Rigorous Statistical Testing Reveals Consistent Global Greening but Divergent Regional Trends Across Multiple Remote Sensing Products

The availability of multiple remote sensing-based vegetation index products provides unprecedented power to monitor vegetation dynamics. Accurately quantifying global vegetation trends remains challenging due to inconsistencies among products and the widespread violation of statistical independence caused by spatiotemporal autocorrelation. Here, we identified and statistically tested global patterns of vegetation change trends by comparing multiple representative long-term remote sensing-based vegetation products and using a powerful statistical approach, Partitioned Autoregressive Time Series (PARTS). All remote sensing-based products exhibited strong temporal and spatial autocorrelation. The autoregressive model accounting for temporal autocorrelation detected, on average, 34.4% of pixels with significant trends, markedly lower than linear regression (46.9%) and the Mann–Kendall test (45.2%), which did not account for autocorrelation. To aggregate pixel-level trends to derive overall trends at regional levels, the estimates from linear regression or ANOVA always gave a highly significant test with a p value less than 10−15. This suggests that failing to consider spatial and temporal autocorrelation tends to inflate Type I error rates and give a spurious pattern. Our results demonstrated a significant global greening trend (p < 0.05), with strong consistency across multiple data products. Areas exhibiting high spatial coherence further identified several greening regions, including China, India, Sahel, and so forth. In contrast, trends in the ecosystems of mid to high latitudes exhibited large fluctuations, which were closely related with the differences in the ecological meaning of vegetation indices and the uncertainty of remote-sensing observations in these regions. Our research provides a more robust and spatially coherent characterization of global greening trends, enhancing our understanding of large-scale ecosystem dynamics under environmental change and informing the selection of vegetation indices for ecosystem change assessment.