## 6 Discussion  
### Validity and Use of the Data  
The measurements, and careful data processing, enabled us to portray the vegetation phenology of the studied sites accurately. Though the optical flux measurements contained some noise, the derived NDVI curves were generally smooth during the growing season. The good correspondence between growing-season NDVI from our towers with data from MODIS was encouraging. The network has radiation sensors and data loggers with very low power consumption that can run on backup batteries during power failures. The measurements therefore provide simple and robust estimates of growing season phenology that are useful additions to flux measurements and will greatly improve our understanding of ecosystem processes. The relatively low cost of optical measurements means that they can be extended to cover more ecosystems. Figure 12: Comparison of NDVI, \(fAPAR_{c}\), and \(fAPAR_{g}\) with diurnal GPP and air temperature. Dashed vertical lines show the ground-observed phenology events (Filled circles 1–6): 1. DOY 114 (April 24) half snow, half bare ground, 2. DOY 130 (May 10) end of snow melting, 3. DOY 139 (May 19) birch winter-bud to bud swelling, 4. DOY 148 (May 28) bud swelling to bud growth begins, 5. DOY 168 (June 17) full leaf development, 6. DOY 259 (September 16) all the leaves have become yellowish. Green fields denote vegetation periods (labeled A–F); see the text for explanation. Our computations demonstrated that the spectral reflectance measured with large FOV sensor is close to that of directional measurements with infinitesimal FOV. This is advantageous since a larger footprint area is monitored, and a stronger canopy signal is obtained compared to narrow-FOV measurements. The large proportion of canopy in the FOV means that phenophase transition points and subtle changes during the growing season are easily observed. The measurements are affected by anisotropy, and the radiative-transfer model simulations showed that our NDVI values are likely to be higher than those measured at nadir. However, by pointing the sensor in east-west direction and by selecting observations close to mid-day we avoid variations due to the regions of maximum and minimum reflectance along the sun principal plane (hotspot and dark-spot, respectively). The large-FOV sampling averages out anisotropy variations within the footprint. Further simulations are necessary to investigate this for a larger number of canopy configurations and a variety of measurement situations, e.g., varied sun zenith angles and relative azimuth angles. We were not able to measure with the same sensor FOV at Norunda forest as at the other sites, and this is likely to introduce some problem when directly comparing data across the sites. However, the error is small as we avoid looking in the direction of the principal plane. We noted large dark-current drift in our narrow-band measurements, particularly during wintertime due to weak incoming light. Temperature drift is a serious noise source for narrow-band spectral measurement in locations with large temperature variability, such as high latitude or altitude regions. Under these conditions, laboratory-calibrated sensor offsets may not be useful, and great care is necessary to correct for the drift. Although our use of night-time observations for drift compensation improved the time series, we have not been able to test if the temperature-drift relationship holds for daytime conditions. The measurements have revealed some interesting phenological features, for example, the shoulder period of the NDVI after the snow melting period, when ground vegetation recovers from winter. This is the period when water from snow melt becomes available to the vegetation. The brief persistence of such surface water may produce lower NDVI values, possibly canceling out the NDVI increase from vegetation growth. This may explain the slight decrease in NDVI noted at some sites during this period.