Most near-infrared spectroscopy (NIRS) studies for wood density use multiple spectral acquisitions taken manually at several points from pith to bark. Recently the use of near-infrared hyperspectral imaging (NIR- HSI) has shown good performance in predictions of wood properties allowing build high spatial resolution predictive images. The combined use of NIR-HSI and X-ray microdensity imaging (X-ray MDI) could be more practical for evaluation of spatial variation of wood density. The aim was to develop a wood density calibration model for NIR-HSI (625 µm pixel size) from X-ray MDI (30 µm pixel size) and evaluate the spatial variation of wood density along stem cross-section in Eucalyptus grandis trees. Wood discs were collected from 18 trees of 6 years old, submitted to two different water availability, located in Sao Paulo. Brazil. The challenge here was to match the pixels of X-ray MDI to pixels of NIR-HSI (in 2 mm width radial region) to transfer accurate values of wood density to each, pixel of NIR-HSI. The R2 of the model was 0. 72, while the root mean squared error was and 4.8 x 10-2 g cm-3 for the validation group. The prediction model allows comparing their spatial distributions according to the growth conditions. Trees with higher water stress showed higher wood density. Treatments showed a similar spatial variation or wood density. increasing from pith to bark. In perspective, these data will allow evaluating the spatial distribution of wood density inter and intra-tree rings in relation to meteorological variability.