## 4 Discussion  
### Evaluation and validation of GPP model  
From an end-user perspective, a statement on the quality of the product provided through a WMS is as important as the product itself. Therefore, documentation of product quality is an essential step to improve the usability of integrated products derived using sensor based data sources. Estimates of GPP are in general validated using eddy flux tower data which provides flux measurements on carbon and water exchange from the land surface to the atmosphere within the footprint of the tower [19]. Within the scope of this study these data were not available yet. As an alternative we have compared daily GPP as derived from the automated processing facility with GPP estimates from the 8-day MODIS GPP product (MOD17A2) as provided through the USGS Land Process DAAC data pool (Figure 6). Figure 6: Comparison of 8-day MODIS GPP product (MOD17A2) with daily GPP estimation from this study for three biome types at six locations located in the Veluwe nature reserve in the centre of the Netherlands (see inset). A comparison was made for the 12 monthly dates as presented in Figure 3.  
For the comparison, 12 MODIS GPP products corresponding to the monthly dates of the GPP products as presented in Figure 3 were downloaded. For the biome types deciduous needle forest, shrubland and grassland, we selected two locations for each biome in the central part of the Netherlands with a relatively large homogenous surface area (Figure 6). For the six selected locations, the estimated GPP derived from the MODIS product was compared to the estimated GPP as derived within the present study (Figure 6). In general the GPP values show a good agreement with an R\({}^{2}\) of 0.76 over all biome types, excluding estimates from the data in April for which relatively low values were obtained for the daily-based GPP from this study. The latter can be related to relatively cloudy conditions at time of overflight of the MODIS sensor at that day. Because the MODIS GPP product is derived from data over eight days, probably data from another day in this period will have been used for that calculation. Estimates of GPP for grassland from this study show a small underestimation compared to the MODIS GPP product, while daily GPP values for shrubland are overestimated (Figure 6). This difference can mainly be attributed to the relatively large uncertainty in the determination of LUE [19]. Earlier studies have indicated the use of alternative approaches to derive LUE for example using the photochemical reflectance index (PRI) [5], however this methodology is not yet operational from spaceborne platforms. Annual GPP estimates derived from this study and the MODIS GPP product were compared to annual GPP figures for the Netherlands available from literature (Table 2). Only a limited number of studies on GPP development for the Netherlands were available [34, 35]. Based on this first limited assessment (Table 2), it was observed that annual GPP values for deciduous needle forest from this study agreed well with measured annual GPP (Table 2). Annual GPP estimates for grassland from both this study and from the MODIS product fall outside the measured range of eight Dutch grassland sites [35]. As already indicated uncertainty in determination of LUE plays an important role in this and further study is therefore required to improve the model results for this biome. Complete modeling of the carbon balance of the biomes can be achieved by taking into account the processes of autotrophic and heterotrophic respiration and carbon removal due to harvest or other disturbances [19]. However, for this study we used a relatively simple productivity model, as the focus was on the development of a complete chain approach from individual sensors to the final end user.