Monitoring vegetation structure and functioning is critical for modelling terrestrial ecosystems and energy cycles. Leaf area index (LAI) is an important structural property of vegetation used in many land-surface, climate, and forest monitoring applications. Remote sensing provides a unique way to obtain estimates of leaf area index at spatially extensive areas. However, the analysis and extraction of quantitative information from remotely-sensed data require accurate cross-calibration with in situ forest measurements, which are generally spatially-and temporally-limited, thereby limiting the ability to compare the seasonal dynamic patterns between field and remotely-sensed time series. This is particularly relevant in temperate broadleaved forests, which are characterized by high level of complexity, which can complicate the retrieval of vegetation attributes from remotely-sensed data. In this study, we performed a long-term comparison of MODIS LAI products with continuous in situ leaf area index measurements collected monthly in temperate and Mediterranean forests from 2000 to 2016. Results indicated that LAI showed a good correlation between satellite and ground data for most of the stands, and the pattern in seasonal changes were highly overlapping between the time-series. We conclude that MODIS LAI data are suitable for phenological application and for up-scaling LAI from the stand level to larger scales. © 2019, Italian Society of Remote Sensing. All rights reserved.