Description: Various space-based sensors have been designed and corresponding algorithms developed to retrieve Aerosol Optical Depth (AOD), the very basic aerosol optical property, yet considerable disagreement still exists across these different satellite datasets. Surface-based observations aim to provide ground truth for validating satellite data, hence their deployment locations should preferably contain as much spatial information as possible, i.e., high spatial representativeness. Using a novel Ensemble Kalman Filter (EnKF) based approach, we objectively evaluate the spatial representativeness of current AERONET sites. Multi-sensor monthly mean AOD datasets from MODIS, MISR, SeaWiFS, OMI and PARASOL are combined into a 605 member ensemble, and AERONET data are considered as the observations to be assimilated into this ensemble using the EnKF. The assessment is made by comparing the analysis error variance (that has been constrained by ground-based measurements), with the background error variance (based on satellite-data alone). Results show that the total uncertainty is reduced by ~27% on average, and could reach above 50% over certain places. The uncertainty reduction pattern also has distinct seasonal patterns, corresponding to the spatial distribution of seasonally varying aerosol types, such as dust in the spring for Northern Hemisphere and biomass burning in the fall for Southern Hemisphere. Dust and biomass burning sites have the highest spatial representativeness, rural and oceanic sites can also represent moderate spatial information, whereas the representativeness of urban sites is relatively localized. A spatial score ranging from 1 to 3 is assigned to each AERONET site based on the uncertainty reduction, indicating its representativeness level.