## 1 Introduction  
Vegetation phenology is the temporal pattern of seasonal vegetation development, particularly leaves, and its subsequent senescence, as determined by variations in climate, day length and characteristics of the vegetation, such as species, and the environment, such as substrate [1; 2; 3]. Monitoring vegetation phenology over time is essential in understanding ecosystem, biological, biophysical, biogeochemical and energy processes and fluxes. Vegetation responds in cyclical ways to these processes, and therefore, variations in vegetation phenology can be linked to environmental change across a wide range of spatial and temporal scales [3; 4; 5; 6]. Vegetation phenology is acknowledged as an essential source of information for understanding the impacts of climate change, due to the relationship between seasonal phenological changes and climatic factors, such as temperature, rainfall and snowfall [7; 8; 9]. However, in order to make use of vegetation phenology in examining the possible impacts of changes in the Earth's climate, phenological measurements are required at a range of spatial scales, namely local, regional and global. Even though there are existing phenological records derived from traditional methods of _in situ_ observations and process-based models, these phenological statistics have limited spatial coverage, due to very sparse networks, lack adequate baselines and uniformity in terminology and methodology, and present limited opportunities for the inter-comparison of observations across regions [2; 10; 11; 12; 13; 14; 15; 16]. Therefore, there is a need for alternative methods to monitor vegetation phenology that will facilitate synoptic observations [17]. One approach that achieves this is the coupling of ground-based measurements of leaf area index (LAI) with spectral reflectance information, often expressed as vegetation indices (VI), derived from multi- and high-temporal satellite remote sensing imagery [6; 18]. The leaf component of a vegetation canopy is a biophysical property that can be measured using the structural attribute, leaf area index (LAI) [19]. Variation in canopy LAI (leaf area per unit ground area) over time is related to the rate of expansion of leaf material, and as such, it is a good indicator of canopy phenology [5; 19; 20; 21; 22]. There are a number of direct and indirect methods to measure LAI. Direct measurement methods include full or partial harvesting of stands, the application of allometric equations and the use of leaf litter-fall collection. Indirect measurement methods include the use of instruments that estimate light transmittance through the canopy as a measure of LAI [23; 24]. However, the limitations of all of these methods are that they only sample discrete locations or, at the very best, small areas, and they are usually limited in terms of temporal repeatability [25]. Remote sensing offers a potential approach to address these limitations, by providing spatial coverage and the repeatability of observation [26]. Relationships between LAI measurements on the ground and remote sensing estimates of vegetation phenology have been widely established [3; 26; 27; 28]. While satellite remotely sensed data collected at various temporal scales can provide the fundamental basis for monitoring changes in phenology [29; 30; 31; 32], there are some potential limitations. Principal amongst these is the impact of cloud cover, which can obscure the Earth's surface in imagery collected by passive optically-based sensors [18] and is a particular problem in temperate latitudes. A study of the availability of cloud-free Landsat imagery across Europe, over a 15-year period (1972-1986), found that, on average, only 50% of the Great Britain land mass would be visible in any image [33].