Increased frequency and intensity of droughts under climate change will have a significant impact on land surface phenology, however, the drought-phenology interactions that are associated with complex temporal effects are not well understood. This study examined the response of land surface phenology to drought cumulative and time-lag effects by using the standardized precipitation and evapotranspiration index (SPEI) and explored the influence of hydrothermal and plant physiological factors on the phenology-drought relationship. We used the maximum correlation (r_max-cml) between phenology and cumulative SPEI (1- to 12-month) to determine the cumulative effect. The maximum correlation (r_max-lag) between phenology and lagged SPEI (1-month) was utilized to analyze the time-lag effect. Overall, the cumulative effect affected 25.36% of the vegetated area at the start of the growing season (SOS), 26.43% of the end of the growing season (EOS), and 26.57% of the length of the growing season (LOS). SOS was negatively affected by long-term SPEI, whereas EOS and LOS had positive correlations with short-term SPEI. The r_max-cml for shrubland was the largest, and the SOS and LOS response time scales of the forest were the shortest. The r_max-cml was larger in arid and semi-arid (AR and SAR) than in humid and semi-humid (HU and SHU). Meanwhile, the response time scales were longer in HU and SHU than in AR and SAR. The time-lag effect had a larger area of impact on land surface phenology than the cumulative effect, the areas were 46.12%, 47.93%, and 50.45% for SOS, EOS, and LOS, respectively, and the lagged time scales were longer. The phenology-SPEI correlation was dominantly driven by hydrological conditions, and the time scales were mainly affected by thermal factors. Moreover, the onset of phenology and the growth/senescence rate of plants influenced the relationship, suggesting that hydrothermal conditions may shift the time of phenology by regulating the growth/senescence rate and may thus modulate the phenology–drought interaction.

Selecting optimal revegetation patterns and filtering priority areas can improve the effectiveness and efficiency of revegetation planning, particularly in areas with severe vegetation damage. However, few people include optimal revegetation patterns and priority restoration areas into revegetation plans. The Near-Nature restoration pays attention to “based on nature” ideas, guiding the degraded ecosystems to reorganize and achieving sustainable restoration through self-regulation. In this study, we conducted a field survey of the native vegetation communities in the Yanhe River catchment, and the data obtained were used to construct the potential distribution suitability of the habitat and screen the priority areas through the combination of MaxEnt and prioritizr models. We drew a heat map of species richness by simulating the potential distribution of 60 native species. The results showed that the potentially suitable habitats for forest cover were distributed in the southern part of the Yanhe River catchment; the potentially suitable habitats for herbaceous plant species were located in the center and the northwest parts of the study area; the potentially suitable habitats for shrub plant species in this area were larger than that of the forest, and herbaceous plants species were distributed in many zones of the study area. This study demonstrated that shrubs and herbaceous plant species in parts of the Loess Plateau should be considered as the pioneer plants of revegetation in future revegetation plans. Moreover, we also mapped the priority area of the Near-Nature restoration based on the richness of the potential native species. The procedure followed in this study could provide guidance for revegetation planning and manual management in the regions where vegetation damage occurs.

Soil‐atmosphere compound drought (SACD) significantly impacts vegetation, with effects expected to intensify under global warming. However, the dynamic coupling relationship between vegetation and SACD considering the optimal time lag remains unclear. To address this, we first employed copulas to develop a SACD index at temporal scales ranging from 1 to 24 months. Based on this index, the coupling relationship represented by the maximum correlation coefficient ( R _max ) and the optimal time lag ( T _opt ) between the Leaf Area Index and the SACD was examined. Furthermore, the coupling degree between the two was explored both temporally and spatially. The results revealed a significant nonlinear trend in both R _max and T _opt , with turning points identified using the Ensemble Empirical Mode Decomposition occurring between 2010–2014 and 2011–2015, respectively. Additionally, it was found that the temporal coupling degree was strong, while the spatial coupling was initially weaker but showed an increasing trend, particularly in water‐limited regions. Land surface model simulations indicated that CO ₂ was the dominant driver of the vegetation‐drought coupling relationship and degree. Machine learning and SHapley Additive Explanations underscored the critical importance of meteorological variables, with radiation, precipitation and temperature being identified as the most influential meteorological factors. Finally, based on the Peter‐Clark Momentary Conditional Independence Plus, the complex causal relationship network between meteorological factors and the vegetation‐drought coupling was revealed. Our study highlights the importance of examining the dynamic coupling between vegetation and SACD, with the findings providing valuable insights to support ecosystem sustainability under climate change.

Vegetation net primary productivity (NPP) serves as a crucial and intuitive indicator for assessing ecosystem health. However, the nonlinear dynamics and influencing factors operating at various time scales are not yet fully understood. Here, the ensemble empirical mode decomposition (EEMD) method was used to analyze the spatiotemporal patterns of NPP and its association with hydrothermal factors and anthropogenic activities across different temporal scales for the Yellow River Basin (YRB) from 2000 to 2020. The results indicate that: (1) the annual average NPP was 236.37 g C/m² in the YRB and increased at rates of 4.64 g C/m²/a¹ (R² = 0.86, p < 0.01) during 2000 to 2020. Spatially, nonlinear analysis indicates that 72.77% of the study area exhibits a predominantly increasing trend in NPP, while 25.17% exhibits a reversing trend. (2) On a 3-year time scale, warming has resulted in an increase in NPP in the majority of areas of the study area (69.49%). As the time scale widens, the response of vegetation to climate change becomes more prominent; especially under the long-term trend, the percentage areas of the correlation between vegetation and precipitation and temperature increased with significance, reaching 48.21% and 11.57%, respectively. (3) Through comprehensive time analysis and multivariate regression analysis, it was confirmed that both human activities and climate factors had comparable impacts on vegetation growth. Among different vegetation types, climate was still the main factor affecting grassland NPP, and only 15.74% of grassland was affected by human activities. For shrubland, forest, and farmland, human activity was a dominating factor for vegetation NPP change. There are still few studies on vegetation change using nonlinear methods in the Yellow River Basin, and most studies have not considered the effect of time scale on vegetation evolution. The findings highlight the significance of multi-time scale analysis in understanding the vegetation dynamics and providing scientific guidance for future vegetation restoration and conservation efforts.

Uncovering the trade-offs and synergy relationship of multiple ecosystem services (ESs) is important for scientific ecosystem management and the improvement of ecological service functions. In this study, we investigated the spatiotemporal changes of four typical ES types (i.e., water yield (WY), carbon storage (CS), soil conservation (SC), and habitat quality (HQ)) from 2001 to 2020 in the Han River Basin (HRB). Meanwhile, the trade-offs and synergies between paired ESs and the socioecological drivers of these ESs were further explored. The results showed that grassland, cropland, and bare land decreased by 12,141.3 km², 624.09 km², and 22.1 km² during the study period, respectively, which can be attributed to their conversion to forests in the HRB. Temporally, the WY, CS, and SC all showed a continuously increasing trend. Spatially, WY and HQ exhibited bipolar clustering characteristics, with WY exhibiting low-value clustering in the upstream and high-value clustering in the downstream, while CS showed the clustering characteristics of a scattered distribution of cold and hot spots from 2001 to 2020. The spatial patterns of aggregation locations in CS and HQ were relatively similar, with clusters of higher ES values mainly distributed in the western and central regions and clusters of lower ES values mainly located in the eastern and southeastern regions, while the aggregation of WY was spatially concentrated. Overall, the CS showed a significant positive correlation with HQ, but a significant negative correlation with WY. Spatially, WY and HQ, CS, and SC showed a substantial trade-off relationship in the northwest and southeast parts of the study area, while HQ, CS, and SC mainly exhibited a synergistic relationship in most parts of the study area. Slope and temperature had high influencing factor coefficients on multiple ESs; the mixed effect of terrain and natural factors was significantly greater than the impact of a single factor on ESs, and terrain factors played an essential role in the changes in ESs. The findings can provide technical and theoretical support for integrated scientific ecosystem management and sustainable development at the local scale.