## 1 Introduction
A more recent study [34] assessed the probability of cloud-free observation across Great Britain, as determined from the MODIS cloud mask product, and found that in one calendar year(2005), there was an average probability of cloud-free viewing only 21.3% of the time, with a minimum probability of just 12.9% and a maximum of 33.3% of the time. This means that for a sensor like Landsat, which has a medium spatial resolution and a revisit period of 16 days, there would be approximately five cloud-free images a year, or fewer for some areas, of the U.K. The analysis [34] showed large spatial variations in cloud cover, with fewer cloud-free observing opportunities in the north and west of Great Britain in 2005 and more in the south. The potential limitations caused by cloud cover are problematic for applications requiring high observation frequencies, such as phenological measurements [35, 36, 37, 38, 39]. Therefore, ideally, satellite sensors with a high revisit frequency are required, but there is then likely to be a trade-off between this high temporal resolution and the spatial resolution of the sensor [40, 41, 42]. Satellite sensors with high temporal resolution tend to have coarse spatial resolution. These would include sensors, such as the Advanced Very High Resolution Radiometer (AVHRR), the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Medium Resolution Imaging Spectrometer (MERIS), which have been used in numerous phenological studies [2, 3, 16, 17, 18, 19, 43, 44, 45, 46]. These sensors have a daily revisit capability, but the spatial resolutions vary from 250 m to 1.1 km, which may be too coarse to capture the spatial variability in the vegetation cover. Medium spatial resolution sensors, which typically have a spatial resolution of the order of 20 to 30 m, have also been used for phenological studies [35, 47, 48]. These sensors have a suitable spatial resolution, but typically, their revisit period is measured in numbers of days, which leads to phenological events being missed. One approach that has been adopted for sensors with coarser temporal resolutions is to \"fuse\" data sets together from a number of different sensors [18], which has the effect of increasing the temporal resolution of the observations made. However, there are potential issues with the complexity of the inter-calibrations between any sensors used and also the time it is likely to take to generate a product, if the intention is to use the system for the operational monitoring of phenology. To date, there have been no phenological studies that have employed high temporal resolution data (daily repeat) from a medium spatial resolution sensor. The Disaster Monitoring Constellation (DMC) Satellite series, developed by Surrey Satellite Technology Ltd (SSTL), U.K., offers the opportunity to collect the types of time series data sets that are crucial in phenological studies at relatively high spatial resolution. The DMC concept is to provide a daily global imaging capability at medium spatial resolution, using a constellation of near-identical sensors in order to facilitate a rapid response to disaster monitoring and mitigation [49, 50, 51]. The first generation DMC satellites had a spatial resolution of approximately 22 or 32 m (depending on the specific satellite) and the potential to collect image data on a daily revisit basis. A unique feature of the DMC constellation is that satellites, while effectively being the same in design, are owned and operated independently by different countries. The first generation satellites, two of which were owned by the U.K. and Nigeria, employed a nadir-viewing push-broom, three-band, multispectral scanner, with a six-lens linear CCD-array-based imager, imaging a line of 20,000 pixels over a swath width of 600 km at a spatial resolution of 32 m [52].