg/kg), serving as primary accumulation sites, while bark functioned as a critical secondary storage organ (0.22 ± 0.09 mg/kg) with strong physiological coordination to roots (r = 0.72, p < 0.001). Leaves demonstrated strict Cd restriction (0.09 ± 0.05 mg/kg) and low variability (CV = 48.7%), indicating evolutionary adaptations to minimize phytotoxicity in photosynthetic tissues. Three functional groups were identified: hyperaccumulators (e.g., Ulmus pumila, root/leaf ratio = 6.37), excluders (e.g., Malus spectabilis, root/leaf ratio = 1.12), and intermediate species (e.g., Syringa oblata) with balanced translocation patterns. Strong root-bark correlations (r = 0.68) and negative stem-leaf associations (r = −0.42) revealed complex interorgan translocation dynamics. Cd speciation analysis showed dominant residual fractions in soils (60–80%) and elevated water-soluble or weakly bound Cd in roots (35–52%). These findings provide a mechanistic basis for designing species-specific phytoremediation strategies, including phytoextraction and ecological stabilization. It will identify suitable tree species for effectively stabilizing or containing the metal pollution within a defined area, thereby preventing its lateral spread or leaching.

The Taklamakan Desert, the primary source of sand and dust in China, has a significant impact on PM_2.5 concentrations in the urban belt around the Tarim Basin. In this study, moderate-resolution imaging spectroradiometer (MODIS) images and machine learning algorithms were used to simulate PM_2.5 concentrations and analyze deaths from ischemic heart disease (IHD), chronic obstructive pulmonary disease (COPD), lung cancer (LC), and stroke (STK) attributed to PM_2.5. From 2015 to 2024, the aerosol optical depth (AOD) showed a decreasing trend, and the spatial distribution pattern gradually increased from west to east. The counties and cities with higher average PM_2.5 concentrations during the 2024 dust-prone season were Awati County, Alar City, Moyu County, and Aksu City, with average concentrations of 89.7 μg/m³, 88.8 μg/m³, 86.0 μg/m³, and 84.1 μg/m³, respectively. PM_2.5 concentrations during the non-dust season were significantly lower (approximately 50%) than those during the dust-prone season. Among the four major fatal diseases, STK and IHD accounted for the majority of attributable deaths, with proportions exceeding 80% of the total. The results show that PM_2.5 concentrations can be accurately monitored over a large scale, addressing the shortage of fixed monitoring stations, and providing a theoretical basis for sustainable development of ecological environment, health-risk management and control in the urban belt around the Tarim Basin.