## 4 Discussion  
Previous research, such as [PERSON] _et al._[59], [PERSON] and [PERSON] [60], [PERSON] and [PERSON] [61], [PERSON] _et al._[17] and [PERSON] _et al._[19], has demonstrated the potential of employing satellite remote sensing to monitor phenological events using vegetation indices linked to LAI. As was discussed earlier, two issues arise from the use of satellite data for phenological monitoring, namely the appropriateness of the spatial sampling (the spatial resolution) and whether imagery is available at the frequency required to measure phenological events. In studies, such as that carried out by [PERSON] _et al._[17] and [PERSON] _et al._[19], the remotely sensed data came from a sensor (MODIS) with a daily repeat cycle (high temporal resolution), but a coarse spatial resolution (pixels of the order of 250 m). Such sensors provide the frequency of observation required for phenological monitoring of U.K. forests, but the spatial resolution is not always appropriate, as noted by [PERSON] _et al._[19], [PERSON] _et al._[62] and [PERSON] _et al._[63]. Where the landscape is heterogeneous, such as typically found in natural and semi-natural woodland, results from coarse spatial resolution sensors have been shown to be less reliable in determining phenological events [64]. [PERSON] _et al._[62] demonstrated the potential of using finer spatial resolution imagery, from Landsat, to monitor phenology in woodland areas. In their study, [PERSON] _et al._[62] identified the start and the end of the growing season and were able to exploit a 30-year archive of Landsat imagery to demonstrate how these phenological events had varied over time. The potential limitation with medium spatial resolution sensors, however, is their temporal resolution. As previously noted, in temperate regions, such as the U.K., cloud cover can be a significant problem [33, 34], and this is exacerbated when using medium resolution sensors, which do not pass directly over a site on a daily basis. [PERSON] _et al._[62] note that the 16-day overpass interval of Landsat is a potential limitation with using it for phenological studies. For example, in the time period  
Figure 3: Relationship between measured LAI and LAI estimated from SAVI for the deciduous study plot at Risley Moss. covered by this study, there were only three cloud-free Landsat scenes available for the study area, making the monitoring of phenological changes impossible. The work presented in this paper indicates the potential of using a constellation of medium spatial resolution satellites that have the same or very similar imaging characteristics to Landsat. The advantage of the DMC constellation approach is that it combines the relatively high spatial characteristics (spatial resolution) required for looking at heterogeneous areas of vegetation, such as woodland, with the high frequency of observation (temporal resolution), due to the possibility of using the overpasses of multiple satellites, required for monitoring phenological events. While this research identified nine usable images, for the operational reasons noted earlier, it is estimated, using the work on cloud frequency of [PERSON] _et al._[34], that with a daily overpass capability, the DMC satellites could have collected 76 cloud-free images over the study period if they had been tasked to do so. The results obtained from the research presented here indicate a strong association between DMC-derived vegetation indices and LAI, the latter being a biophysical parameter that has already been shown to be suitable for monitoring phenology. The nature of the association between the DMC-derived vegetation indices and LAI is similar to that identified by [PERSON] _et al._[19], who found that LAI estimates produced from satellite imagery could be used to identify phenological changes in woodlands in southern England over a growing season. In a study looking at alluvial forests, [PERSON] _et al._[63] found a strong relationship between vegetation indices derived from remote sensing and _in situ_ LAI measured on the ground.