Global climate changes have increased the imbalance of water resources, especially in northern China, which comprises typical arid and semiarid regions. Large-scale afforestation has been implemented over the past three decades in northern China. The ecosystem water use efficiency (WUE) connects the carbon cycle and water cycle of the terrestrial ecosystems and is defined as the ratio of the gross primary productivity (GPP) to the evapotranspiration. However, there are still an insufficient number of studies on the impact of the afforestation on the WUE. In this study, we applied the random forest (RF) model to explore the impacts of climate and nonclimate factors on the WUE in northern China. The results showed that in areas with high precipitation, the forests had the highest WUE, while in the arid areas, the croplands had the highest WUE. Of the total area, 44.34% showed a significant increase, and 5.89% showed a significant decrease in the WUE from 1982–2015 in northern China. The main driving factors for the changes in the WUE were climate factors, including the precipitation, temperature and solar radiation, which contributed to approximately 84% of the WUE trends, while human activities, such as afforestation, contributed to approximately 16% of the WUE trends. Overall, although the climate had a larger impact on the WUE dynamics than the human activities, our results suggested that the impacts of the afforestation programs on forest carbon and water cycles should be considered in the context of climate change.

The global land surface cover is undergoing extensive changes in the context of global change, especially in the Loess Plateau, where ecological restoration policies have been vigorously implemented since 2000. Evaluating the impact of these policies on land cover is of great significance for regional sustainable development. Nonetheless, there are few quantitative assessment studies of the impact of ecological restoration policies on land use and land cover change (LULCC). In this study, a relative contribution conceptual model (RCCM) was used to explore the contribution of the policies to LULCC under the influence of natural background change, which was based on the Markov chain and the future land use simulation (FLUS) model. The results show that LULCC is influenced by ecological restoration policies and the natural environment, of which the policies contribute about 72.37% and natural change contribute about 27.63%. Ecological restoration policies have a profound impact on LULCC, changing the original direction of LULCC greatly. Additionally, these policies regulate the pattern of LULCC by controlling the amount of cropland as a rebalanced leverage. These findings provide useful information for facilitating sustainable ecological development in the Loess Plateau and theoretically supporting environmental decision-making.

Worldwide urbanization leads to ecological changes around urban areas. However, few studies have quantitatively investigated the impacts of urbanization on vegetation coverage so far. As an important indicator measuring regional environment change, fractional vegetation cover (FVC) is widely used to analyze changes in vegetation in urban areas. In this study, on the basis of a partial derivative model, we quantified the effect of temperature, precipitation, radiation, and urbanization represented as nighttime light on vegetation coverage changes in the Beijing–Tianjin–Hebei (BTH) region during its period of rapid resident population growth from 2001 to 2011. The results showed that (1) the FVC of the BTH region varied from 0.20 to 0.26, with significant spatial heterogeneity. The FVC increased in small cities such as Cangzhou and in the Taihang Mountains, while it decreased in megacities with populations greater than 1 million, such as Beijing and Zhangjiakou Bashang. (2) The BTH region experienced rapid urbanization, with the area of artificial surface increasing by 18.42%. From the urban core area to the fringe area, the urbanization intensity decreased, but the urbanization rate increased. (3) Urbanization and precipitation had the greatest effect on FVC changes. Urbanization dominated the FVC changes in the expanded area, while precipitation had the greatest impacts on the FVC changes in the core area. For future studies on the major influencing factors of FVC changes, quantitative analysis of the contribution of urbanization to FVC changes in urban regions is crucial and will provide scientific perspectives for sustainable urban planning.

We have integrated the observational capability of satellite remote sensing with plot-scale tree-ring data to upscale the evaluation of forest responses to drought. Satellite data, such as the normalized difference vegetation index (NDVI), can provide a spatially continuous measure with limited temporal coverage, while tree-ring width index (RWI) provides an accurate assessment with a much longer time series at local scales. Here, we explored the relationship between RWI and NDVI of three dominant species in the Southwestern United States (SWUS) and predicted RWI spatial distribution from 2001 to 2017 based on Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km resolution NDVI data with stringent quality control. We detected the optimum time windows (around June–August) during which the RWI and NDVI were most closely correlated for each species, when the canopy growth had the greatest effect on growth of tree trunks. Then, using our upscaling algorithm of NDVI-based RWI, we were able to detect the significant impact of droughts in 2002 and in 2011–2014, which supported the validity of this algorithm in quantifying forest response to drought on a large scale.

Water availability is one of the most important resources for forest growth. However, due to its complex spatio-temporal relationship with other climatic factors (e.g., temperature and solar radiation), it paradoxically shows both positive and negative correlations (i.e., dual roles) with forest vigor for unknown reasons. In this study, a multiperspective analysis that combined the deficit of the Normalized Difference Vegetation Index (dNDVI) and multitimescale Standardized Precipitation Evapotranspiration Index (SPEI) was conducted for the forests in China, from which their correlation strengths and directions (positive or negative) were linked with spatio-temporal patterns of environmental temperature (T) and water balance (WB) (i.e., precipitation minus potential evapotranspiration). In this way, the reasons for the inconsistent roles of water were revealed. The results showed that the roles of water availability greatly depended on T, WB, and seasonality (i.e., growing or pregrowing season) for both planted and natural forests. Specifically, a negative role of water availability mainly occurred in regions of T below its specific threshold (i.e., T ≤ T_threshold) during the pregrowing season. In contrast, a positive role was mainly observed in warm environments (T > T_threshold) during the pregrowing season and in dry environments where WB was below its specific threshold (i.e., WB ≤ WB_threshold) during the growing season. The values of T_threshold and WB_threshold were related to the vegetation type, with T_threshold ranging from 1.3 to 4.7 °C and WB_threshold ranging from 129.1 to 238.8 mm/month, respectively. Our study revealed that the values of T_threshold and WB_threshold for a specific forest were stable, and did not change with the SPEI time-scales. Our results reveal the dual roles of water availability in forest vigor and highlight the importance of environmental climate and seasonality, which jointly affect the roles of water availability in forest vigor. These should be considered when monitoring and/or predicting the impacts of drought on forests in the future.

The Qinghai–Tibet Plateau (QTP) plays a crucial role in the terrestrial carbon cycle, but the gross primary productivity (GPP) estimates for the region remain highly uncertain due to limited flux observations and modeling challenges. Here, we integrated 65.2 site years of eddy covariance data from 19 flux sites with multi-source remote sensing observations to develop a data driven GPP model for the QTP. Eleven machine learning algorithms from two automated machine learning (AutoML) platforms, H2O AutoML and FLAML, were evaluated to construct an ensemble model named AutoML. The model showed strong performance at site-level across alpine meadow, steppe, wetland, and shrub ecosystems, achieving R² up to 0.95 and RMSE as low as 0.42 g C m⁻² d⁻¹. By validating extracted site-level GPP values from the upscaling GPP datasets against with flux observations, AutoML-GPP demonstrates overall superior or equivalent performance over global GPP products (FLUXCOM X-base, GOSIF, and FluxSat). Regional upscaling estimated a mean annual total GPP of 374.20 Tg C yr⁻¹ from 2002 to 2018, with a slight upward trend of 0.08 Tg C yr⁻¹. Spatially, higher GPP occurred mainly in the eastern QTP, with anomalies linked to climate extremes in 2008, 2010, and 2015. AutoML-GPP effectively captures climate-induced interannual anomalies in the QTP’s GPP, coinciding with GOSIF-GPP and FluxSat GPP, and outperforming the recent released well-known global upscaling flux dataset FLUXCOM X-base. This study provides improved GPP estimation for the QTP, offering new insights into carbon cycling and climate–vegetation interactions.