Fractional vegetation cover (FVC) is an important biophysical parameter of terrestrial ecosystems. Variation of FVC is a major problem in research fields related to remote sensing applications. In this study, the global FVC from 1982 to 2011 was estimated by GIMMS NDVI data, USGS global land cover characteristics data and HWSD soil type data with a modified dimidiate pixel model, which considered vegetation and soil types and mixed pixels decomposition. The evaluation of the robustness and accuracy of the GIMMS FVC with MODIS FVC and Validation of Land European Remote sensing Instruments (VALERI) FVC show high reliability. Trends of the annual FVC_max and FVC_mean datasets in the last 30 years were reported by the Mann–Kendall method and Sen’s slope estimator. The results indicated that global FVC change was 0.20 and 0.60 in a year with obvious seasonal variability. All of the continents in the world experience a change in the annual FVC_max and FVC_mean, which represents biomass production, except for Oceania, which exhibited a significant increase based on a significance level of p = 0.001 with the Student’s t-test. Global annual maximum and mean FVC growth rates are 0.14%/y and 0.12%/y, respectively. The trends of the annual FVC_max and FVC_mean based on pixels also illustrated that the global vegetation had turned green in the last 30 years. A significant trend on the p = 0.05 level was found for 15.36% of the GIMMS FVC_max pixels on a global scale (excluding permanent snow and ice), in which 1.8% exhibited negative trends and 13.56% exhibited positive trends. The GIMMS FVC_mean similarly produced a total of 16.64% significant pixels with 2.28% with a negative trend and 14.36% with a positive trend. The North Frigid Zone represented the highest annual FVC_max significant increase (p = 0.05) of 25.17%, which may be caused mainly by global warming, Arctic sea-ice loss and an advance in growing seasons. Better FVC predictions at large regional scales, with high temporal resolution (month) and long time series, would advance our ability to understand the characteristics of the global FVC changes in the last 30 years and predict the response of vegetation to global climate change.

Quantifying the spatial pattern of large-scale forest biomass can provide a general picture of the carbon stocks within a region and is of great scientific and political importance. The combination of the advantages of remote sensing data and field survey data can reduce uncertainty as well as demonstrate the spatial distribution of forest biomass. In this study, the seventh national forest inventory statistics (for the period 2004–2008) and the spatially explicit MODIS Land Cover Type product (MCD12C1) were used together to quantitatively estimate the spatially-explicit distribution of forest biomass in China (with a resolution of 0.05°, ~5600 m). Our study demonstrated that the calibrated forest cover proportion maps allow proportionate downscaling of regional forest biomass statistics to forest cover pixels to produce a relatively fine-resolution biomass map. The total stock of forest biomass in China was 11.9 Pg with an average of 76.3 Mg ha⁻¹ during the study period; the high values were located in mountain ranges in northeast, southwest and southeast China and were strongly correlated with forest age and forest density.

Wind farms (WFs) can affect the local climate, and local climate change may influence underlying vegetation. Some studies have shown that WFs affect certain aspects of the regional climate, such as temperature and rainfall. However, there is still no evidence to demonstrate whether WFs can affect local vegetation growth, a significant part of the overall assessment of WF effects. In this research, based on the moderate-resolution imaging spectroradiometer (MODIS) vegetation index, productivity and other remote-sensing data from 2003 to 2014, the effects of WFs in the Bashang area of Northern China on vegetation growth and productivity in the summer (June–August) were analyzed. The results showed that: (1) WFs had a significant inhibiting effect on vegetation growth, as demonstrated by decreases in the leaf area index (LAI), the enhanced vegetation index (EVI), and the normalized difference vegetation index (NDVI) of approximately 14.5%, 14.8%, and 8.9%, respectively, in the 2003–2014 summers. There was also an inhibiting effect of 8.9% on summer gross primary production (GPP) and 4.0% on annual net primary production (NPP) coupled with WFs; and (2) the major impact factors might be the changes in temperature and soil moisture: WFs suppressed soil moisture and enhanced water stress in the study area. This research provides significant observational evidence that WFs can inhibit the growth and productivity of the underlying vegetation.

Forests have been recognized to sequester a substantial amount of carbon (C) from the atmosphere. However, considerable uncertainty remains regarding the magnitude and time course of the C sink. Revealing the intrinsic relationship between forest age and C sink is crucial for reducing uncertainties in prediction of forest C sink potential. In this study, we developed a stepwise data assimilation approach to combine a process‐based Terrestrial ECOsystem Regional model, observations from multiple sources, and stochastic sampling to inversely estimate carbon cycle parameters including carbon sink at different forest ages for evergreen needle‐leaved forests in China. The new approach is effective to estimate age‐dependent parameter of maximal light‐use efficiency (R² = 0.99) and, accordingly, can quantify a relationship between forest age and the vegetation and soil C sinks. The estimated ecosystem C sink increases rapidly with age, peaks at 0.451 kg C m⁻² yr⁻¹ at age 22 years (ranging from 0.421 to 0.465 kg C m⁻² yr⁻¹), and gradually decreases thereafter. The dynamic patterns of C sinks in vegetation and soil are significantly different. C sink in vegetation first increases rapidly with age and then decreases. C sink in soil, however, increases continuously with age; it acts as a C source when the age is less than 20 years, after which it acts as a sink. For the evergreen needle‐leaved forest, the highest C sink efficiency (i.e., C sink per unit net primary productivity) is approximately 60%, with age between 11 and 43 years. Overall, the inverse estimation of carbon cycle parameters can make reasonable estimates of age‐dependent C sequestration in forests.