Scalable indicators are promising to assess ecosystem services. In a large (660 ha) coffee agroforestry farm,we calibrated the relationship between the Normalized Difference Vegetation Index (NDVI), calculatedon a High Resolution (HR) satellite image and ground-truth LAI, providing a 2-layer (shade trees andcoffee) LAI calibration with LAI 2000 and a new technique based on the cumulative distribution of LAIalong transects. The effective and apparent clumping of coffee leaves were computed (0.76 and 0.89,respectively). We also calibrated the relationship between the derived HR-LAI farm map and NDVI fromthe Moderate Resolution Imaging Spectroradiometer (MODIS) in order to re-construct LAI time-series(2001-2011).Coffee LAI, as derived from MODIS after substracting the contribution of shade tree LAI varied seasonallybetween 2.4 and 4.4 m2m?2, with a maximum by the end of wet season (peak of harvest), steep declineduring the drier-cooler season, minimum after annual coffee pruning, recovery during the next rainyseason and pause during the grain filling period. MODIS also detected significant inter-annual variationsin LAI originating from annual pruning, or plot renovation followed by a progressive LAI recovery duringup to 4 years.We related the coffee-LAI time-series with farm registries to examine the impacts of management onLAI and on selected ecosystem services, namely yield and hydrological services. Nitrogen fertilizationwas adjusted annually by the farmer and appeared as the best yield predictor (R2= 0.53). CombiningN-fertilization with LAI from 6 significant months of the year, the prediction was improved (R2= 0.74),confirming LAI as an important co-predictor of yield. We ended up with a yield prediction model includingalso the percentage of pruned resprouts (R2= 0.79), with potential uses for regional yield mapping orreconstruction of historical yield time-series. (Résumé d'auteur)