A fast, reliable, and objective estimation of oil-palm leaf area index, or LAI, which is directly related with canopy response to global environmental change, could help the management of large industrial estates towards precision farming in several ways. Besides field LAI measurements, that can reveal very long and complicated, remote sensing can provide a means to extract this information exhaustively at a large scale in a limited time, as long as a robust model had been calibrated. The present work analyses two scales: a single oil-palm tree on one hand, and a block of palm trees on the other hand. It tests a protocol adapted to palm plantation structure to seek correlations between the radiometric information derived from a satellite image acquired at very high spatial resolution (0.7m per pixel) by Quickbird sensor, and field measurements performed in the fields with a LICOR LAI-2000 Plant Canopy Analyser. Finally, we derived linear models to predict LAI at the two scales: for the whole block and for an individual tree, obtained respectively with 76% and 58% of correlation, and a respective precision of LAI restitution of 0.5 and 0.9. These results are then discussed in terms of operability and usefulness, and some possible improvements are proposed, as well as future perspective given by remote sensing opportunities.