The abrupt COVID-19 lockdown in early 2020 offered a unique natural experiment to examine vegetation productivity responses to sudden declines in human activity. Although vegetation often responds to environmental changes with time lags, how such lags operate under short-term, intensive disturbances remains unclear. This study combined multi-source environmental data with an interpretable machine learning framework (XGBoost-SHAP) to analyze spatiotemporal variations in net primary productivity (NPP) across the Beijing-Tianjin-Hebei region during the strict lockdown (March–May) and recovery (June–August) periods, using 2017–2019 as a baseline. Results indicate that: (1) NPP showed a significant increase during lockdown, with 88.4% of pixels showing positive changes, especially in central urban areas. During recovery, vegetation responses weakened (65.31% positive) and became more spatially heterogeneous. (2) Integrating lagged environmental variables improved model performance (R² increased by an average of 0.071). SHAP analysis identified climatic factors (temperature, precipitation, radiation) as dominant drivers of NPP, while aerosol optical depth (AOD) and nighttime light (NTL) had minimal influence and weak lagged effects. Importantly, under lockdown, vegetation exhibited stronger immediate responses to concurrent temperature, precipitation, and radiation (SHAP contribution increased by approximately 7.05% compared to the baseline), whereas lagged effects seen in baseline conditions were substantially reduced. Compared to the lockdown period, anthropogenic disturbances during the recovery phase showed a direct weakening of their impact (decreasing by 6.01%). However, the air quality improvements resulting from the spring lockdown exhibited a significant cross-seasonal lag effect. (3) Spatially, NPP response times showed an “urban-immediate, mountainous-delayed” pattern, reflecting both the ecological memory of mountain systems and the rapid adjustment capacity of urban vegetation. These findings demonstrate that short-term removal of anthropogenic disturbances shifted vegetation responses toward greater immediacy and sensitivity to environmental conditions. This offers new insights into a “green window period” for ecological management and supports evidence-based, adaptive regional climate and ecosystem policies.

Coastal wetlands, situated at the critical land–sea ecotone, play a vital role in sustaining ecological balance and supporting human activities. Currently, these ecosystems face dual stresses from climate change and intensified anthropogenic activities, making the quantitative assessment of ecosystem functions—represented by Gross Primary Productivity (GPP)—essential for their protection and management. However, a knowledge gap remains regarding coastal–urban complex ecosystems, and existing studies on coastal wetlands often overlook macro-environmental drivers beyond sea-level rise. This study leveraged the MOD17A2H V006 dataset to generate a 500 m GPP product for Zhanjiang City. We analyzed the spatiotemporal dynamics of GPP, utilized land use data to examine the evolution of coastal wetlands, and employed the Geodetector model to quantify the contributions of various factors to GPP in Zhanjiang and its coastal wetlands. The results indicate that: (1) GPP in Zhanjiang exhibited an overall steady upward trend, increasing at an average rate of $13.8\mathrm{g}\mathrm{C}·{\mathrm{m}}^{-2}·{\mathrm{yr}}^{-1}$. However, it displayed strong spatial heterogeneity, characterized by higher values in the southwest and lower values in the northern and coastal regions. (2) The land use pattern in Zhanjiang underwent significant transformations over the past two decades. Cropland and impervious surfaces expanded markedly, increasing by 194.6 km² and 290.42 km², respectively, while coastal wetland areas showed a continuous decline, with degraded and newly formed areas of 101.5 km² and 42 km², respectively. (3) The Geodetector results revealed that the q-value of Nighttime Light (NTL) increased from negligible values to over 0.1, emerging as a dominant driving factor. Although the driving force of anthropogenic activity factors on Zhanjiang and its coastal wetlands has steadily increased, natural factors currently remain the dominant forces. These findings unravel the driving mechanisms of natural and anthropogenic factors on GPP in Zhanjiang, providing valuable scientific evidence for the sustainable development of coastal ecosystems.

Spring vegetation phenology is highly sensitive to climate change; however, climate drivers and their threshold responses at the urban scale remain insufficiently and systematically quantified. Focusing on 31 provincial capitals and municipalities in mainland China, this study integrated MODIS MCD12Q2-derived start-of-season (SOS) for spring green-up and TerraClimate climate data (2001–2023) at a 500 m grid resolution. SOS trends were characterized using the Mann–Kendall test and the Theil–Sen slope estimator. Building on these trend metrics, we developed an XGBoost–SHAP framework using the interannual rate of temperature change (tem_slope) and the interannual rate of precipitation change (pre_slope) as input features, to quantify the nonlinear contributions of climate-change rates to SOS trends and to identify key thresholds. Results indicate that the multi-year mean SOS across China’s provincial capitals and municipalities is primarily distributed between approximately DOY 74 and 138, exhibiting a clear spatial pattern of earlier green-up in the south, later green-up in the north, and delayed green-up on plateaus, with pronounced shifts in distribution centers and dispersion among climatic zones and cities. At the city level, the mean SOS trend shows an overall advancing rate of 0.81 d·year⁻¹ (i.e., the average of city-mean Sen slopes across the 31 cities). Pixel-level trend analyses show that advancing and delaying trends commonly coexist within most cities; among pixels with significant or marginally significant SOS trends identified by the Mann–Kendall test (MK p < 0.10) across all cities, advancing and delaying SOS pixels account for 75.02% and 24.98%, respectively. At the city scale, the proportions of advancing versus delaying pixels vary markedly among cities, forming directional structures characterized by advance-dominant, delay-dominant, or bidirectional coexistence patterns. SHAP dependence relationships further reveal that the effects of tem_slope and pre_slope on SOS trends are generally nonlinear and piecewise, with substantial heterogeneity across climate zones and cities. The identified tipping points and associated sensitive ranges collectively delineate spatially differentiated climate-sensitive intervals, which define the nonlinear response boundaries of spring SOS to sustained warming and precipitation changes. This study provides quantitative evidence for regional differences in urban spring phenological responses to climate change across major Chinese cities and offers a methodological reference for identifying actionable climate thresholds in urban greening design and climate-adaptive management.

Vegetation restoration has significantly altered terrestrial ecosystem dynamics in the Three-North Shelterbelt Forest region (TNSFR), but its impacts on groundwater storage (GWS) remain poorly quantified, particularly as forest stand structure is rarely integrated. This study integrated terrestrial water storage data, land surface model simulation results, and in-situ groundwater observations to assess the spatiotemporal GWS variations. Furthermore, combining meteorological data, forest inventory data, yearbook statistics, and Normalized Difference Vegetation Index (NDVI) data explores how climate and vegetation restoration affect GWS changes. Results show that vegetation restoration in the TNSFR has exhibited a widespread upward trend, with NDVI increasing at an average rate of 0.015 decade⁻¹ over the past two decades. Concurrently, GWS has declined significantly with a mean rate of -5.47 mm yr⁻¹, primarily concentrated in regions with substantial increases in forest coverage. In groundwater-dependent ecosystems, monocultures show higher water consumption than mixed forests, and groundwater decline in mature coniferous forests is linked to high-water-consumption spruce species. Structural equation modeling confirms vegetation restoration as the leading anthropogenic driver of GWS decline in the TNSFR. This study highlights the need for tailored ecological strategies for coordinating ecological recovery with sustainable water management in dryland areas.

Vegetation restoration has significantly altered terrestrial ecosystem dynamics in the Three-North Shelterbelt Forest region (TNSFR), but its impacts on groundwater storage (GWS) remain poorly quantified, particularly as forest stand structure is rarely integrated. This study integrated terrestrial water storage data, land surface model simulation results, and in-situ groundwater observations to assess the spatiotemporal GWS variations. Furthermore, combining meteorological data, forest inventory data, yearbook statistics, and Normalized Difference Vegetation Index (NDVI) data explores how climate and vegetation restoration affect GWS changes. Results show that vegetation restoration in the TNSFR has exhibited a widespread upward trend, with NDVI increasing at an average rate of 0.015 decade⁻¹ over the past two decades. Concurrently, GWS has declined significantly with a mean rate of -5.47 mm yr⁻¹, primarily concentrated in regions with substantial increases in forest coverage. In groundwater-dependent ecosystems, monocultures show higher water consumption than mixed forests, and groundwater decline in mature coniferous forests is linked to high-water-consumption spruce species. Structural equation modeling confirms vegetation restoration as the leading anthropogenic driver of GWS decline in the TNSFR. This study highlights the need for tailored ecological strategies for coordinating ecological recovery with sustainable water management in dryland areas.

Vegetation restoration has significantly altered terrestrial ecosystem dynamics in the Three-North Shelterbelt Forest region (TNSFR), but its impacts on groundwater storage (GWS) remain poorly quantified, particularly as forest stand structure is rarely integrated. This study integrated terrestrial water storage data, land surface model simulation results, and in-situ groundwater observations to assess the spatiotemporal GWS variations. Furthermore, combining meteorological data, forest inventory data, yearbook statistics, and Normalized Difference Vegetation Index (NDVI) data explores how climate and vegetation restoration affect GWS changes. Results show that vegetation restoration in the TNSFR has exhibited a widespread upward trend, with NDVI increasing at an average rate of 0.015 decade⁻¹ over the past two decades. Concurrently, GWS has declined significantly with a mean rate of -5.47 mm yr⁻¹, primarily concentrated in regions with substantial increases in forest coverage. In groundwater-dependent ecosystems, monocultures show higher water consumption than mixed forests, and groundwater decline in mature coniferous forests is linked to high-water-consumption spruce species. Structural equation modeling confirms vegetation restoration as the leading anthropogenic driver of GWS decline in the TNSFR. This study highlights the need for tailored ecological strategies for coordinating ecological recovery with sustainable water management in dryland areas.