There are strong uncertainties regarding LAI dynamics in forest ecosystems in response to climate change.While empirical growth & yield models (G&YMs) provide good estimations of tree growth at the standlevel on a yearly to decennial scale, process-based models (PBMs) use LAI dynamics as a key variable forenabling the accurate prediction of tree growth over short time scales. Bridging the gap between PBMsand G&YMs could improve the prediction of forest growth and, therefore, carbon, water and nutrientfluxes by combining modeling approaches at the stand level.Our study aimed to estimate monthly changes of leaf area in response to climate variations fromsparse measurements of foliage area and biomass. A leaf population probabilistic model (SLCD) wasdesigned to simulate foliage renewal. The leaf population was distributed in monthly cohorts, and thetotal population size was limited depending on forest age and productivity. Foliage dynamics were drivenby a foliation function and the probabilities ruling leaf aging or fall. Their formulation depends on theforest environment.The model was applied to three tree species growing under contrasting climates and soil types. Intropical Brazilian evergreen broadleaf eucalypt plantations, the phenology was described using 8 parameters. A multi-objective evolutionary algorithm method (MOEA) was used to fit the model parameterson litterfall and LAI data over an entire stand rotation. Field measurements from a second eucalypt standwere used to validate the model. Seasonal LAI changes were accurately rendered for both sites (R2= 0.898adjustment, R2= 0.698 validation). Litterfall production was correctly simulated (R2= 0.562, R2= 0.4018validation) and may be improved by using additional validation data in future work. In two French temperate deciduous forests (beech and oak), we adapted phenological sub-modules of the CASTANEA modelto simulate canopy dynamics, and SLCD was validated using LAI measurements. The phenological