This study attempts to quantify the relative contributions of vegetation greening in China due to climatic and human influences from multiple observational datasets. Satellite measured vegetation greenness, Normalized Difference Vegetation Index (NDVI), and relevant climate, land cover, and socioeconomic data since 1982 are analyzed using a multiple linear regression (MLR) method. A statistically significant positive trend of average growing-season (April–October) NDVI is found over more than 34% of the vegetated areas, mainly in North China, while significant decreases in NDVI are only seen in less than 5% of the areas. The relationships between vegetation and climate (temperature, precipitation, and radiation) vary by geographical location and vegetation type. We estimate the NDVI changes in association with the non-climatic effects by removing the climatic effects from the original NDVI time series using the MLR analysis. Our results indicate that land use change is the dominant factor driving the long-term changes in vegetation greenness. The significant greening in North China is due to the increase in crops, grasslands, and forests. The socioeconomic datasets provide consistent and supportive results for the non-climatic effects at the provincial level that afforestation and reduced fire events generally have a major contribution. This study provides a basis for quantifying the non-climatic effects due to possible human influences on the vegetation greening in China.

During 1961–2012, the regional average annual potential evapotranspiration (PET) of Southwest China (SWC) and the four subregions (named as SR1, SR2, SR3, and SR4) showed different decreases (excluding SR3); while the breakpoint analysis suggested that PET changes (i.e., sign and magnitude) have shifted. Based on a group of sensitivity experiments with Penman‐Monteith equation and a new separating method, the contributions of each climate factor alone (i.e., net radiation, Rn; mean temperature, Tave; wind speed, Wnd; and vapor pressure deficit, Vpd) to PET changes were calculated. Results showed that declined Wnd in SR1, reduced Rn in SR2, SR4, and SWC, and increased Vpd in SR3 were responsible for the PET changes during 1961–2012. However, the determinant factor for each subregion and SWC varied in different segmented periods, which were identified using the breakpoint analysis. The impacts of PET shifts on SWC dryness/wetness (reflected by the 3 month Standardized Precipitation‐Evapotranspiration index, SPEI‐3) during 1961–2012 were then quantified. Briefly, SPEI‐3 changes in SR3, SR4, and SWC had the determinant factor of PET in the first one or two period(s), and precipitation in the last period; while they were attributed to PET (precipitation) in SR1 (SR2) for each segmented period. It is found that PET and precipitation had comparable contributions to the variations in SWC dryness/wetness. Our findings have suggested that more attentions should be paid to the impacts of PET changes and shifts in future studies of dryness/wetness or drought.

As an essential variable in linking water, carbon, and energy cycles, evapotranspiration (ET) is difficult to measure. Remote sensing, reanalysis, and land surface model-based ET products offer comprehensive alternatives at different spatio-temporal intervals, but their performance varies. In this study, we selected four popular ET global products: The Global Land Evaporation Amsterdam Model version 3.0a (GLEAM3.0a), the Modern Era Retrospective-Analysis for Research and Applications-Land (MERRA-Land) project, the Global Land Data Assimilation System version 2.0 with the Noah model (GLDAS2.0-Noah) and the EartH2Observe ensemble (EartH2Observe-En). Then, we comprehensively evaluated the performance of these products over China using a stratification method, six validation criteria, and high-quality eddy covariance (EC) measurements at 12 sites. The aim of this research was to provide important quantitative information to improve and apply the ET models and to inform choices about the appropriate ET product for specific applications. Results showed that, within one stratification, the performance of each ET product based on a certain criterion differed among classifications of this stratification. Furthermore, the optimal ET (OET) among these products was identified by comparing the magnitudes of each criterion. Results suggested that, given a criterion (a stratification classification), the OETs varied among stratification classifications (the selected six criteria). In short, no product consistently performed best, according to the selected validation criterion. Thus, multi-source ET datasets should be employed in future studies to enhance confidence in ET-related conclusions.