Global warming accelerates the breakdown of carbon stored in permafrost regions, releasing it into the atmosphere and amplifying climate change, particularly during winter when photosynthesis ceases. The Northern Hemisphere's permafrost is primarily concentrated in two key regions — the Arctic and the Tibetan Plateau — each with distinct environmental characteristics. However, previous studies often treat these regions separately, missing the opportunity to compare their winter CO2 emissions within a unified framework. Here, we synthesized 2,487 monthly CO2 flux measurements from 166 in-situ sites to quantify the spatial and temporal variations and key drivers of winter CO2 emissions in these two regions. Our analysis reveals that combined winter emissions from the Arctic and Tibetan Plateau are estimated to be 1,289 ± 25 Tg C yr-1. From 1982 to 2022, winter CO2 emissions increased by 2.10 ± 0.23 Tg C yr-1. Notably, since 2001, winter CO2 emissions have surged in the Arctic while declining in the Tibetan Plateau. The driving factors also differ: soil temperature dominates in the Arctic (51%), whereas soil moisture plays the most significant role on the Tibetan Plateau (33%). These findings highlight the contrasting mechanisms governing winter carbon emissions in these regions and underscore the importance of incorporating region-specific factors when predicting permafrost-carbon feedbacks in a warming world.

Nitrogen is an essential nutrient for plant growth and plays a critical role in photosynthesis and gross primary productivity (GPP). The light use efficiency (LUE) models are perhaps the most classical and widely used to estimate GPP. However, large uncertainties remain in seasonal GPP estimation, partly due to neglecting the effect of nitrogen on ecosystem GPP. Here, we firstly evaluate the error source of LUE model at 89 FLUXNET sites worldwide and found that the large seasonal errors in model estimates were primarily attributable to model structure, particularly the omission of soil nitrogen constraints. We then examined the relationships between soil nitrogen content and tower GPP over the seasonal scale for deciduous broadleaf forests. We found soil nitrogen mineralization, a process for determining soil available nitrogen, closely matched and exhibited strong correlation with observed GPP at daily scale (R2 = 0.66, p < 0.01), monthly scale (R2 = 0.74, p < 0.01) and seasonal scales (R2 = 0.67, p < 0.01). We further found that incorporating soil nitrogen into the estimation of GPP can improve the explained variance of GPP up to 18%. Our results demonstrated that the significant positive impact of soil nitrogen mineralization on GPP cannot be ignored in terrestrial ecosystem GPP estimation by LUE models. Our finding highlights the potential of soil nitrogen mineralization in the estimation of GPP by LUE models for deciduous broadleaf forests.

Evapotranspiration (ET) plays a vital role in water resource management, climate regulation, and ecosystem functioning. In the Heihe River Basin, an ecologically and agriculturally important region in northwestern China, water availability is the primary constraint on sustainable development. Accurate ET estimation and an understanding of its response to climate change are crucial for quantifying water budgets and informing effective water resource management. In this study, we developed a high-quality ET product for the Heihe River Basin by integrating in situ flux observations from a regional network of 10 flux sites with MODIS-derived NDVI, land use/land cover (LUCC) data, and gridded meteorological variables. Using this dataset, we analyzed long-term ET trends and explored the key drivers of interannual variability. Our results show pronounced spatial heterogeneity in annual ET, ranging from approximately 70 mm/a to 800 mm/a, with the highest and lowest values both occurring in the middle and lower reaches of the basin. During 2001-2024, ET has significantly increased across the basin. Subalpine Forest, Riparian Woodland, Alpine Grassland, Temperate Grassland, Temperate Wetland, Cropland and Gobi/desert all exhibited significant increasing trends, whereas other ecosystems showed no significant changes. Driver analysis revealed that Precipitation and NDVI were the dominant factors controlling interannual variability in 56.4% and 31.8% of the basin, respectively, followed by temperature (7.3%) and downward shortwave radiation (4.4%). Warming-wetting climate caused significant ET increasing trend in the lower reaches of the Heihe River Basin. Land cover change, particularly transitions between vegetated and non-vegetated types, contributed to substantial ET variations at regional scales but had a relatively minor influence at the basin scale. These findings highlight the importance of high-resolution ET monitoring for advancing our understanding of water and energy dynamics in cold and arid inland basins.

Soil organic carbon density (SOCD) is a key indicator for evaluating the stability of soil carbon pools, especially in alpine ecosystems that are sensitive to climate change. However, research on the response of SOCD to different aridity gradients is still limited. Therefore, we conducted extensive sampling along different aridity gradients of the Qinghai-Tibetan Plateau to study how SOCD varies with aridity gradients and how environmental factors drive changes in SOCD under different aridity gradients. The study has found that the SOCD at depths of 0-100 cm on the Qinghai-Tibetan Plateau ranges from 0.39 to 78.12 kg C m-2. Meanwhile, the SOCD is higher in the more humid southeastern region than in the more arid northwestern region. Topography, climate, and vegetation factors jointly regulate the distribution of SOCD. The SOCD shows a nonlinear variation trend and decreases with the increase of aridity; the key aridity threshold is 0.66. The altitude, aridity, mean annual temperature (MAT), and normalized vegetation index (NDVI) are the main drivers of SOCD below this threshold (low aridity gradient). Moreover, the SOCD is primarily affected by the NDVI and MAT above this threshold (high aridity gradient). The above research results emphasize the differences in SOCD across aridity gradients, suggesting that it may be possible to mitigate the negative impact of aridification on soil organic carbon by increasing vegetation coverage in areas with high and low aridity gradients.

Global climate change and intensifying human activities have substantially altered grassland productivity across China, which hosts one of the world's largest and most diverse grassland systems. By integrating multi-source remote sensing, biogeochemical modeling, machine learning, and spatial statistical analyses, we quantified spatiotemporal dynamics of actual net primary productivity (ANPP) across eight major grassland types in China from 2000 to 2019. Results reveal a widespread grassland restoration trend, with mean ANPP increasing by 3.34 g C·m-2·year-1 and 68.4 % of grassland areas exhibiting restoration. Climate change emerged as the dominant driver, promoting productivity across 29.4 % of grasslands, primarily through enhanced precipitation. Human activities independently contributed to restoration across 14.1 % of areas but reduced productivity in many arid regions relative to climate-determined potential. Notably, synergistic interactions between climate change and human activities jointly promoted restoration across 24.9 % of grassland areas. Optimal parameter geographic detector analysis identified precipitation as the strongest control on ANPP spatial variability (q ≈ 0.62-0.73), while individual human activity factors showed weak independent effects. However, climate-human interactions consistently exhibited nonlinear enhancement effects. Future projections based on Random Forest models indicate that the SSP245 moderate-emission scenario yields the highest and most spatially stable grassland productivity by 2060.

Cloud absorption of solar radiation strongly influences Earth's radiation balance and climate change. Whether numerical models underestimate this absorption compared with observations has long been a highly debated issue in cloud-radiation research. Using stateof-the-art model-derived reanalyses, NCEP CFSv2, ECMWF ERA5, and NASA MERRA2, and the latest collocated satellite-surface observation in 2012-2023, we reinvestigate this controversial issue. The results demonstrate the observed cloud absorption of solar radiation still notably exceeds the modeled (regardless of model products), but their discrepancy has dropped a lot, particularly for NCEP CFSv2 and ECMWF ERA5. While a further investigation is needed, the reduced discrepancy may reflect the progress of shortwave radiation schemes in models, notably the integration of Rapid Radiative Transfer Model for General Circulation Models (RRTMG) and the Monte Carlo Independent Column Approximation (McICA). Additionally, it is noteworthy that there is not a perfect approach to obtaining the observed cloud absorption, and particularly the water vapor difference between clear and cloudy skies will often result in its unrealistic overestimation. If the impact from the water vapor difference is corrected, NCEP CFSv2, ECMWF ERA5, and NASA MERRA2 underestimate globally-mean cloud absorption by approximately 8.26, 14.50 and 16.51 W/m2, respectively.

Arid ecosystems are subject to extreme and persistent environmental stress, yet the mechanisms underlying their structural stability and functional persistence under global change remain poorly understood. Although rigorous study on the geographical distribution patterns of microbial communities and their climate-driven mechanisms under climate change is still lacking, deserts play a critical role in global biogeochemical cycles. This study integrates field-collected samples with publicly available sequencing data, using machine learning based spatial prediction techniques to investigate the environmental regulation mechanisms of microbial community structure and functional characteristics across multiple deserts. By integrating these maps with future climate scenarios, we assess the potential impacts of environmental change and human activities on microbial distribution dynamics. Results indicate that desert microbial ecosystems are primarily shaped by deterministic environmental constraints rather than spatial succession. Aridity index, precipitation, temperature, and vegetation cover exert the strongest influence on desert bacterial species richness and functional diversity. Under the SSP245 scenario, microbial diversity and richness are projected to decline by 2.7%, while phylogenetic diversity is expected to increase by approximately 1.6% by 2030, 2050, and 2100. In contrast, the SSP585 scenario shows diversity and abundance increasing until 2050, followed by a decline by 2100, with Shannon diversity decreasing by 4.3% and phylogenetic diversity fluctuating within +/- 3.6% of current values. This study establishes a comprehensive baseline for the spatial distribution and ecological functions of desert microbial communities, thereby improving predictions of arid ecosystem responses to future climate change and degradation, and highlighting the role of desert microbes in climate feedbacks as well as ecosystem resilience under ongoing global warming and desertification.

Despite characterized by large interannual variability (IAV), global terrestrial evapotranspiration (ET) has existed a consistent increasing trend since the 1980s. However, the regions and processes governing the present ET trend and IAV still remain unclear. Using an ensemble of process-based hydrological models, remote sensingbased, machine learning, and land surface products, we find that the increasing trend and substantial IAV in global land ET are driven by divergent regions. Result from models ensemble shows that the humid regions, especially in the Northern Hemisphere, contribute the 72.47 f 5.77 % of the increase in global land ET over 1982-2020. In this domain, climate warming and vegetation greening (increased leaf area index (LAI)) have caused the increase in ET of 0.43 f 0.22 and 0.30 f 0.13 mm yr-2, respectively, although the increased LAI is the largest contributions (63.69 f 25.13 %) to the global ET increases. Especially, climate warming in the humid regions at the high latitudes has prolonged the growing season, and provided sufficient water through freeze-thaw process for the enhanced plants photosynthesis in spring and even summer. The IAV of the global land ET, however, is dominated by drylands (with contribution fractions being 59.66 f 16.89 %), and dominant role in this region is mainly due to the fact that the precipitation, which serves as a primary source of moisture supply, has the large interannual oscillation with the El Nino/Southern Oscillation (ENSO) events and is largely allocated to evaporation. With future anthropogenic warming, global land ET is expected to continue rising with the trait of a significant interannual variations, and the dominant roles of humid regions and drylands still remains and are stronger than that in present. This study will merit more attention about regional roles for understanding and projecting dynamics of the global water cycle.

Abstract Climate warming and increasingly frequent hydroclimatic extremes are altering snowfall regimes and snow cover dynamics, with important implications for frozen‐ground stability. Yet the combined effects of snow cover and environmental controls on ground thermal conditions remain poorly constrained. Using meteorological observations and remote sensing data during 2005–2024, we applied a partial least‐squares path model (PLS‐PM) to quantify how snow cover dynamics and environmental factors jointly influence ground temperature based freezing indices (FI GT ) across China's frozen‐ground regions. Snow cover characteristics and FI GT trends showed strong spatial heterogeneity. The insulating effect of snow varied markedly among regions and was much stronger in the permafrost regions of Northeast China than on the Qinghai‐Tibet Plateau (QTP) or in seasonally frozen ground regions. Over the past two decades, FI GT declined across all frozen ground regions. At 0 cm depth, the rate of decline was greatest in permafrost regions of Northeast China (−25 ± 22°C·d yr −1 ), followed by seasonally frozen ground (−7 ± 10°C·d yr −1 ) and QTP permafrost (−1 ± 7°C·d yr −1 ). Shallow‐soil FI GT was mainly controlled by climatic change, snow insulation, and soil properties, whereas deeper soil FI GT was increasingly governed by soil mediated effects. In regions with stable snow cover, snow exerted a stronger insulating influence on surface and shallow‐soil freezing; in regions with unstable snow cover, FI GT was more strongly constrained by climatic background and geographic setting. The findings advance understanding of frozen‐ground sensitivity to changing snow regimes and provide a scientific basis for assessing ecological and hydrological risks under continued climate warming.

Accurate estimation of actual evapotranspiration (ETa) in arid regions is essential for effective water resource management. Despite notable advances in ETa modeling, limited research has systematically addressed the joint optimization of variable selection, model development, and interpretability. To fill this gap, we performed variables selection through correlation analysis and design eight input strategies that integrate key meteorological and remote sensing variables. Five state-of-the-art machine learning models-Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Tabular Neural Network (TabNet), and Deep Forest (DF)-are employed for ETa prediction. To enhance model interpretability, we apply SHapley Additive exPlanations (SHAP) and Permutation Importance to quantify the contribution of each input variable. Our findings demonstrate that the DF model combined with the S6 input strategy (a combination of six key meteorological and remote sensing variables), denoted as DF6, delivers the best predictive performance. Leveraging this optimal configuration, we generate a high-resolution ETa dataset for Northwest China, spanning from 2001 to 2024 at 8-day intervals with 5 km spatial resolution. Interpretability analysis highlights net radiation (Rn) as the most influential factor, exhibiting a strong positive effect on ETa. Furthermore, Mann-Kendall trend analysis reveals a significant upward trend in ETa, particularly in the northwestern and southeastern subregions. This study presents a robust and interpretable modeling framework that improves ETa estimation under data-limited conditions and supports long-term hydrological analysis and sustainable water management in arid regions.

Soil organic carbon (SOC) is critical for terrestrial carbon cycling processes. Snow cover days (SCD) and snow depth (SD) on the Tibetan Plateau have changed significantly over the past decade and are associated with SOC patterns. However, current field studies lack the spatial resolution to capture the spatial heterogeneity of snowpack effects on soil organic carbon across high-altitude regions, and the relative importance of snow cover has also remained unquantified within the context of complex multifactor interactions. In this study, we aim to fill these knowledge gaps, through machine learning models and structural equation models, using remote sensing data of snow and soil datasets from 2015 to 2023. The results indicate that under the multiple environmental factors, snow cover (SD and SCD) is associated with 32.03 percent of the relative contribution to SOC. The SOC response to snow cover varies significantly across different ecosystem types. Specifically, snow cover influences SOC through both soil temperature (ST) and soil moisture (SM) in alpine meadows, whereas ST is the dominant pathway in alpine steppe and alpine desert. Overall, the spatial patterns averaged from 2015 to 2023 show SM associations at low SCD and ST associations at more persistent SCD. The findings clarify the significance of snow cover in high elevation regions over the past decade for SOC, enhancing our understanding of the terrestrial carbon cycle and carbon balance on the Tibetan Plateau.

Global warming accelerates the breakdown of carbon stored in permafrost regions, releasing it into the atmosphere and amplifying climate change, particularly during winter when photosynthesis ceases. The Northern Hemisphere's permafrost is primarily concentrated in two key regions — the Arctic and the Tibetan Plateau — each with distinct environmental characteristics. However, previous studies often treat these regions separately, missing the opportunity to compare their winter CO2 emissions within a unified framework. Here, we synthesized 2,487 monthly CO2 flux measurements from 166 in-situ sites to quantify the spatial and temporal variations and key drivers of winter CO2 emissions in these two regions. Our analysis reveals that combined winter emissions from the Arctic and Tibetan Plateau are estimated to be 1,289 ± 25 Tg C yr-1. From 1982 to 2022, winter CO2 emissions increased by 2.10 ± 0.23 Tg C yr-1. Notably, since 2001, winter CO2 emissions have surged in the Arctic while declining in the Tibetan Plateau. The driving factors also differ: soil temperature dominates in the Arctic (51%), whereas soil moisture plays the most significant role on the Tibetan Plateau (33%). These findings highlight the contrasting mechanisms governing winter carbon emissions in these regions and underscore the importance of incorporating region-specific factors when predicting permafrost-carbon feedbacks in a warming world.

The eddy covariance (EC) technique is currently the most widely used method for measuring carbon exchange between terrestrial ecosystems and the atmosphere at the ecosystem scale. Using this technique, a regional carbon flux network comprising a total of 34 sites has been established in the Heihe River basin (HRB) in northwest China. This network has been measuring the net ecosystem exchange (NEE) of CO2 for a variety of vegetation types. In this study, we have compiled and post-processed half-hourly flux data from these 34 EC flux sites in the HRB to create a continuous, homogenized time series dataset. We employ standardized processing procedures to fill data gaps in meteorological and NEE measurements at half-hourly intervals. NEE measurements are also partitioned into gross primary production (GPP) and ecosystem respiration (Reco). Furthermore, half-hourly meteorological and NEE data are aggregated into daily, weekly, monthly, and yearly timescales. As a result, we produced a continuous carbon flux and auxiliary meteorological dataset, which includes 18 sites with continuous multi-year observations during 2008–2022 and 16 sites observed only during the 2012 growing season, amounting to a total of 1513 site months. Evapotranspiration and energy flux measurements are also included. Using the post-processed dataset, we explored the temporal and spatial characteristics of carbon exchange in the HRB. In the diurnal variation curve, GPP, net carbon uptake, and Reco peak later for ecosystems in the artificial oasis (cropland and wetlands) compared to those outside the artificial oasis (grassland, forest, woodland, and Gobi/desert). Seasonal net carbon uptake, GPP, and Reco peak in early July for grassland, forest, woodland, and cropland but remain close to zero throughout the year for Gobi/desert. In the last decade, net carbon uptake of wetlands has significantly increased, while NEE for other ecosystems has not exhibited significant trends. Annual net carbon uptake, GPP, and Reco are significantly higher for sites inside the artificial/natural oasis compared to those outside the oasis. This post-processed carbon flux dataset has numerous applications, including exploring the carbon exchange characteristics of alpine and arid ecosystems, analyzing ecosystem responses to climate extremes, conducting cross-site synthesis from regional to global scales, supporting regional and global upscaling studies, interpreting and calibrating remote sensing products, and evaluating and calibrating carbon cycle models. The dataset can be accessed at https://doi.org/10.11888/Terre.tpdc.301321 (Wang et al., 2024).

Quantifying and attributing the spatiotemporal variation of snow phenology in North America and Europe is important for better understanding hydrological processes, the global climate system, and regional water resource management. Using snow depth observation data at 372 meteorological stations from 1960 to 2023, we investigated the spatiotemporal variation of snow-covered days (SCD), snow onset date (SOD), and snow end date (SED) in North America and Europe and their sensitivity to climatic factors. Long-term trends examination indicated that SOD delayed at 87.10 % of stations, SED advanced at 91.13 % of the stations, and SCD decreased at 95.97 % of the stations. Temperature increase is the main driving factors of these changes, particularly after 1990, with significant SCD reductions, delayed SOD, and advanced SED. In stable snow regions, SCD shortens by 6.02 days per 1 degrees C increase in temperature, while in unstable snow regions, the reduction is 6.42 days per 1 degrees C increase. This study highlights the profound impact of climate change on snow phenology, with implications for water resources and ecosystems.

Evapotranspiration (ET) is a fundamental process linking the energy, water, and carbon cycles in terrestrial ecosystems. In dryland regions like Northwest China, where water availability strongly constrains ecosystem functioning, understanding the spatiotemporal dynamics of actual evapotranspiration (ETa) and its controlling mechanisms is critical for ecohydrological assessments under climate change. However, these regions face persistent challenges due to sparse vegetation, heterogeneous soils, and limited observations, which impede accurate regional-scale ETa quantification and attribution analysis. This study investigates the spatiotemporal patterns and controlling factors of ETa in Northwest China from 2001 to 2024 using the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model optimized with remote sensing and flux observations. The model was optimized using 16 flux towers, achieving an R2 of 0.74 and enabling reliable regional ETa estimation under data-scarce conditions. The optimized model produced multi-year average ETa of 292.2 mm and an increasing trend of 0.43 mm yr-1. ETa exhibited a clear spatial gradient, higher in the east, lower in the west, corresponding to vegetation and water availability. Importantly, standardized ridge regression revealed that environmental factors explained 83.4 % of ETa variability, with relative humidity alone contributing the largest share (33.6 %). Vegetation dynamics accounted for 16.6 %, primarily associated with farmland expansion and afforestation. This findings offer a robust framework for disentangling ETa drivers in data-scarce drylands and underscore the dominant role of both atmospheric humidity and land cover change in shaping regional evapotranspiration patterns. This study delivers valuable insights for sustainable water and land resource management under climate change.

Accurately estimating aboveground forest biomass is essential for understanding regional and global carbon cycles. In subalpine forest regions, complex topography and challenging forest inventories introduce significant uncertainties in estimating structural parameters and aboveground biomass. To generate continuous biomass maps over large-scale mountainous forests, it is necessary to extrapolate biomass from sample plots using appropriate models and remote sensing data. This study utilized ground survey data, unmanned aerial vehicle (UAV) -Light detection and ranging (LiDAR) data, Global Ecosystem Dynamics Investigation (GEDI) data, Sentinel-1, and Landsat 8 Operational Land Imager (OLI) imagery to estimate forest biomass in Qilian Mountain National Park. Key findings include the following: First, UAV-LiDAR-derived parameters, when integrated with a random forest model, effectively predict forest biomass at the sample plot level. Second, combining GEDI data with UAV-LiDAR estimates provides the accurate biomass predictions at footprint points. Third, by extrapolating biomass from discrete GEDI footprints and incorporating variables from Sentinel-1 and Landsat 8 OLI, a continuous, high-accuracy forest biomass map for the entire Qilian Mountain National Park was generated (R2 = 0.66, root-mean-square error = 19.08 Mg/ha, and relative root-mean-square error = 11.04%). This study successfully scaled biomass estimates from plots to a regional level, enhancing biomass estimation accuracy in mountainous regions by leveraging complementary data sources.

The seasonal freeze-thaw (FT) cycles of the permafrost active layer and seasonally frozen ground have significant impacts on ecosystems. Advances in remote sensing have enabled global monitoring of seasonal surface FT cycles. However, previous studies have generally overlooked the impact of remote sensing dataset accuracy on multiple FT parameters and have failed to sufficiently explore the changes in thawing and freezing intervals (TI/FI) in the Northern Hemisphere. This study evaluates the FT-ESDR remote sensing product using site-observed soil temperature data and provides a comprehensive analysis of surface FT characteristics in the Northern Hemisphere, with a focus on freezing and thawing intervals based on FT parameters calculated using a 15-day moving window method. The results indicate that the FT status calculated from site data shows strong agreement with remote sensing data products, with this agreement varying by season, land cover type, air temperature, precipitation. Compared to the FT parameters calculated from site data, FT-ESDR dataset underestimate several FT parameters. Combined analysis of site observation data and remote sensing data products indicates the presence of permafrost degradation, characterized by a longer thawed duration (TD), shorter frozen duration (FD), earlier start of thawing (SOT) and end of thawing (EOT), delayed start of freezing (SOF) and end of freezing (EOF), and reduced FT frequency (FTCF1/FTCF2) for both thawing and freezing seasons. The variations in FT parameters differ based on vegetation and permafrost types. The thawing interval (TI) and freezing interval (FI) show a shortening trend at most sites, indicating an acceleration of the surface thawing and freezing processes. The shortening of TI/FI during thawing/freezing is associated with increased/decreased air temperatures during the corresponding periods. Notably, the variation characteristics of sites in agricultural land differ from those in other land cover types. Overall, our results highlight the underestimation of FT parameters derived from the FT-ESDR dataset and their sensitivity to environmental factors. It also emphasizes the acceleration of surface thawing and freezing processes, which are influenced by air temperature.

Under the ongoing trend of climate warming and increasing humidity on the Qinghai–Tibet Plateau, the Three River Source Region (TRSR) has shown strong sensitivity to global climate change. Its vegetation change is particularly worthy of attention and research. The Normalized Difference Vegetation Index (NDVI) is a key indicator for assessing the growth status of vegetation. However, the insufficiency of existing NDVI datasets in terms of spatiotemporal continuity has limited the accuracy of long-term vegetation change studies. This study proposed a machine learning-based downscaling framework that integrates the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI and the Global Inventory Monitoring and Modeling System (GIMMS) NDVI data to reconstruct a long-term, high-resolution NDVI dataset. Unlike conventional statistical fusion approaches, the proposed framework employs machine learning-based nonlinear relationships to generate long-term, high-resolution NDVI data. Three machine learning algorithms—Random Forest (RF), LightGBM, and CatBoost—were evaluated. Their performance was validated using the MODIS NDVI as reference, with the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (R) as evaluation metrics. Based on model comparison, the CatBoost model was identified as the optimal algorithm for spatiotemporal data fusion (R2 = 0.9014, RMSE = 0.0674, MAE = 0.0445), significantly outperforming RF and LightGBM models and demonstrating stronger capability for NDVI spatiotemporal reconstruction. Using this model, a long-term, 1 km monthly GIMMS-MODIS NDVI dataset from 1982 to 2014 was successfully reconstructed. On the basis of this dataset, the spatiotemporal variation characteristics of vegetation in the TRSR from 1982 to 2014 were systematically analyzed. The research results show that: (1) The constructed long-series high-resolution NDVI dataset has a high consistency with MODIS NDVI data; (2) From 1982 to 2014, the NDVI in the TRSR showed an increasing trend, with an average growth rate of 0.0020/10a (p < 0.05). NDVI showed obvious spatial heterogeneity, characterized by a decreasing gradient from southeast to northwest. (3) The Yellow River source exhibited the most evident vegetation recovery, the Yangtze River Source area showed a moderate improvement, whereas the Lancang River Source area displayed little noticeable change. (4) Broad-leaved forests experienced the most significant growth, while cultivated vegetation displayed a marked tendency toward degradation. This study provides both a high-accuracy long-term NDVI product for the TRSR and a methodological foundation for advancing vegetation dynamics research in other high-altitude regions.

Carbon fluxes are essential indicators assessing vegetation carbon cycle functions. However, the extent and mechanisms by which climate change and human activities influence the spatiotemporal dynamics of carbon fluxes in arid oasis and non-oasis area remains unclear. Here, we assessed and predicted the future effects of climate change and human activities on carbon fluxes in the Hexi Corridor. The results showed that the annual average gross primary productivity (GPP), net ecosystem productivity (NEP), and ecosystem respiration (Reco) in the Hexi Corridor oasis increased by 263.91 g C·m−2·yr−1, 118.45 g C·m−2·yr−1 and 122.46 g C·m−2·yr−1, respectively, due to the expansion of the oasis area by 3424.84 km2 caused by human activities from 2000 to 2022. Both oasis and non-oasis arid ecosystems in the Hexi Corridor acted as carbon sinks. Compared to the non-oasis area, the carbon fluxes contributions of oasis area increased, ranging from 10.21

The spatiotemporal heterogeneity of urban vegetation phenology (UVP) has intensified due to coupled urban expansion and climate change, yet the systematic understanding of UVP responses along urban-rural gradients across diverse climatic contexts and urban expansion remains limited. Therefore, this study selected 31 Chinese cities across diverse climate zones and city sizes using multi-source remote sensing data (2001-2020) to quantify the synergistic effects of urban expansion and climate change on urban-rural UVP differences (ΔUVP). First, UVP in China exhibited advanced start of growing season (SOS), delayed end of growing season (EOS), and extended length of growing season (GSL), with more pronounced shifts in southeastern regions compared to northwestern zones. Furthermore, the magnitudes of SOS advancement, EOS delay, and GSL extension gradually decreased along the urban-rural gradient. ΔUVP in large cities was smaller than that in other city sizes, whereas arid and semi-arid zones exhibited significantly greater ΔUVP than humid and semi-humid zones. Second, ΔSOS, ΔEOS, and ΔGSL demonstrated predominantly negative, positive, and positive correlations with both urban heat island intensity (ΔLST) and urban expansion intensity (ΔISP), respectively. Medium cities demonstrated the maximum response magnitudes of ΔUVP to ΔLST compared to other city sizes, whereas small towns demonstrated the maximum response magnitudes of ΔUVP to ΔISP. The response magnitudes of ΔUVP to both ΔLST and ΔISP were significantly greater in arid and semi-arid zones than in humid and semi-humid zones. Finally, principal component analysis confirmed that urban factors predominantly drive ΔUVP variations, with ΔISP identified as the primary regulatory factor. These findings provide critical insights into urban vegetation dynamics under rapid expansion and climate change.