Global warming affects the hydrological characteristics of the cryosphere. In arid and semi-arid regions where precipitation is scarce, glaciers and snowmelt water assume important recharge sources for downstream rivers. Therefore, the simulation of snowmelt water runoff in mountainous areas is of great significance in hydrological research. In this paper, taking the Hutubi River Basin in the Tianshan Mountains as the study area, we used the “MODIS Daily Cloud-free Snow Cover 500 m Dataset over China” (MODIS_CGF_SCE) to carry out the Snowmelt Runoff Model (SRM) simulation and evaluated the simulation accuracy. The results showed that: (1) The SRM preferably simulated the characteristics of the average daily flow variation of the Hutubi River from May to October, from 2003–2009. The monthly total runoff was maximum in July and minimum in October. Extreme precipitation events influenced the formation of flood peaks, and the interannual variation trend of total runoff from May to October was increased. (2) The mean value of the volume difference (${\mathrm{D}}_{\mathrm{V}}$) during the model validation period was 8.85%, and the coefficient of determination (${\mathrm{R}}^2$) was 0.73. In general, the SRM underestimates the runoff of the Hutubi River, and the simulation accuracy is more accurate in the normal water period than in the high-water period. (3) By analyzing MODIS_CGF_SCE from 2003 to 2009, areas above 3200 m elevation in the Hutubi River Basin were classified as permanent snow areas, and areas below 3200 m were classified as seasonal snow areas. In October, the snow area in the Hutubi River Basin gradually increased, and the increase in snow cover in the permanent snow area was greater than that in the seasonal snow area. The snowmelt period was from March to May in the seasonal snow area and from May to early July in the permanent snow area, and the minimum snow cover was 0.7%.

In this paper, the Taklimakan Desert land surface emissivity measured by portable Fourier Transform thermal InfraRed spectroscopy (FTIR) was used to re-estimate Global LAnd Surface Satellite (GLASS) BroadBand Emissivity (BBE) and Moderate Resolution Imaging Spectroradiometer (MODIS) BBE optimal coefficients equations. In addition, the revised BBE equations of both GLASS and MODIS with the optimized coefficients were obtained. Comparing the FTIR and MODIS BBE data with the values retrieved before and after the revised GLASS BBE equation, it was found that the revised GLASS BBE equation is more accurate than the original one. First, according to the error analysis with FTIR data, the value of R² is increased from 0.4 to 0.9, and the Root Mean Square Error (RMSE) and Bias are reduced by 1 and 3 magnitude orders, respectively. Second, with MODIS BBE data, the value of R² is increased from 0.6 to 0.9, the RMSE and Bias are reduced by 1 and 2 magnitude orders, respectively. Finally, the Taklimakan Desert BBE was calculated using the revised GLASS BBE equation. The results showed that the BBE values are between 0.890 and 0.920 in the desert center, between 0.920 and 0.950 in the sparse vegetation areas, and between 0.950 and 0.980 in the oasis edges.

One of the key elements influencing carbon emissions is changes in land use, which affects the roles of carbon sources and sinks. We calculated the carbon emissions from land use in Xinjiang, constructed a Geographically and Temporally Weighted Regression model (GTWR), and investigated the spatio-temporal evolutionary trajectory and heterogeneity of carbon emissions based on the land use data of three periods from 2000 to 2020 and the socio-economic and energy data of the same period. The results show the following: ① in Xinjiang, the area of water, forests, grasslands, and idle land declined between 2000 and 2020, while the area of construction and agricultural land increased; ② Xinjiang’s land-use-related carbon emissions are rising annually. The primary driver of this expansion is the region’s growing area of construction land; ③ the degree of economic development, the amount of land used for building, and the degree of urbanization are the primary factors influencing carbon emissions in Xinjiang; and ④ In Xinjiang, there is a clear spatial heterogeneity in the factors that influence carbon emissions. Additionally, different influencing factors have different effects on emissions, depending on the region and stage of development, indicating that the level of resources and the region’s economic center of gravity have different effects on emissions.

Soil moisture (SM) in the mountains is critical for agropastoral productivity, and it is subject to both large-scale climate gradients and fine-scale effects of terrain, vegetation and soil. However, how the climate, topography, soil and vegetation factors impact surface SM spatiotemporal dynamics remains elusive in mountainous terrains, due to their complex interactions. Based on multi-source datasets, this study employs the structural equation model to investigate the impact pathways of climate and vegetation factors on annual surface SM dynamics from the year 2000 to 2022 in the Tianshan Mountains of China (TS). We also utilize the factor and interaction detectors of Geographical Detector to explore the individual and interactive effects of climate, topography, soil and vegetation factors on the spatial pattern of the annual surface SM. Moreover, their integrated impacts on the spatiotemporal dynamics of annual surface SM were investigated based on the explanatory power from the factor detector and total effects from structural equation modeling. The results showed that the multi-year average surface SM was 0.21 m³·m⁻³ for the whole region, with greater values in areas with dense vegetation and high elevation. Annual surface SM exhibited significant increasing trends across different land cover classifications and elevation zones, which was directly influenced by vegetation greenness enhancement. Precipitation (PRE) and relative humidity (RH) also significantly influenced the temporal variations in surface SM through their indirect effect on vegetation greenness, while these indirect effects were much lower than the direct effect of vegetation greenness. RH, PRE and surface net solar radiation (SSR) showed strong individual and interactive effects on the spatial distribution of surface SM, particularly the interactive effects of RH and PRE with wind speed (WS). Surface SM was highly sensitive to RH and PRE in the central TS. Overall, vegetation greenness, PRE and RH were the main drivers of surface SM variations across both temporal and spatial scales, while SSR, total evaporation and WS primarily shaped its spatial distribution. These insights enhance our understanding of land–atmosphere interactions in mountainous areas and provide scientific references for sustainable agropastoral water resource management under global warming.