Gross primary productivity (GPP) is an important parameter in the carbon cycle and climate change studies. The results of GPP fluxes estimated based on multiple models or remote sensing vary widely, but current studies of GPP in Chinese grasslands tend to ignore data uncertainty. In this study, uncertainty analysis of GPP datasets estimated based on terrestrial ecosystem models and remote sensing was conducted using cross-validation, standard error statistics, and ensemble empirical modal decomposition. We found that 1) the fit coefficients R² of two-by-two cross-validation of GPP datasets mostly exceeded 0.8 at the global scale. 2) GPP from different sources were consistent in portraying the spatial and temporal patterns of GPP in Chinese grasslands. However, due to many differences in model structure, parameterization and driving data, some uncertainties still exist, especially in the parts of dry-cold areas where the standard deviations are relatively large. 3) Uncertainties were higher for future scenarios than for historical periods, and GPP uncertainties were much higher for future high-emissions scenarios than for low- and medium-emissions scenarios. This study highlighted the need for uncertainty analysis when GPP is applied to spatio-temporal analysis, and suggested that when comparing and assessing carbon balance conditions, multiple source data sets should be combined to avoid misleading conclusion due to uncertainty.

The burning of fossil fuels by humans emits large amounts of CO₂ into the atmosphere and strongly affects the Earth’s carbon balance, with grassland ecosystems changing from weak carbon sinks that were previously close to equilibrium to core carbon sinks. Chinese grasslands are located in typical arid–semi-arid and semi-arid climatic regions, and drought events in the soil and atmosphere can have strong and irreversible consequences on the function and structure of Chinese grassland ecosystems. Based on this, we investigated the response of the gross primary production (GPP) of Chinese grasslands to land–atmosphere moisture constraints, using GPP data simulated through four terrestrial ecosystem models and introduced copula functions and Bayesian equations. The main results were as follows: (1) Soil moisture trends were not significant, and changes were dominated by interannual variability. The detrended warm-season SM correlated with GPP at 0.48 and 0.63 for the historical and future periods, respectively; thus, soil moisture is the critical water stress that regulates interannual variability in Chinese grassland GPP. (2) The positive correlation between shallow SM (0–50 cm) and GPP was higher (r = 0.62). Shallow-soil moisture is the main soil layer that constrains GPP, and the soil moisture decrease in shallow layers is much more likely to cause GPP decline in Chinese grasslands than that in deep-soil water. (3) The probability of GPP decline in Chinese grasslands caused by drought in shallow soils of 0–20 and 20–50 cm is 32.49% and 27.64%, respectively, which is much higher than the probability of GPP decline in deeper soils. In particular, soil drought was more detrimental to grassland GPP in Xinjiang and the Loess Plateau. (4) The probability of soil drought causing GPP decline was higher than that of atmospheric drought during the historical period (1.78–8.19%), but the probability of an atmospheric drought-induced GPP deficit increases significantly in the future and becomes a key factor inhibiting GPP accumulation in some regions (e.g., the Loess Plateau). Our study highlighted the response of grassland ecosystems after the occurrence of soil drought, especially for the shallow-soil-water indicator, which provides important theoretical references for grassland drought disaster emergency prevention and policy formulation.

This study aims to analyze the driving factors and threshold responses of the NDVI across different regional scales in Hunan Province, revealing the main influences on vegetation cover and the corresponding threshold effects and providing essential data for precise future afforestation planning. We use NDVI data and its associated driving factors, employing correlation analysis methods to investigate the spatial differentiation and threshold effects of vegetation driving factors at different regional scales. First, various analytical techniques, including Sen’s trend analysis, the Mann–Kendall significance test, and the Hurst index, are applied to assess changes in vegetation cover between 2000 and 2020 and to predict future trends. Second, to explore the differences in vegetation’s driving mechanisms at different regional scales, the optimal parameters-based geographic detector model is employed, which integrates continuous variable discretization methods and selects the optimal parameter set by maximizing explanatory power. This approach is particularly suitable for analyzing nonlinear relationships. Lastly, threshold regression analysis is conducted on the key driving factors identified through the optimal parameters-based geographic detector model. The results show that vegetation cover in most areas of Hunan significantly increased from 2000 to 2020; however, our predictions suggest slight degradation in the future. The optimal parameters-based geographic detector model identified topography and geomorphology as the primary factors affecting the spatial and temporal distribution of the NDVI, with notable regional differences in other factors. The influence of natural factors has weakened over time, while anthropogenic activities increasingly affect vegetation. Moreover, dual-factor influences exhibit stronger explanatory power than single-factor influences. The threshold response analysis reveals that slope is a key factor influencing the NDVI, with a positive threshold relationship observed at both the provincial and subregional scales, although the threshold points vary by subregion. The temperature and NDVI are negatively correlated, with varying threshold points across regions. The abovementioned research findings suggest that future afforestation efforts in Hunan should take into account the distinct characteristics of each subregion. Afforestation strategies should be tailored based on the specific threshold relationships observed in each area to enhance their effectiveness.