The measurement of biomass can be obtainedfrom remote sensing analysis and modelling , the impacts of which are related to oil palm cultivation in industrial plantations. Our study aims at producing a spatial model for oil palm biomass estimation, at different scales of spatial analysis. The study was carried out in the industrial plantations of the Cameroonian Society of Palm Groves (SOCAPALM). The developed methodology combined: (i) the mapping of palm groves (Kumar, 2015), (ii) the characterisationof palm groves (Gadal, 2013), (iii) biomass estimation, and (iv) the comparison of the obtained results with Spot6, Landsat 7 ETM+ and Landsat 8 OLI images from 2001 to 2015. The first results were obtained for the mapping of the SOCAPALM industrial palm groves between 2001 and 2015. The obtained maps were highly correlated (Kappa of 0.91 for Spot 6, 0.92 for Landsat 7 and 0.82 for Lansat8), however, because of the presence of mixed pixels, some confusion between oil palm and other classes were observed. One of the factors affecting biomass estimation is spatial accuracy. Several improvements have been suggested : (1) mapping palm groves at a subpixel scale using super-resolution mapping; (2) developing a classification system of cartographic products. The use of satellites images with different spatial resolutions may also help to generate new data taking into account the level of spatial analysis.