## 1 Introduction
Recently, networks in the support of spectral data sampling have been developed, notably SpecNet ([[http://specnet.info](http://specnet.info)]([http://specnet.info](http://specnet.info))), and EUROSPEC, a recent European network established as a COST activity ([[http://cost-es0903.fem-environment.eu/](http://cost-es0903.fem-environment.eu/)]([http://cost-es0903.fem-environment.eu/](http://cost-es0903.fem-environment.eu/)); see a recent review by [PERSON] _et al._[28]). While spectral measurements have been undertaken in various disciplines like biology, agriculture, and geology for decades, an important aim of these networks is to promote continuous spectral measurements in time across a variety of ecosystems. By linking spectral data with data from flux towers the networks aim at improving ecosystem monitoring, and improve understanding and upscaling efforts using satellite earth observation [29]. Sampling can be carried out in a variety of spectral, spatial and temporal resolutions, depending on the aim of the analysis. Hyperspectral sampling, with hundreds of spectral channels across the reflected optical spectrum, is useful for investigating detailed biogeochemical mechanisms, e.g., the photosynthetic process, or for formulating optimal vegetation indices. Multispectral or panchromatic measurements cannot achieve these aims but may be sufficient for validating satellite-sensed measurements of phenology or vegetation productivity. Measurements of photosynthetically active radiation (PAR) can be useful for determining vegetation phenology, and for estimating GPP (for a review, see [PERSON] _et al._[30]). The aim of the paper is to demonstrate a spectral sampling design, tested at five flux tower sites, consisting of various sensors that are relatively inexpensive, easy to maintain, and that can provide continuous year-round data. We present the theory of measurements to show that reflectance measured from our viewing geometry with large field-of-view (FOV) is close to that from infinitesimal FOV. We also show the computation of different PAR fractions, considering multiple reflections between canopy and ground. We present data from the first year of measurement (2010) to demonstrate the feasibility of the measurements for vegetation phenology and calibration, and discuss some advantages and possible errors with the type of data collection. Our analysis is carried out in relation to our effortsin calibrating satellite observations to be used for vegetation productivity and phenology. The network reported in this article represents a contribution to existing global and regional flux and spectral networks. **2. Infrastructure of the Spectral Network**  
The network consists of five sites, located in Sweden and Finland, which range latitudes from 56\({}^{\circ}\)N through 69\({}^{\circ}\)N (Figure 1). Their location was determined by the access to existing flux towers. Optical signals were sampled continuously throughout 2010, except for some short periods of power failure. Table 1 provides details about the measurement sites. At two sites (the coniferous forests) we utilize existing high flux towers for our sensors, and at the remaining sites we have erected separate mats for the measurements (for an example, see Figure 2). \begin{table}
\begin{tabular}{l l l l l l} \hline  
* The JYP-1000 sensor from SDEC France ([[http://www.sdec-france.com/](http://www.sdec-france.com/)]([http://www.sdec-france.com/](http://www.sdec-france.com/))) is used for PAR measurement, see further details in Section 2.2.  
\end{table} 
Table 1: Sample site information (location in decimal degrees).