## 1 Introduction
The difficulties in observing these subtle changes from space should not be underestimated given the normal variations in snow cover length, cloudiness, understorey vegetation, and other factors, that interfere with the remotely sensed signal during the critical periods around the beginning and end of the growing season. Nevertheless, several researchers have mapped variations in surface phenology from space data [7,16-22]. The changes observed in some of these studies appear to be larger than those observed in ground phenological datasets. Validation of phenology data can be done against a variety of reference data, including phenological data records, climate data, or physiologically modeled phenology. However, these approaches are not free from drawbacks. Phenological observations by ground observers may be of limited use since, for practical reasons, normally only a few individuals or very small areas are observed. The spatial representation of these data is therefore often uncertain, and may not be representative of the synoptic changes monitored from space [23]. Climate data generally represent larger areas, but do not represent local climatic conditions, and will not provide any information on other abiotic or biotic causes of variations in phenology. The same problems affect phenological models driven by climate data, e.g., degree day calculations, which are not complete without considering other effects [24]. Physiological processes and reasons behind the vegetation phenophase transitions are not yet fully understood, thus limiting process-based phenology modeling. The extent to which trends observed in Earth observation data can be attributed to real changes in vegetation phenology is not fully known, and probably varies between different biomes. In sub-arctic deciduous forests, estimates of start of season based on the AVHRR and MODIS data appear to be well correlated with field observations [20], however, in Nordic coniferous forests, estimates of growing season parameters based on Terra/MODIS vegetation indices have not shown to be accurate [25]. Coniferous forests are likely more difficult to model than deciduous, and no consistent trend in seasonality was found for American coniferous forests, as opposed to nearby tundra regions [26]. Influence of snow melting, and covariation between vegetation phenology and astronomical and meteorological factors (sun elevation, snow seasonality, cloudiness _etc._) confound the analysis, particularly for high latitude areas. Another complicating factor is that differences in data processing methodologies may generate conflicting and inconsistent results [23]. Spatially representative and consistent time series of near-ground optical measurements will enable better understanding of satellite-based phenology data, and enable improvements of processing methodology. Phenology cameras are excellent tools for observing the greenness and leaf status of vegetation stands [27]. Data from these cameras are useful for investigating effects of weather events, and can, depending on how they are mounted, be used for observing different parts of the forest stand, e.g., for separation of overstorey and understorey variations. However, there is a lack of common standards for phenology cameras. Absolute calibration of the recorded data is seldom done, and signal and spectral drifts are problems that are difficult to quantify. Furthermore, practical problems in maintenance, mass data transfer, and image processing all limit the extensive use of phenology cameras. Cameras and radiometric sensors should be viewed as complementary tools for monitoring the phenology of vegetation stands.