Ongoing climate change directly affects tree physiology and forest productivity but is also likely to affect the global vulnerability of forest ecosystems through an amplification of diseases and parasitism. Tree phenology reflects their adaptive responses to climate change and can therefore be used to infer forest condition. Although phenological trends studies based on remotely-sensed data have been widely investigated at the global scale, still little is known on pheno-phases anomalies at regional scales. Sufficient long-term satellite data time series at medium spatial resolution (≤ 1km) are now available to fill this gap but the derived indicators still need to be validated and/or related to disturbance processes. Over the whole French forests and for the last decade, we compared several biophysical parameters from the latest reprocessed products (e.g. MODIS combined and SPOT VEGETATION CYCLOPES data). Chrono-sequences of vegetation indices were computed to investigate the best vegetation health indexes or relative indicators of perturbations: (i) non-parametric modelisation of the forest foliar dynamic was performed using the TIMESAT software to explore inter-annual anomalies (ii) long-term trends and seasonal changes were distinguished by means of the BFAST algorithm. Results were validated and interpreted thanks to 10 years of phenological and sanitary observations provided by the French Permanent Plot Networks for Forest Monitoring (Level I and RENECOFOR). Spatial patterns of tree species phenophases and their anomalies can be depicted. Both latitudinal and altitudinal temperature gradients can be retrieved as well as discrepancies between tree species. Moreover, spatially explicit analysis reveal consistency between satellite-based perturbation index and large scale abiotic (i.e. drought) and biotic (i.e. bark beetles, defoliators) damages inventories. Based on those results, we review the abilities of long-term data series for near real time forest health monitoring and the perspectives of applications in the frame of future satellite missions combining high temporal and spatial resolution.