The greening of the Earth over the last decades is predominantly indicated by the enhancements of leaf area index (LAI). Quantifying the relative contribution of multiple determinants, especially changes in climate and in land management changes (LMC), remains an arduous challenge. To solve this problem, we develop a simple yet novel data-driven method, called the Paired Land Use Experiment (PLUE), for mesoscale analysis. Using PLUE, we analyze vegetation development of the Sanjiang Plain, a transboundary plain between China and Russia, with roughly homogeneous climate but with distinct land management practices across the border-intensified agricultural development on China side (CNSP) versus largely little-disturbed natural vegetation on Russia side (RUSP). Both CNSP and RUSP LAI show significant trends (p < 0.05), with the annual variability reaching values of 9.8 × 10⁻³ yr⁻¹ and 11.3 × 10⁻³ yr⁻¹, respectively. However, in CNSP, the LAI increase is concentrated in the middle of the year, especially in five 8-day periods from 26 June to 28 July. During this period, the LAI trend ofCNSP is much higher than that of RUSP, at 92.7 × 10⁻³ yr⁻¹ (p < 0.01) and 43.8 × 10⁻³ yr ⁻¹ (p < 0.01), respectively. Meanwhile, LAI decreased in CNSP at the begging and end of the growing season. The results show that different LMC practices lead to notably different seasonal variability in vegetation changes. The PLUE method offers a new potential tool in driver identification of vegetation greenness change based on observations. We argue for the necessity of parameterizing these different LMC in Earth system models.