Vegetation greening significantly affects regional tempeatures by altering surface energy balance through biophysical process. However, quantitatively assessments on those biophysical effects would suffer from large uncertainties using different satellite observations which are still unknown regionally. To explicitly evaluate the efficiency of heterogeneous greening on both land surface temperature (LST) and near-surface air temperature (T2) and clarify those uncertainties from satellite datasets, we adopt a space-for-time approach to estimate temperature sensitivity variations across three ecological restoration regions in China-Southwest China, the Loess Plateau, and Northeast China-due to their substantial leaf area index (LAI) increases driven by national ecological programs from 2003 to 2019. Meanwhile, biophysical impacts extracted from different LAI and land use/cover (LULC) satellite products are compared between growth greening (within-type LAI increase) and conversion greening (transition from openland to forest). Results indicate that forests exhibit the greatest uncertainty due to more complex feedbacks to surface climate. Openland-to-forest conversions produce substantially stronger cooling than greening within the same vegetation type in both LST and T2. Due to their distinct representation of energy exchange, LST sensitivity generally exhibits a 2-4 times stronger response than T2 in both greening processes resulting with the mean sensitivity of growth greening (based on GLASS LAI and MCD12Q1) being -0.32 f 0.03 K & sdot;(LAI)- 1 for LST and - 0.08 f 0.01 K & sdot;(LAI)- 1 for T2. Additionally, temperature sensitivity using GLASS LAI generally estimates larger cooling magnitudes than that using BNUV6 LAI. The findings highlight the importance of conducting multi-dataset comparisons and cross-validation when evaluating climate benefits of ecological restoration.