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.

Compound droughts and heatwaves (CDHWs) are likely to cause more severe natural disasters than a single extreme event, and they have been exacerbated by rapid global warming. Based on high-resolution grid data, this study combines the daily-scale ERA5-Land dataset and the monthly-scale SPEI dataset with multiple indicators to analyze CDHWs. We calculated and analyzed the temporal and spatial modal distribution of CDHWs in Central Asia from 1981 to 2018, and in this paper, we discuss the sequence relationship between drought events, heatwave events, and CDHWs. The results show that the number of CDHWs in the study region have increased over time and expanded in terms of area, especially in eastern and southwestern Central Asia. The tsum (total frequency of CDHWs) was 0.5 times higher than the total heatwave frequency and it increased at a rate of 0.17/yr. The maximum duration of tmax (maximum duration of CDHWs in days) was 17 days. Furthermore, the occurrence rate of tmax was 96.67%, and the AH (CDHWs’ accumulated heat) had a rate of 97.78%, which, upon examination of the spatial trend pattern, accounted for the largest increase in terms of area. We also found that the TAH (CDHWs’ average temperature anomalies, SPEI < −0.5) shows obvious seasonality, with the increases in winter and spring being significantly greater than the increases in summer and autumn. The intensity of the CDHWs was stronger than that of a single extreme event, the temperature anomaly was higher than the average of 0.4–0.8 °C, and there was a north–south spatial pattern across the study region. In eastern and northwestern Central Asia, the AH and heatwaves (SPEI < −0.5) increased by 15–30 times per year on average. During the transition from the base period to the reference period, CDHWs increased by 25%, and the number of dry days prior to the CDHWs decreased by 7.35 days. The conclusion of our study can provide a theoretical basis for coping with climate change in arid zones.

Vegetation phenology reflects the response mechanisms in ecology and climate change, so it is important that the parameters used to study vegetation phenology are accurate. Previous studies mainly focused on phenological changes. However, because the extraction methods used in those investigations led to inconsistencies in setting vegetation phenological parameters, a more accurate approach needs to be developed. To resolve this issue, we select five methods to extract the start of the growing season (SOS) and the end of the growing season (EOS) from the normalised difference vegetation index (NDVI3g) data. The five chosen methods are the second-order derivative method (Method 1), the first-order derivative method (Method 2), the 0.2 dynamic threshold method (Method 3), the 0.5 dynamic threshold method (Method 4), and the fixed threshold method (Method 5). Our study area is the Northern Hemisphere (above 30°N), and our study period is 1982 to 2015. After applying the five methods, we evaluate the consistency of the vegetation phenological parameters. The results show that (1) regardless of the method used, the average changes in phenological parameters are consistent; however, the SOS and EOS under Methods 1, 3 and 5 are up to 30 days earlier than those under Methods 2 and 4. (2) Under all five methods, the SOS trend mainly shows an advance, but the trend is substantially higher under Methods 1, 3 and 4 than under Methods 2 and 5 from 45°N to 60°N. The distribution of the EOS trend under different methods is consistent. (3) Under the tested extraction methods, the SOS trends of evergreen needleleaf forests (ENF) and mixed forests (MF) have significant differences (p < 0.05), whereas, the EOS trend for different vegetation types is consistent. (4) By analysing the consistency of the phenological parameters between remote sensing data and ground data under different methods, we now know that Methods 3 and 4 are the most accurate for extracting the SOS and EOS, respectively. The above results can provide a reference for the accurate extraction of phenological parameters above 30°N.

Arid salt marsh ecosystems endure chronic water scarcity and high salinity stress, with the stability of their functions inextricably linked to the pivotal role of the rhizosphere microenvironment of halophytes. This study focused on three typical halophytes (Kalidium cuspidatum, Nitraria tangutorum, Reaumuria soongarica) in the Jiantan wetland, and deeply explore how these halophytes differently regulate the soil microenvironment through the rhizosphere effect. The results showed that the rhizosphere soil of Kalidium cuspidatum had higher pH, Na⁺, and K⁺ contents, while the rhizosphere soil of R. soongarica had higher total carbon, soil organic carbon, alkali-hydrolyzable nitrogen, and microbial biomass. Microbial community analysis revealed that rhizosphere soil of fungal diversity was significantly higher in K. cuspidatum than in R. soongarica, with distinct differences in bacterial and fungal community structures. These differences were closely associated with factors such as Na⁺, Olsen phosphorus, microbial biomass carbon and alkali-hydrolyzable nitrogen. Among the dominant phyla, Proteobacteria and Ascomycota predominate, with Desulfobacterota and Mortierellomycota exhibiting the highest explanatory power (>48%) for physicochemical property variations. The microbial network of rhizosphere soil of R. soongarica has the highest complexity (with 633 nodes and 3300 edges), but the proportion of positive correlation edges was the lowest (21.58%). Structural equation modeling indicates that soil physical properties indirectly influence network complexity by negatively regulating chemical properties and microbial biomass, while microbial diversity had a direct positive effect on dominant phylum composition and network complexity. This study elucidated the differentiated adaptive strategies of rhizosphere microenvironment-microbe interactions in halophytes, providing a theoretical basis for wetland ecological restoration.

Arid ecosystems are highly sensitive to climate change, yet it remains unclear whether regional greening is accompanied by consistent changes in vegetation greenness throughout the growing season. Using SPOT/VEGETATION NDVI and meteorological data from 1999 to 2018, we applied a month-to-month NDVI difference metric (V _NDVI ) to characterize intra-seasonal variation in satellite-observed vegetation greenness and to examine its relationships with concurrent and antecedent climatic conditions across arid Northwest China. May–October was defined as the common analytical window for regional comparison, with May–July representing the early-to-peak growing season and August–October representing the late growing season. Although NDVI exhibited positive long-term trends in most months, VNDVI trends showed pronounced intra-seasonal asymmetry. VNDVI increased significantly in June and July but decreased significantly in October, indicating that regional greening was accompanied by a redistribution of month-to-month vegetation greenness changes rather than a uniform enhancement throughout the growing season. These trends also exhibited marked spatial heterogeneity. Negative V _NDVI trends in May were concentrated in the Altai Mountains and the western Tianshan Mountains, whereas positive trends in June and July were most evident in the central Tianshan Mountains and along the northern margin of the Tarim Basin. The relationships between V _NDVI and climatic variables varied among months and regions. Precipitation was generally positively associated with month-to-month vegetation greenness changes, whereas temperature and radiation showed negative associations in several months and regions. Hierarchical partitioning further revealed that the relative independent contributions of climatic variables to NDVI variability differed among vegetation types, with temperature accounting for a particularly large proportion of the explained variation in croplands during the early growing season.